WO2019067471A2

WO2019067471A2 - Systems and methods for conducting in-situ monitoring in additive manufacture

Info

Publication number: WO2019067471A2
Application number: PCT/US2018/052720
Authority: WO
Inventors: Haresh G. MALKANI; Sergio BUTKEWITSCH CHOZE; Kyle A. CRUM
Original assignee: Arconic Inc.
Priority date: 2017-09-27
Filing date: 2018-09-25
Publication date: 2019-04-04
Also published as: WO2019067471A3

Abstract

Disclosed are various embodiments of systems and methods for conducting, based at least in part on in-situ monitoring data as input, iterative simulations of various part-build simulation models to generate instructions to an Additive Manufacture ("AM") machine. An embodiment of the present disclosure provides a method that at least includes: building, by the AM machine, an AM part based on a digital twin; monitoring, by an in-situ monitoring sensor, the AM build process; to obtain in-situ monitoring sensing data; determining a discrepancy between the AM part being built and one or more predefined criteria defined based on the digital twin; iteratively simulating an AM simulation model, based on the in-situ monitoring sensing data, to identify an AM process adjustment or a lack of the at least one AM process adjustment; and causing to implement the AM process adjustment by the AM machine to remedy the discrepancy or instructing the AM machine to discharge the AM part.

Description

SYSTEMS AND METHODS FOR CONDUCTING IN-SITU MONITORING IN

ADDITIVE MANUFACTURE

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority from U.S. Provisional Patent Application No. 62/564,097 filed September 27, 2017, and entitled "COMPUTER-DRIVEN SYSTEMS AND COMPUTER-IMPLEMENTED METHODS CONFIGURED FOR CONDUCTING IN-SITU MONITORING IN ADDITIVE MANUFACTURE," which is incorporated herein by reference in its entirety for all purposes.

FIELD OF TECHNOLOGY

[0002] The subject matter herein generally relates to additive manufacture ("AM"), and specifically relates to systems and methods for conducting, based at least in part on in-situ monitoring data as input, iterative simulations of various part-build simulation models to generate instructions to AM machine during the AM process.

BACKGROUND OF TECHNOLOGY

[0003] Additive manufacturing may be used to build, via computer control, successive layers of an AM part. Defects in the AM part may occur due to errors in parameters of the AM process.

SUMMARY OF THE INVENTION

[0004] The present disclosure provides systems and methods for conducting, based at least in part on in-situ monitoring data as input, iterative simulations of various part-build simulation models to generate instructions to an Additive Manufacture (AM) machine. An embodiment of the present disclosure provides a method that at least includes the steps of: (A) building, by an AM machine, at least one portion of at least one AM part during an AM build process based at least in part on a digital twin of the at least one AM part; wherein the digital twin comprises at least one of: i) one or more operational parameters of the AM machine for building the at least one AM part, ii) one or more properties of a desired design for the at least AM part, or iii) one or more AM build process parameters; wherein the digital twin is suitable to be used to certify, without a physically inspection of the actual AM part, a compliance of the actual AM part to the desired design; (B) monitoring, by at least one in-situ monitoring sensor, the AM build process of the at least one AM part, wherein the at least one in-situ monitoring sensor is configured to utilize one or more in-situ monitoring techniques to collect and transmit in-situ monitoring sensing data related to the AM build process of the at least one AM part; (C) receiving, by the processor, from the at least one in-situ monitoring sensor, first in-situ monitoring sensing data during the AM build process of the at least one portion of the at least one AM part; (D) determining, by the processor, at least one discrepancy between the at least one portion of the at least one AM part and one or more predefined criteria, by comparing the first in-situ monitoring sensing data to the one or more predefined criteria that has been defined based on the digital twin; (E) iteratively performing the following to (i) remedy the at least one discrepancy or (ii) determine that the at least one portion of the at least one AM part is to be discarded: a) simulating, by the at least one processor, at least one AM simulation model, based on the first in- situ monitoring sensing data, to identify: i) at least one first AM process adjustment or ii) a lack of the at least one first AM process adjustment; wherein the at least one first AM process adjustment is an adjustment to at least one of: 1) the one or more operational parameters of the AM machine, or 2) the one or more AM build process parameters; b) causing, by the at least one processor, to implement the at least one first AM process adjustment by the AM machine in the AM build process of the at least one portion of the at least one AM part; c) receiving, by the at least one processor, from the at least one in-situ monitoring sensor, second in-situ monitoring sensing data related to the AM build process of the at least one portion of the at least one AM part; and d) determining, by the at least one processor, whether i) the at least one discrepancy has been remedied or ii) the at least one discrepancy has not been remedied; and e) when the at least one discrepancy has not been remedied, simulating, by the at least one processor, the at least one AM simulation model, based on the second in-situ monitoring sensing data, to identify: i) at least one second AM process adjustment or ii) a lack of the at least one second AM process adjustment; and (F) instructing, by the at least one processor, to discharge the at least one portion of the at least one AM part when the lack of the at least one first AM process adjustment or the lack of the at least one second AM process adjustment has been determined.

[0005] In some embodiments, the one or more in-situ monitoring techniques are at least one of: i) taking temperature measurements of at least one build plate; ii) measuring beam power; iii) taking visual images, thermal images, or both, by a camera; iv) obtaining acoustic data from the build plate from at least one acoustic emission sensor; v) performing layer-by-layer optical topography; vi) obtaining gas flow data from one of more gal flow sensor; vii) monitoring a molten pool size by the camera, an optical sensor, or both; viii) monitoring a powder distribution by the camera, the acoustic emission sensor; or ix) monitoring a powder contamination by the camera, the optical sensor, or both.

[0006] In some embodiments, the first in-situ monitoring sensing data and the second in-situ monitoring sensing data comprise one or more parameters selected from electrical signals, laser powers, laser angles, raster patterns, layer overlap measurements, and any combination thereof.

[0007] In some embodiments, the compliance of the actual AM part to the desired design is determined based on the in-situ monitoring sensing data related to at least one or more particular points in a build portion of the actual AM part that are determined to deviate from the one or more predefined criteria by more than a predetermined tolerance value.

[0008] In some embodiments, the simulating the at least one AM simulation model, based on the first in-situ monitoring sensing data, is performed in a closed loop.

[0009] In some embodiments, the exemplary method may further include creating, by the at least one processor, based at least in part on the in-situ monitoring sensing data, in an open loop mode, at least one data record that is representative of a condition of the at least one AM part utilizing the in-situ monitoring sensing data.

[00010] In some embodiments, the in-situ monitoring sensing data comprises one or more of orientation, volume filling, temperature, cracking, and other parameters.

[00011] In some embodiments, the exemplary method may further include validating, by the at least one processor, one or more engineering material properties.

[00012] In some embodiments, the exemplary method may further include verifying, by the at least one processor, that the at least one AM part and the corresponding digital twin meet the one or more predefined criteria.

[00013] In some embodiments, the exemplary method may further include utilizing, by the at least one processor, at least one numerical optimization to exhaust a design space for a determination of one or more input conditions for the validating the one or more engineering material properties and the verifying that the at least one AM part and the corresponding digital twin meet the one or more predefined criteria.

[00014] An embodiment of the present disclosure provides a system that includes at least the following components: an Additive Manufacturing (AM) machine, configured to build at least one portion of at least one AM part during an AM build process based at least in part on a digital twin of the at least one AM part; the digital twin of the at least one AM part; wherein the digital twin comprises at least one of: i) one or more operational parameters of the AM machine for building the at least one AM part, ii) one or more properties of a desired design for the at least AM part, or iii) one or more AM build process parameters; wherein the digital twin is suitable to be used to certify, without a physically inspection of the actual AM part, a compliance of the actual AM part to the desired design; at least one in-situ monitoring sensor, configured to utilize one or more in-situ monitoring techniques to collect and transmit in-situ monitoring sensing data related to the AM build process of the at least one AM part; at least one processor; and a non-transitory computer readable storage medium storing thereon program logic, wherein, when executing the program logic, the at least one processor is configured to: (A) receive, from the at least one in-situ monitoring sensor, first in-situ monitoring sensing data during the AM build process of the at least one portion of the at least one AM part; (B) determine at least one discrepancy between the at least one portion of the at least one AM part and one or more predefined criteria, by comparing the first in-situ monitoring sensing data to the one or more predefined criteria that has been defined based on the digital twin; (C) iteratively perform the following to (i) remedy the at least one discrepancy or (ii) determine that the at least one portion of the at least one AM part is to be discarded: a) simulate at least one AM simulation model, based on the first in-situ monitoring sensing data, to identify: i) at least one first AM process adjustment or ii) a lack of the at least one first AM process adjustment; wherein the at least one first AM process adjustment is an adjustment to at least one of: 1) the one or more operational parameters of the AM machine, or 2) the one or more AM build process parameters; b) causing to implement the at least one first AM process adjustment by the AM machine in the AM build process of the at least one portion of the at least one AM part; c) receiving, from the at least one in-situ monitoring sensor, second in-situ monitoring sensing data related to the AM build process of the at least one portion of the at least one AM part; and d) determine whether i) the at least one discrepancy has been remedied or ii) the at least one discrepancy has not been remedied; and e) when the at least one discrepancy has not been remedied, simulate the at least one AM simulation model, based on the second in-situ monitoring sensing data, to identify: i) at least one second AM process adjustment or ii) a lack of the at least one second AM process adjustment; and (D) instruct to discharge the at least one portion of the at least one AM part when the lack of the at least one first AM process adjustment or the lack of the at least one second AM process adjustment has been determined.

BRIEF DESCRIPTION OF THE DRAWINGS

[00015] The present disclosure can be further explained with reference to the attached drawings, wherein like structures are referred to by like numerals throughout the several views. The drawings shown are not necessarily to scale, with emphasis instead generally being placed upon illustrating the principles of the present disclosure. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present disclosure.

[00016] FIG. 1 is a schematic illustration of an overall architecture of that may occur within an exemplary inventive computer-based AM systems and related methods according to one or more embodiments of the present disclosure;

[00017] FIG. 2 is a schematic representation of an exemplary inventive computer-based

AM system according to an embodiment of the present disclosure;

[00018] FIG. 3 is a schematic representation of a block according to an embodiment of the present disclosure; [00019] FIGs. 4A-4E are schematic representations of an exemplary analysis workflow according to an embodiment of the present disclosure;

[00020] FIGs. 5A-5E are schematic representations of an exemplary analysis workflow according to an embodiment of the present disclosure; and

[00021] FIGs. 6A-6E are schematic representations of an exemplary analysis workflow according to an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

[00022] The present disclosure can be further explained with reference to the included drawings, wherein like structures are referred to by like numerals throughout the several views. The drawings shown are not necessarily to scale, with emphasis instead generally being placed upon illustrating the principles of the present disclosure. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present disclosure.

[00023] Among those benefits and improvements that have been disclosed, other objects and advantages of this invention can become apparent from the following description taken in conjunction with the accompanying figures. Detailed embodiments of the present disclosure are disclosed herein; however, it is to be understood that the disclosed embodiments are merely illustrative of the invention that may be embodied in various forms. In addition, each of the examples given in connection with the various embodiments of the present disclosure is intended to be illustrative, and not restrictive.

[00024] Throughout the specification, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrases "in one embodiment" and "in some embodiments" as used herein do not necessarily refer to the same embodiment(s), though they may. Furthermore, the phrases "in another embodiment" and "in some other embodiments" as used herein do not necessarily refer to a different embodiment, although they may. Thus, as described below, various embodiments of the invention may be readily combined, without departing from the scope or spirit of the invention. Further, when a particular feature, structure, or characteristic is described in connection with an implementation, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other implementations whether or not explicitly described herein.

[00025] The term "based on" is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of "a," "an," and "the" include plural references. The meaning of "in" includes "in" and "on."

[00026] It is understood that at least one aspect/functionality of various embodiments described herein can be performed in real-time, faster-than-real-time, and/or dynamically. As used herein, the term "real-time" is directed to an event/action that can occur instantaneously or almost instantaneously in time when another event/action has occurred. For example, the "realtime processing," "real-time computation," and "real-time execution" all pertain to the performance of a computation prior to an actual time that the related physical process or physical transformation occurs (e.g., adding a build layer to an AM part), so that results of the real-time computation (e.g., a simulated dynamics model of the AM part being built) can be used in guiding the physical process (e.g., AM process). As used herein, the term "faster-than-real-time" is directed to simulations in which advancement of simulation time may occur faster than real world time. For example, some of the "faster-than-real-time" simulations of the present disclosure may be configured in accordance with one or more principles detailed in D. Anagnostopoulos, 2002, "Experiment scheduling in faster-than-real-time simulation," 148-156. 10.1109/PADS.2002.1004212.

[00027] As used herein, the term "dynamically" means that events and/or actions can be triggered and/or occur without any human intervention. In some embodiments, events and/or actions in accordance with the present disclosure can be in real-time and/or based on a predetermined periodicity of at least one of: nanosecond, several nanoseconds, millisecond, several milliseconds, second, several seconds, minute, several minutes, hourly, several hours, daily, several days, weekly, monthly, etc.

[00028] As used herein, the term "runtime" corresponds to any behavior that is dynamically determined during an execution of a software application or at least a portion of software application.

Additive Manufacturing

[00029] As used herein, "additive manufacturing" means "a process of joining materials to make objects from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing methodologies", as defined in ASTM F2792-12a entitled "Standard Terminology for Additively Manufacturing Technologies". The AM parts described herein may be manufactured via any appropriate additive manufacturing technique described in this ASTM standard, such as binder jetting, directed energy deposition, material extrusion, material jetting, powder bed fusion, or sheet lamination, among others. In one embodiment, an additive manufacturing process includes depositing successive layers of one or more materials (e.g., powders of materials) and then selectively melting and/or sintering the materials to create, layer- by-layer, an AM part/product. In one embodiment, an additive manufacturing processes uses one or more of Selective Laser Sintering (SLS), Selective Laser Melting (SLM), and Electron Beam Melting (EBM), among others. In one embodiment, an additive manufacturing process uses an EOSINT M 280 Direct Metal Laser Sintering (DMLS) additive manufacturing system, or comparable system, available from EOS GmbH (Robert-Stirling-Ring 1, 82152 Krailling/Munich, Germany). Additive manufacturing techniques (e.g. when utilizing metallic feedstocks) may facilitate the selective heating of materials above the liquidus temperature of the particular alloy, thereby forming a molten pool followed by rapid solidification of the molten pool. Non-limiting examples of additive manufacturing processes useful in producing AM products include, for instance, DMLS (direct metal laser sintering), SLM (selective laser melting), SLS (selective laser sintering), and EBM (electron beam melting), among others. Any suitable feedstocks may be used, including one or more materials, one or more wires, and combinations thereof. In various embodiments, AM is configurable to utilize various feedstocks - e.g. metallic feedstocks (e.g. with additives to promote various properties, e.g. grain refiners and/or ceramic materials), plastic feedstocks, and polymeric feedstocks (or reagent-based feedstock materials which form polymeric AM builds/AM parts), to name a few. In some embodiments the additive manufacturing feedstock is comprised of one or more materials. Shavings are types of particles. In some embodiments, the additive manufacturing feedstock is comprised of one or more wires. A ribbon is a type of wire.

[00030] In one approach, the AM parts metal alloys described herein are in the form of an additive manufacturing feedstock.

[00031] As noted above, additive manufacturing may be used to create, layer-by-layer, an AM part/product. In one embodiment, a powder bed is used to create an AM part/product (e.g., a tailored alloy product and/or a unique structure unachievable through traditional manufacturing techniques (e.g. without excessive post-processing machining)).

[00032] In one approach, a method comprises (a) dispersing an AM feedstock (e.g. metal alloy powder in a bed), (b) selectively heating a portion of the material (e.g., via an energy source or laser) to a temperature above the liquidus temperature of the particular AM part/product to be formed, (c) forming a molten pool and (d) cooling the molten pool at a cooling rate of at least 1000 °C per second. In one embodiment, the cooling rate is at least 10,000 °C per second. In another embodiment, the cooling rate is at least 100,000 °C per second. In another embodiment, the cooling rate is at least 1,000,000 °C per second. Steps (a)-(d) may be repeated as necessary until the AM part/product is completed.

[00033] In another approach, a method comprises (a) dispersing a feedstock (e.g. AM material powder) in a bed, (b) selectively binder jetting the AM material powder, and (c) repeating steps (a)-(b), thereby producing a final additively manufactured product (e.g. including optionally heating to burn off binder and form a green form, followed by sintering to form the AM part).

[00034] In another approach, electron beam (EB) or plasma arc techniques are utilized to produce at least a portion of the AM part/product. Electron beam techniques may facilitate production of larger parts than readily produced via laser additive manufacturing techniques. An illustrative example provides feeding a to the wire feeder portion of an electron beam gun. The wire may comprise a metal feedstock (e.g. metal alloy including titanium, cobalt, iron, nickel, aluminum, or chromium alloys to name a few). The electron beam heats the wire or tube, as the case may be, above the liquidus point of the alloy to be formed, followed by rapid solidification of the molten pool to form the deposited material. [00035] The alloy may be, for instance, an aluminum-based alloy, a titanium-based alloy (including titanium aluminides), a nickel-based alloy, an iron-based alloy (including steels), a cobalt-based alloy, or a chromium-based alloy, among others.

[00036] Any suitable alloy composition may be used with the techniques described above to produce AM part/product. Some non-limiting examples of alloys that may be utilized are described below. However, other alloys may also be used, including copper-based, zinc-based, silver-based, magnesium-based, tin-based, gold-based, platinum-based, molybdenum-based, tungsten-based, and zirconium-based alloys, among others.

[00037] As used herein, "aluminum alloy" means a metal alloy having aluminum as the predominant alloying element. Similar definitions apply to the other corresponding alloys referenced herein (e.g. titanium alloy means a titanium alloy having titanium as the predominant alloying element, and so on).

[00038] In some embodiments, the inventive specially programmed computing systems with associated devices are configured to operate in the distributed network environment, communicating over a suitable data communication network (e.g., the Internet, etc.) and utilizing at least one suitable data communication protocol (e.g., IPX/SPX, X.25, AX.25, AppleTalk(TM), TCP/IP (e.g., HTTP), etc.). Of note, the embodiments described herein may, of course, be implemented using any appropriate hardware and/or computing software languages. In this regard, those of ordinary skill in the art are well versed in the type of computer hardware that may be used, the type of computer programming techniques that may be used (e.g., object- oriented programming), and the type of computer programming languages that may be used (e.g., C++, Objective-C, Swift, Java, Javascript). The aforementioned examples are, of course, illustrative and not restrictive. [00039] The material disclosed herein may be implemented in software or firmware or a combination of them or as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. As used herein, the machine-readable medium may include any medium and/or mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). By way of example, and not limitation, the machine-readable medium may comprise computer readable storage media, for tangible or fixed storage of data, or communication media for transient interpretation of code-containing signals. Machine-readable storage media, as used herein, refers to physical or tangible storage (as opposed to signals) and includes without limitation volatile and non-volatile, removable and nonremovable media implemented in any method or technology for the tangible storage of information such as computer-readable instructions, data structures, program modules or other data. Machine-readable storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, flash memory storage, or any other physical or material medium which can be used to tangibly store the desired information or data or instructions, including but not limited to electrical, optical, acoustical or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), and which can be accessed by a computer or processor.

[00040] In another form, a non-transitory article, such as non-volatile and non-removable computer readable media, may be used with any of the examples mentioned above or other examples except that it does not include a transitory signal per se. It does include those elements other than a signal per se that may hold data temporarily in a "transitory" fashion such as RAM and so forth. In some embodiments, the present disclosure may rely on one or more distributed and/or centralized databases (e.g., data center).

[00041] As used herein, the term "server" should be understood to refer to a service point which provides processing, database, and communication facilities. By way of example, and not limitation, the term "server" can refer to a single, physical processor with associated communications and data storage and database facilities, or it can refer to a networked or clustered complex of processors and associated network and storage devices, as well as operating software and one or more database systems and application software that support the services provided by the server. Servers may vary widely in configuration or capabilities, but generally a server may include one or more central processing units and memory. A server may also include one or more mass storage devices, one or more power supplies, one or more wired or wireless network interfaces, one or more input/output interfaces, or one or more operating systems, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, or the like.

[00042] As used herein, a "network" should be understood to refer to a network that may couple devices so that communications may be exchanged, such as between a server and a client device or other types of devices, including between wireless devices coupled via a wireless network, for example. A network may also include mass storage, such as network attached storage (NAS), a storage area network (SAN), or other forms of computer or machine-readable media, for example. A network may include the Internet, one or more local area networks (LANs), one or more wide area networks (WANs), wire□ line type connections, wireless type connections, cellular or any combination thereof. Likewise, sub□ networks, which may employ differing architectures or may be compliant or compatible with differing protocols, may interoperate within a larger network. Various types of devices may, for example, be made available to provide an interoperable capability for differing architectures or protocols. As one illustrative example, a router may provide a link between otherwise separate and independent LANs.

[00043] As used herein, the terms "computer engine" and "engine" identify at least one software component and/or a combination of at least one software component and at least one hardware component which are designed/programmed/configured to manage/control other software and/or hardware components (such as the libraries, software development kits (SDKs), objects, etc.).

[00044] Examples of hardware elements may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth. In some embodiments, the one or more processors may be implemented as a Complex Instruction Set Computer (CISC) or Reduced Instruction Set Computer (RISC) processors; x86 instruction set compatible processors, multi-core, or any other microprocessor or central processing unit (CPU). In various implementations, the one or more processors may be dual-core processor(s), dual-core mobile processor(s), and so forth.

[00045] Software may refer to 1) libraries; and/or 2) software that runs over the internet or whose execution occurs within any type of network. Examples of software may include, but are not limited to, software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computing code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware elements and/or software elements may vary in accordance with any number of factors, such as desired computational rate, power levels, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints.

[00046] One or more aspects of at least one embodiment may be implemented by representative instructions stored on a machine-readable medium which represents various logic within the processor, which when read by a machine causes the machine to fabricate logic to perform the techniques described herein. Such representations, known as "IP cores" may be stored on a tangible, machine readable medium and supplied to various customers or manufacturing facilities to load into the fabrication machines that actually make the logic or processor.

[00047] In some embodiments, the exemplary inventive AM processes of the present disclosure may include one or more steps detailed, without limitation, in U.S. Patent Pub. No. 2016/0224017 which is hereby incorporated herein by reference. For example, the AM process may be a process of joining materials to make objects from 3D model data, usually layer upon layer. In some embodiments, additive manufacturing includes building successive layers of an AM material (e.g., aluminium alloy powder) by depositing a feed stock powder of the AM material (e.g., metal powder) and then selectively melted and/or sintered (e.g. with a laser or other heat source) to create, layer-by-layer, an AM part (e.g., an aluminium alloy product, a titanium alloy product, a nickel alloy product). Additive build processes utilizing a powder feedstock that can employ one or more of the embodiments of the instant disclosure include: direct metal laser sintering (e.g. a powder bed fusion process used to make metal AM parts directly from metal powders without intermediate "green" or "brown" parts); directed energy deposition (e.g., an AM process in which focused thermal energy is used to fuse materials by melting as they are being deposited); powder bed fusion (e.g. an AM process in which thermal energy selectively fuses regions of a powder bed); or laser sintering (e.g., a powder bed fusion process used to produce objects from powdered materials using one or more lasers to selective fuse or melt the particles at the surface, layer by layer, in an enclosed chamber) to name a few. Some non-limiting examples of suitable additive manufacturing systems include the EOSINT M 280 Direct Metal Laser Sintering (DMLS) additive manufacturing system, available from EOS GmbH (Robert-Stirling-Ring 1, 82152 Krailling/Munich, Germany). Other suitable additive manufacturing systems include Selective Laser Sintering (SLS) systems, Selective Laser Melting (SLM) systems, and Electron Beam Melting (EBM) systems, among others.

[00048] Fig. 1 shows an illustrative example of an overall architecture 100 of various activities that may occur within an exemplary inventive computer-based AM system 102 that may be configured to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. While some activities identified in Fig. 1 are detailed herein as occurring in sequential order, such description is done for purposes of convenience and should not be viewed as being limited since, as a skilled practitioner would readily recognize, at least some activities may occur concurrently, in reverse order, or not occur at al under certain condition(s).

[00049] Referring to item 104 of Fig. 1, in at least some embodiments, the exemplary inventive computer-based AM system may receive/obtain electronical data describing one or more parts to be manufactured ("part data"). In some embodiments, the exemplary inventive computer-based AM system may analyze the part data to determine one or more functions that are desired for each AM part. In some embodiments, when an AM part may be constructed from a plurality of sub-parts and/or when the AM part may be intended to be combined with at least one other part, which may or may not be manufactured utilizing an AM process, to perform its intended function, the exemplary inventive computer-based AM system may further determine one or more characteristics that may influence how the AM part would perform for its intended purpose(s).

[00050] In some embodiments, any individual part manufactured via AM may be subject to one or more additional processes, such as machining for finishing purposes and/or forging for inducing desired microstructural properties. In some embodiments, at least one sub-part may not be manufactured via AM. In some embodiments, the exemplary inventive computer-based AM system may be configured to perform such analysis/determination as part of preparation for generating software instructions and/or software model(s) that may direct how the AM part is created during the additive manufacturing process. In some embodiments, the exemplary inventive computer-based AM system may be configured to perform the above analysis/determination as part of a real-time feedback mechanism that may be configured to utilize the analysis/determination performed during the activity of item 104 to influence, in real time, how an exemplary AM process performs during one or more preceding and/or subsequent activities of the exemplary inventive computer-based AM system of Fig. 1.

[00051] Referring to, for example, item 106 of Fig. 1, in at least some embodiments, based on the part data and additional data generated at preceding stage(s), the exemplary inventive computer-based AM system may analyze/determine how a proposed (initial) design of the AM part in the part data received/obtained by the exemplary inventive computer-based AM system would be suitable/fit to perform its intended function(s). In some embodiments, the exemplary inventive computer-based AM system may be configured to analyze/determine how the design of the AM part would influence the overall performance of the exemplary inventive computer- based AM system. In some embodiments, during a part of the activity of item 106, the exemplary inventive computer-based AM system may be configured to dynamically alter the material composition of the initial design of the AM part to improve performance of the exemplary inventive computer-based AM system during one or more subsequent activities without sacrificing and/or improving how the AM part would perform for its intended function(s). In some embodiments, the exemplary inventive computer-based AM system may be configured to perform such analysis/determination as part of a real-time feedback mechanism that may be configured to utilize the analysis/determination during the activity of item 106 to influence, in real time, how the exemplary AM process performs during one or more preceding and/or subsequent activities of the exemplary inventive computer-based AM system of Fig. 1.

[00052] Referring to item 108 of Fig. 1, in at least some embodiments, based on the part data and additional data generated at preceding stage(s), the exemplary inventive computer-based AM system may select at least one of: i) feedstock (e.g., usable material) processing paths, ii) material composition(s) from one or more pre-determined material compositions that would be sufficiently suitable to the intended function(s) of the AM part, and/or iii) AM processing path(s).

[00053] In some embodiments, the exemplary inventive computer-based AM system may be configured to analyze how the material composition of the AM part would influence the overall performance of the exemplary inventive computer-based AM system. For example, the exemplary inventive computer-based AM system may be configured to analyze one or more characteristics, properties, and/or performance criteria/specifications based, at least in part, on one or more intended end-use applications. For example, the exemplary inventive computer- based AM system may be configured to analyze one or more of life expectancy, cost, weight, density (e.g., theoretical density), porosity, corrosion resistance, and other similarly suitable parameter of the AM build part.

[00054] In some embodiments, a part of the activity of item 108, the exemplary inventive computer-based AM system may be configured to select, from one or more pre-determined material compositions, an initial (first) material composition of the AM part, and processing path in the part data to improve performance of the exemplary inventive computer-based AM system during one or more subsequent activities without sacrificing and/or improving how the AM part would perform for its intended function(s). In some embodiments, the exemplary inventive computer-based AM system may be configured to perform such analysis/determination as part of a real-time feedback mechanism that may be configured to utilize the analysis/determination during the activity of item 108 to influence, in real time, how the exemplary AM process performs during one or more preceding and/or subsequent activities of the exemplary inventive computer-based AM system of Fig. 1.

[00055] Referring to item 110 of Fig. 1, in at least some embodiments, based on the part data and additional data generated at preceding stage(s), the exemplary inventive computer-based AM system may run one or more part-build simulations to analyze/test how, for example without limitation, one or more characteristics of the AM part would influence and/or be influenced by one or more subsequent activities of the exemplary inventive computer-based AM system. In some embodiments, as a part of the activity 110, the exemplary inventive computer-based AM system may be configured to dynamically alter, in real-time, the one or more part build simulation parameters based, at least in part, on one or more real-time characteristics of the exemplary inventive computer-based AM system and/or one or more real-time internal and/or external conditions associated with the exemplary inventive computer-based AM system (e.g., a temperature inside of an AM machine). In some embodiments, the exemplary inventive computer-based AM system may be configured to perform such analysis/determination as part of a real-time feedback control mechanism that may be configured to utilize the one or more AM part build simulations developed during the activity of item 110 to influence, in real time, how the exemplary AM process performs during one or more preceding and/or subsequent activities of the exemplary inventive computer-based AM system of Fig. 1. In some embodiments, the one or more AM part build simulations may be based, at least in part, on at least in part, any given simulation of any given part, may be influenced by and compared to simulation(s) of other sufficiently similar AM part(s).

[00056] In some embodiments, during the activity of item 110, the exemplary inventive computer-based AM system may be configured to generate a dynamically adjustable digital representation ("digital twin") 138 of the AM part that would be manufactured. In some embodiments, the digital twin 138 includes current and/or historical data related to function(s) of the AM part; the design of the AM part, and/or the material composition of the AM part (the part-centered data such as design data 128 and material data 130). In some embodiments, in addition to the part-centered data, the digital twin 138 may include AM process parameter(s) associated with the exemplary AM process to be employed to manufacture the AM part and/or code instructions that are configured to direct an exemplary AM machine to build the AM part (the build-centered data such as simulation data 132 and process data 134). In some embodiments, the build-centered data may include historical error data generated during the additive manufacturing of other similar AM part(s) (i.e., digital twin(s) of previously manufactured other similar AM part(s)).

[00057] In some embodiments, in addition to the part-centered data and the build- centered data, the digital twin 138 may include certification requirement data (e.g., defect determination parameter(s)) that may be employed to certify that the AM part would be fit for its intended function(s) in connection with in-situ monitoring (item 116) and post-build inspection (item 118) (the certification-centered data such as inspection data 136). In some embodiments, the digital twin 138 may be configured to be self-contained, self-adjustable, and/or self-executing computer entity that is agnostic to a type of an AM machine that may be employed to build the AM part.

[00058] Referring to item 112 of Fig. 1, in at least some embodiments, the exemplary inventive computer-based AM system may be configured to utilize the digital twin 138 to determine one or more settings for the exemplary AM machine for building the AM part (AM machine setting data). In some embodiments, the exemplary inventive computer-based AM system may be configured, during the activity of item 112 to incorporate the AM machine setting data into the digital twin 138. In some embodiments, the AM machine setting data may include data that cause the exemplary machine to calibrate itself in a particular way prior to building the AM part (AM machine calibration data). In some embodiments, as a part of the activity of item 112, the exemplary inventive computer-based AM system may be configured to utilize the monitoring data collected, in real-time, about the exemplary AM machine, while the exemplary AM machine builds other AM part(s), to dynamically adjust the AM machine setting data in the digital twin 138 of the AM part to account, without limitation, for machine-to-machine parameter variability.

[00059] In some embodiments, the monitoring data may include at least one of: i) operational parameter(s) of the exemplary AM machine, ii) internal (in-situ) conditions of the exemplary AM machine (e.g., temperature within a build chamber, 0₂ concentration, etc.), which may be generated, for example without limitation, during activity of item 116, and/or iii) external conditions associated with the exemplary AM machine (e.g., environmental conditions (e.g., surrounding temperature, atmospheric pressure, humidity, etc.)).

[00060] Referring to item 114 of Fig. 1, the exemplary inventive computer-based AM system may be configured to execute the digital twin 138 so that the AM machine may be instructed to build the AM part in accordance with the corresponding digital twin 138. For example, based on the digital twin 138, the AM machine may be instructed to deposit an initial layer of AM part based on an estimated build position, extract actual coordinates of the build layer, compare the coordinates of the initial build layer with the estimated coordinates sent to the AM machine, and determining a deviation, if any, between an ideal (estimated) build of the digital twin 138 and the actual build layer.

[00061] In some embodiments, as a part of activity of item 114, based on the digital twin 138, if particular point(s) in the build portion of the AM part is/are determined to deviate from a threshold condition to a tolerable degree (e.g., an out-of-compliance-but-reparable condition), the exemplary inventive computer-based AM system may be configured to mitigate such noncompliance by adjusting build instruction(s) for next build layer(s) and/or build portion(s) of the same layer in which the noncompliance has been determined. In some embodiments, the out- of-compliance-but-reparable condition may be a condition in which repair would not be needed. In one embodiment, the out-of-compliance-but-reparable condition may be a condition that would be within tolerances without need to repair. In one embodiment, the out-of-compliance- but-reparable condition may be a condition that would be outside of tolerances but still repairable.

[00062] In some embodiments, as a part of activity of item 114, based on the digital twin 138, if particular point(s) in the build portion of the AM part is/are determined to deviate from a threshold condition to a non-tolerable degree (unrepairable condition), the exemplary inventive computer-based AM system may be configured to cause the exemplary AM machine to stop the build process. In such case, to, for example, provide and/or promote the efficient use of feedstock material(s) and/or AM machine build time, resulting in cost savings, the defective AM intermediate may be discarded, avoiding the deposition of additional layers.

[00063] In some embodiments, the exemplary inventive computer-based AM system may be configured to execute an active feedback control mechanism (item 126 of Fig. 1) which may be triggered based, at least in part, on the in-situ monitoring data (item 116) whenever there is/are discrepancy(ies)/deviation(s) within at least one of: i) definitions determined during the material selection activity (item 108), with or without executing the iterative adjustment of build material selection (item 122 of Fig. 1); ii) definitions determined during the part build simulation activity (item 110), with or without the interposition of the optimization step 124); and/or iii) definitions determined during the AM machine's set points determination (item 112).

[00064] In some embodiments, independently of the discrepancies identified during the material selection activity (item 108) (optionally influenced by item 122), during the part build simulation activity (item 110) (optionally influenced by item 124) and during the AM machine's set points determination (item 112) being known or quantifiable, the inventive active feedback control mechanism (item 126) may be configured to either interrupt the build process of the AM part and/or re-run the iterative adjustments (items 122 and/or 124) to affect values of items 108, 110, and 112 of Fig. 1 until quality metrics identified in item 116 meet the specification. Consequently, in at least some embodiments, the in-situ monitoring (item 116) drives the inventive active feedback control mechanism (item 126) to dynamically specify machine set points that result in a successful completion of the build or in sufficiently earlier stop of the build process to minimize the waste of material and/or time. In some embodiments, the inventive active feedback control mechanism (item 126) may be configured as at least one of suitable control strategies such as, without limitation, classical Proportional-Integral-Derivative (PID) control, adaptive control, optimal control, and combinations thereof, etc.

[00065] Referring to item 118 of Fig. 1, in at least some embodiments, the exemplary inventive computer-based AM system may be configured to generate a final state of the digital twin 138 after the physical AM part (item 120: the physical twin) has passed the post-build inspection (item 118) so that the final state of the digital twin 138 is utilized to certify a subsequently built AM part as being fit for its intended function(s) (e.g., compliance with the certification requirements and other desired requirement(s)) without actual/physical evaluation of the subsequent AM part itself. In some embodiments, the post-build inspection (item 118) may include non-destructive testing, destructive testing (completed on parts), or both.

[00066] Referring to at least activities of items 114 and 116 of Fig. 1, the exemplary inventive computer-based AM system may be configured to dynamically adjust, in real-time, the digital twin 138 and/or the AM build process based, at least in part, on and may include at least one of: i) the part design data (item 128 of Fig. 1), ii) the material composition data (item 130 of Fig. 1), iii) the part-build simulation data (item 132 of Fig. 1), iv) the AM process data (item 134 of Fig. 1), and/or v) the inspection/certification data (item 136 of Fig. 1).

[00067] In some embodiment, the AM process data may be the process data collected during the production and/or certification of similar AM part(s), which may be then used to complete one or more of i) the part design data 128, ii) the material composition data 130, and/or iii) the part-build simulation data 132. In some embodiment, the inspection/certification data 136 is used to adjust one or more of i) the part design data 128, ii) the material composition data 130, and/or iii) the part-build simulation data 132.

[00068] In some embodiment, the digital twin 138 may be stored according to a predetermined data model and/or schema and include all of data items 128-136. In some embodiment, the digital twin 138 may include data that describes the machine setup changes resulting from the inventive active feedback control mechanism (item 126). In some embodiments, the digital twin 138 of AM process parts may be configured to be processed by applying at least one of suitable analytical techniques such as, without limitation, machine learning algorithms, neural networks, and/or predictive modelling techniques.

Production and Processing

[00069] In some embodiments, the AM part/product may be subject to any appropriate dissolving (e.g. includes homogenization), working and/or precipitation hardening steps. If employed, the dissolving and/or the working steps may be conducted on an intermediate form of the additively manufactured body and/or may be conducted on a final form of the additively manufactured body. If employed, the precipitation hardening step is generally conducted relative to the final form of the AM part/product. [00070] After or during production, an AM part/product may be deformed (e.g., by one or more of rolling, extruding, forging, stretching, compressing). The final deformed product may realize, for instance, improved properties due to the tailored regions and thermo-mechanical processing of the final deformed AM part/product. Thus, in some embodiments, the final product is a wrought AM part/product, the word "wrought" referring to the working (hot working and/or cold working) of the AM part/product, wherein the working occurs relative to an intermediate and/or final form of the AM part/product. In other approaches, the final product is a non-wrought product, i.e., is not worked during or after the additive manufacturing process. In these non-wrought product embodiments, any appropriate number of dissolving and precipitating steps may still be utilized.

Product Applications

[00071] The resulting AM part/products made in accordance with the systems and methods described herein may be used in a variety of product applications. In one embodiment, the AM parts (e.g. metal alloy parts) are utilized in an elevated temperature application, such as in an aerospace or automotive vehicle. In one embodiment, an AM part or product is utilized as an engine component in an aerospace vehicle (e.g., in the form of a blade, such as a compressor blade incorporated into the engine). In another embodiment, the AM part or product is used as a heat exchanger for the engine of the aerospace vehicle. The aerospace vehicle including the engine component / heat exchanger may subsequently be operated. In one embodiment, the AM part or product is an automotive engine component. The automotive vehicle including an automotive component (e.g. engine component) may subsequently be operated. For instance, the AM part or product may be used as a turbo charger component (e.g., a compressor wheel of a turbo charger, where elevated temperatures may be realized due to recycling engine exhaust back through the turbo charger), and the automotive vehicle including the turbo charger component may be operated. In another embodiment, an AM part or product may be used as a blade in a land based (stationary) turbine for electrical power generation, and the land-based turbine included the AM part or product may be operated to facilitate electrical power generation. In some embodiments, the AM part or products are utilized in defense applications, such as in body armor, and armed vehicles (e.g., armor plating). In other embodiments, the AM part or products are utilized in consumer electronic applications, such as in consumer electronics, such as, laptop computer cases, battery cases, cell phones, cameras, mobile music players, handheld devices, computers, televisions, microwaves, cookware, washers/dryers, refrigerators, and sporting goods, among others.

[00072] In another aspect, the AM part or products are utilized in a structural application.

In one embodiment, the AM part or products are utilized in an aerospace structural application. For instance, the AM part or products may be formed into various aerospace structural components, including floor beams, seat rails, fuselage framing, bulkheads, spars, ribs, longerons, and brackets, among others. In another embodiment, the AM part or products are utilized in an automotive structural application. For instance, the AM part or AM part or products may be formed into various automotive structural components including nodes of space frames, shock towers, and subframes, among others. In one embodiment, the AM part or product is a body-in-white (ΒΓνΥ) automotive product.

[00073] In another aspect, the AM part or products are utilized in an industrial engineering application. For instance, the AM part or products may be formed into various industrial engineering products, such as tread-plate, tool boxes, bolting decks, bridge decks, and ramps, among others. [00074] Fig. 2 shows an illustrative example of an overview of a distributed computer network system 200 including an exemplary inventive computer-based AM system that may be configured to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. In some embodiments, the exemplary inventive AM system may include several different entities, such as an AM operator's terminal 208 and customers 204 all operatively communicable via a shared communication network 206, such that data, such as the inventive AM digital twin files, may be transferred between any one of the aforementioned connected entities 202 and 204. The customer logical environment 204 may include an authentication server that may be arranged to authenticate if a customer entity is authorized to access a relevant data file, such as a particular AM digital twin. In some embodiments, the shared communication network 206 may relate to the Internet, a LAN, a WAN, or any other suitable computer network. In some embodiments, the AM process logic environment 202 may effectively be a print farm, comprising one or more different operatively connected AM Machines/3D printers 210. Accordingly, the terms "AM machines" and "3D print farm" may be used interchangeably to refer to the same physical entity(ies) in the ensuing description, and the term "3D print farm" is analogous to the term "3D printing bureau."

[00075] The customer environment 204 may include a server 218 operatively connected to the communication network 206, enabling direct data connections and communication with the attached terminal 208 and the 3D print farm 202. In addition, the server 218 may host a website through which a user using any one of the different operatively connected terminals 202 and 208, may interact with the customer environment 204 using standard web browsers.

[00076] In some embodiments, the server 218 may be operatively connected to a database 220, which may be stored in a storage device local to the server 218, or in an external storage unit (not shown). In some embodiments, the exemplary inventive AM system may be configured so that the customer environment 204 provides several different functions. For example, it provides a centralized network peer, which is entrusted with managing access rights to proprietary information included in the inventive AM digital twin file. It may also provide a centralized networked means for advertising and accessing content, such as the inventive AM digital twin files, AM parameter settings, and for securely distributing content between different networked terminals. Such content may also relate to CAD software made available by a software developer who can be the AM operator.

[00077] In some embodiments, the exemplary inventive AM system may be configured so that access to information included in an exemplary inventive AM digital twin file may be controlled via the customer environment 204, using a combination of unique identifier(s) and data encryption. By unique identifiers is intended any electronically verifiable identifier. For example, the unique identifier associated with a 3D printer may relate to the printer's serial number. The database 220 maintains a record of all parties registered to use the 3D printers (AM machines). Such parties may include, but are not limited to registered AM operators 208. This information may be stored as one or more records and/or tables within the database 220.

[00078] In some embodiments, the exemplary inventive AM system may be configured to require a registration capability in order for each operatively connected entity to be uniquely identifiable by the customer environment 204, to thereby enable the customer environment 204 to manage access rights to encrypted content. For example, to manage access rights to the encrypted content of exemplary inventive AM digital twin files. In some embodiments, the exemplary inventive AM system may be configured so that the exemplary 3D print farm 202 may include a server 212, which is operatively connected to the shared communication network 206. The server 212 may itself be operatively connected to one or more different AM machines/3D printers 210. In some embodiments, the function of the server 212 is to execute one or more activities identified in Fig. 1 such as dynamically instructing an appropriate AM machine 210 to AM produce an exemplary AM part based on exemplary inventive AM digital twin.

[00079] In some embodiment, individual setup parameters of a particular AM machine

(item 124 of Fig. 1) may be determined based on an execution of a calibration routine utilized at step 112 of Fig. 1. In some embodiments, the setup parameters of each AM machine may be stored in a database, such as item 214 of Fig. 2, associated with the AM machine's corresponding serial number, such as item 216 of Fig. 2. An exemplary general calibration routine performed by the exemplary inventive computer-based AM system is shown at section 4 of J. Palomo, et al., Journal of Statistical Software, "SAVE: An R Package for the Statistical Analysis of Computer Models" (2015).

[00080] In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize one or more of the following in-situ AM build monitoring techniques to generate data to be utilized by the inventive part build simulation engine of the present disclosure, such as, without limitation, techniques described in A. Sharma et al., 2006, "Apparatus and Method for Z-Height Measurement and Control for a Material Deposition Based Additive Manufacturing Process"; Proc. National Seminar on Non-Destructive Evaluation Dec. 7 - 9, 2006, Hyderabad, Indian Society for Non-Destructive Testing Hyderabad Chapter D. N.Trushnikov et al., 2012, "Online Monitoring of Electron Beam Welding of TI6AL4V Alloy Through Acoustic Emission"; and Mat.-wiss. u. Werkstofftech. 2012, 43, No. 10, (DOI 10.1002/mawe.201200933), "Secondary-Emission signal for weld formation monitoring and control in electron beam welding (EBW)."

[00081] In some embodiments, the exemplary inventive computer-based AM system may be configured to then utilize monitoring data obtained by the in-situ monitoring subsystem as, for example without limitations, detailed above to certify engineering properties of an AM manufactured part. In some embodiments, an exemplary in-situ monitoring subsystem may be configured to operate, for example, in both modes:

[00082] i) an open loop mode, when measurement(s) would be taken for creating a record of AM part condition, and

[00083] ii) a closed loop, where the obtained measurements are fed into the inventive iterative control module (item 122 of Fig. 1) and/or the part-build simulation model (item 110 of Fig. 1).

[00084] Fig. 3 shows an illustrative example of a block diagram depicting an exemplary embodiment of an in-situ monitoring subsystem 300 implemented by combination of in-situ monitoring/measurement 302 (item 116 of Fig. 1) at least one or more of:

i) iterative automatic control 304 (item 122 of Fig. 1),

ii) material selection optimal adjustment 308 (item 122 of Fig. 1), and

iii) part-build optimal adjustment 310 (item 124 of Fig. 1).

[00085] In some embodiment, the iterative automatic control module 304 may be configured to adapt the part-build parameters according to the in-situ monitoring data transmitted by the in-situ measurements 302. In some embodiments, the exemplary inventive computer- based AM system is configured to utilize the in-situ monitoring data as input to run the part-build simulation model based, at least in part, on geometry, material and/or boundary condition parameter(s). In some embodiments, based at least in part on one or more predetermined trigger conditions (e.g., new feedstock material), the exemplary inventive computer-based AM system is configured to utilize the in-situ monitoring data as input to run the part-build simulation model based, at least in part, on geometry, material and/or boundary condition parameter(s). In some embodiments, at predetermined periodicity, the exemplary inventive computer-based AM system is configured to utilize the in-situ monitoring data as input to run the part-build simulation model based, at least in part, on geometry, material and/or boundary condition parameter(s).

[00086] In some embodiments, the exemplary inventive computer-based AM system may, based on the in-situ monitoring data, be configured to validate engineering material properties and verify that the AM manufactured part, and/or the corresponding digital twin, adhere to the geometry 306 prescribed by the part design (item 106 of Fig. 1). For example, the exemplary inventive computer-based AM system may be configured to utilize numerical optimization such that a design space may be exhausted for the determination of operating/input conditions for validating engineering material properties and verifying the adherence to the geometry prescribed by the part design.

[00087] An exemplary use of the in-situ monitoring data to perform validation and verification by the exemplary inventive computer-based AM system may be based on a Volume Quality Measurement in-situ monitoring technique (VQM). In one example, the VQM may include capturing the material being deposited layer-by-layer by way of video recording and/or taking thermal images. In some embodiments, during the verification, in which the system would be verifying if the AM part is fit (still abides to the requirements) under a given defect map. For example, the exemplary inventive computer-based AM system may be configured to superimpose all layers to reconstruct a full 3D part, mapping all defects that occurred and are sufficiently large to have been captured in video. In some embodiments, this 3D part map of defects may be then compared to a specification to make a pass/fail determination. For example, the 3D map of the AM part (with the defects) may be also inputted into an exemplary inventive simulation model to predict the behaviour of the AM part with that particular defect. During validation of the material engineering properties, the exemplary inventive simulation model may be configured to span multiple scales utilizing techniques of the Integrated Computational Materials Engineering (ICME) approach.

[00088] In one embodiment, the exemplary inventive computer-based AM system may be configured to combine the in-situ monitoring 302 (item 116 of Fig. 1) with the material selection dynamic adjustment 310 (item 122 of Fig. 1) and/or part build dynamic adjustment 308 (item 124 of Fig. 1) as described above.

[00089] In some embodiments, the exemplary in-situ monitoring subsystem may be configured to collect in-situ measurements (inputs) in one or more of the following formats, but not limited to, electrical signals, laser power, laser angle, raster patterns, layer overlap, and other similarly suitable physical quantities. In some embodiments, the in-situ measurements (inputs) include, but are not limited to, electrical measurements, image based measurements and/or acoustic measurements, collected/measured one at a time and/or in combination based on a particular purpose such as determining the adherence of the partial and/or total AM build part relative to specification(s)/the corresponding digital twin and, in the closed loop, to provide data to the iterative control module (item 122 of Fig. 1) to support actions aimed at the build part compliance.

[00090] In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize various techniques to perform in-situ monitoring. [00091] In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize one or more of the following in-situ AM build monitoring techniques that may include, but are not limited to:

i) measuring the build layer temperature (e.g., via contact thermal couples);

ii) measuring the laser/electron beam power (e.g., per the control system and/or calculated wattage from some feedback on the system);

iii) performing the visual / thermal imaging (e.g., from images or video obtained during the build);

iv) acquiring sound recordings (e.g., via acoustic emission sensors located on or around the build plate);

v) performing the layer by layer optical topography;

vi) measuring gas flows (e.g., via gas flow meters / sensors placed on the entry into (e.g., pressure sensor feedback) and/or within the chamber);

vii) monitoring the molten pool size (e.g., via a camera and/or optical sensor);

viii) monitoring the powder distribution (e.g., via a camera and/or acoustic emission sensor); and ix) monitoring the powder contamination (e.g., via a camera and/or optical sensor and/or communicated from the control system with inputs/information from the feedstock source material supply).

[00092] For example, knowing a laser power may allow determining a temperature that the AM material may be subjected to, which, in turn, may influence a cooling rate relative to the ambient condition(s) in place. The in-situ monitoring may capture distortions due to inadequate cooling rates and modulate the laser power accordingly until a satisfactory condition would be met. In some embodiments, the simulation model (the digital twin) may be updated in real-time to guide the exemplary inventive computer-based AM system during this modulation.

[00093] In some embodiments, the exemplary in-situ monitoring subsystem may be configured to output in-situ monitoring data (outputs) that includes, but not limited to, orientation, volume filling (or voids), temperature, cracking, and other similarly suitable parameters.

[00094] In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize data cubes and n-D arrays to store the inventive digital twin data. In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize schema to store the inventive digital twin data for AM. Examples of data schema include but not limited to a NIST data schema with a visual representation shown at page 8 of Y. Lu et al, "AMMD- An Open Database for Additive Manufacturing Analytics," Engineering Lab, National Institute of Standards and Technology, RAPID 2017, May 9, 2017; and/or a MIMS schema (e.g., GRANTA schema (Granta Design, Materials Park, OH)).

[00095] In some embodiments, the exemplary inventive computer-based AM system may be configured to combine the in-situ monitoring data and/or the post-build inspection data (item 136 of Fig. 1) as input for the part build simulation and/or perform optimal adjustments in one or more of i) the determination AM machine's set points (item 112 of Fig. 1), the material selection adjustment 310 (item 122 of Fig. 1), and/or the part build adjustment 308 (item 124 of Fig. 1). For example, the exemplary inventive computer-based AM system may be configured to process the combination of the in-situ monitoring data (items 116 and 134 of Fig. 1) and the post-build inspection data (item 136 of Fig. 1) to generate updated geometry, material distribution(s) and/or boundary condition(s) other than those prescribed during the part design (item 106 of Fig. 1) and/or process design phases. In some embodiments, the process design phase includes but not limited to a recipe generation phase.

[00096] In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize an exemplary inventive data model to handle both the design and monitoring data. For example, an inventive digital twin may be applied during the AM process/manufacture.

[00097] In some embodiments, the exemplary inventive computer-based AM system is configured to include but not limited to design data (item 128 of Fig. 1) in the exemplary inventive digital twin during the AM processing. In one embodiment, the design data may include but not limited to part design data and process design data. In one embodiment, examples of the part design data may include but not limited to designer's name, geometry changes, and/or revision history. In one embodiment, examples of the process design data may include but not limited to designer's name, process types, candidate orientations, process simulations data, and/or process optimization data.

[00098] In some embodiments, the exemplary inventive computer-based AM system is configured to include but not limited to material data (item 130 of Fig. 1) of material powder in the exemplary inventive digital twin during the AM processing. In one embodiment, examples of the material data may include but not limited to vendor, lot number, Serial Number, particular Materials Information Management System (MIMS) identifier (e.g., GRANTA tag assigned by GRANTA MIMS (Granta Design, Materials Park, OH, USA)), and/or particular Manufacturing Execution Systems (MES) tag assigned to data entities such as batch and/or time-series such as dates. [00099] In some embodiments, the exemplary inventive computer-based AM system is configured to include but not limited to process data (item 134 of Fig. 1) in the exemplary inventive digital twin during the AM processing. In one embodiment, the process data may include but not limited to preprocessing information, processing information, and/or postprocessing information. In one embodiment, examples of the pre-processing information may include but not limited to pre-processing information for manufacturing, support structure definitions, and/or process parameters to be used. In one embodiment, examples of the processing information may include but not limited to operator name, machine identification, in- situ monitoring data from in-situ monitoring (item 116 of Fig. 1), AM machine quality reports, control procedure report, and/or part images. In one embodiment, examples of the postprocessing information may include but not limited to Non-Destructive Testing (NDT) information, mechanical test information, microstructure information, blue light scan data, and/or dimensional evaluation information. In one embodiment, the post-processing information may be stored in a suitable Laboratory Information Management System (LEVIS)/ Product Lifecycle Management system (PLM), test/property database, and/or characterization database.

[000100] In some embodiments, the exemplary inventive computer-based AM system is configured to include but not limited to simulation data (item 132 of Fig. 1) and/or inspection data (item 136 of Fig. 1) in the exemplary inventive digital twin during the AM processing. In some embodiments, the exemplary inventive computer-based AM system is configured to include but not limited to customer information, part name, number, and/or specifications, and/or original model file, data, and/or geo-properties. [000101] In some embodiments, the exemplary inventive computer-based AM system is configured to provide data in digital twin to lessons, design guidelines, and/or production instructions during the AM processing.

Exemplary illustrations

[000102] The following describes exemplary AM process in accordance with one or more embodiments of the present description based on at least the following quantities:

i) Melt pool width: a high-fidelity physics simulation model that predicts the rapid solidification rate of the metal while the laser scans a particular region; for example, the predicted widths are compared to experimental measurements for the width of melt pools at various operating conditions; and/or

ii) Porosity: a mix of high-fidelity physics simulation models and empirically derived models predict the resulting porosity in metal AM test specimens; for example, the predicted porosity values are compared to experimental measurements at various operating conditions.

[000103] Figs. 4A-4E illustrate an exemplary analysis workflow for calibrating the probabilistic melt pool width simulation model during the AM process that may be used by the exemplary inventive computer-based AM system to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. Specifically, while a traditional use of simulation is by obtaining simulation results at a single set of modeling assumptions. On the other hand, Fig. 4A illustrates a diagram of melt pool width as a function of laser scan speed obtained from the probabilistic melt pool width simulation process as implemented in the exemplary inventive computer-based AM system according to some embodiment. The exemplary inventive computer-based AM system is configured to vary the melt pool width modeling assumptions and generate different simulation results accordingly, referred to as Sensitivity Analysis ("SA").

[000104] The different simulation results from the probabilistic melt pool width simulation as presented in Fig. 4A represent the uncertainty in the response, traditionally referred to as Uncertainty Quantification ("UQ"). On the other hand, Fig. 4B illustrates results obtained from the probabilistic melt pool width simulation process as implemented in the exemplary inventive computer-based AM system that is configured to repeat UQ for different plausible groups of probabilistic melt pool width simulation modeling assumptions. The simulation results as shown in Fig. 4B enables illustration of a distribution over distributions of uncertainties of the probabilistic melt pool width simulations.

[000105] The distribution over distributions of uncertainties of the probabilistic melt pool width simulations as presented in Fig. 4B enables the exemplary inventive computer-based AM system to further compare the distribution over distributions with noisy measurements according to some embodiment. Fig. 4C illustrates comparison results obtained from the probabilistic melt pool width simulations process as implemented in the exemplary inventive computer-based AM system, which is configured to compare the distribution over distributions with noisy measurements, denoted by experimental data points. The experimental data points as shown in Fig. 4C reflect the limited experimental measurements.

[000106] In some embodiments, the results from the comparison of the distribution over distributions with noisy measurements as presented by Fig. 4C may be used to perform an exemplary inventive Bayesian calibration as shown in Fig. 4D to determine a set of assumptions that represent most closely to the actual experiment. Fig. 4D illustrates a sub-process of the probabilistic melt pool width simulations process as implemented in the exemplary inventive computer-based AM system that is configured to perform the exemplary inventive Bayesian calibration to determine the set of assumptions that most closely represent the actual experiment.

[000107] The sub-process begins at step 402 in which the exemplary inventive computer- based AM system is configured to run the probabilistic melt pool width simulations for a large number of candidate modeling assumptions. Then at step 404, the exemplary inventive computer-based AM system is configured to build statistical surrogate model that approximates the input to output relationships, e.g., Output = f(Input), within the simulation. Finally, at step 406, the exemplary inventive computer-based AM system is configured to use the surrogate within a Bayesian inference computational engine to find all plausible assumptions that match the level of noise within the measurements.

[000108] Fig. 4E illustrates results obtained from the process as implemented in the exemplary inventive computer-based AM system that is configured to use the Bayesian calibrated probabilistic melt pool width simulation model to predict responses at arbitrary input configurations, based on the all plausible assumptions that match the level of noise within the measurements as obtained from the sub-process as described supra in relation with Fig. 4D.

[000109] Figs. 5A-5E illustrate an exemplary analysis workflow for calibrating the probabilistic porosity simulation model during the AM process that may be used by the exemplary inventive computer-based AM system to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. Specifically, Fig. 5A illustrates a diagram of porosity as a function of laser scan speed obtained from the probabilistic porosity simulation process as implemented in the exemplary inventive computer-based AM system according to some embodiment. The exemplary inventive computer-based AM system is configured to perform SA, i.e., varying the porosity modeling assumptions and generating different simulation results accordingly.

[000110] The different simulation results from the probabilistic porosity simulation as presented in Fig. 5A represent the UQ. On the other hand, Fig. 5B illustrates results obtained from the probabilistic porosity simulation process as implemented in the exemplary inventive computer-based AM system that is configured to repeat UQ for different plausible groups of probabilistic porosity simulation modeling assumptions. The simulation results as shown in Fig. 5B enables illustration of a distribution over distributions of uncertainties of the probabilistic porosity simulations (e.g., Dirichlet distribution).

[000111] For example, the distribution over distributions of uncertainties of the probabilistic porosity simulations as presented in Fig. 5B enables the exemplary inventive computer-based AM system to further compare the distribution over distributions with noisy measurements according to some embodiment. Fig. 5C illustrates comparison results obtained from the of the probabilistic porosity simulations process as implemented in the exemplary inventive computer-based AM system, which is configured to compare the distribution over distributions with noisy measurements, denoted by experimental data points. The experimental data points as shown in Fig. 5C reflect the limited experimental measurements.

[000112] In some embodiments, the results from the comparison of the distribution over distributions with noisy measurements as presented by Fig. 5C may be used to perform an exemplary inventive Bayesian calibration as shown in Fig. 5D to determine a set of assumptions that represent most closely to the actual experiment. Fig. 5D illustrates a sub-process of the probabilistic porosity simulations process as implemented in the exemplary inventive computer- based AM system that is configured to perform the exemplary inventive Bayesian calibration to determine the set of assumptions that most closely represent the actual experiment.

[000113] The sub-process begins at step 502 in which the exemplary inventive computer- based AM system is configured to run the probabilistic porosity simulations for a large number of candidate modeling assumptions. Then at step 504, the exemplary inventive computer-based AM system is configured to build statistical surrogate model that approximates the input to output relationships within the simulation. Finally, at step 506, the exemplary inventive computer-based AM system is configured to use the surrogate within a Bayesian inference computational engine to find all plausible assumptions that match the level of noise within the measurements.

[000114] Fig. 5E illustrates results obtained from the process as implemented in the exemplary inventive computer-based AM system that is configured to use the Bayesian calibrated probabilistic porosity simulation model to predict responses at arbitrary input configurations, based on the all plausible assumptions that match the level of noise within the measurements as obtained from the sub-process as described supra in relation with Fig. 5D.

[000115] Figs. 6A-6E illustrate an exemplary analysis workflow for calibrating the probabilistic tensile yield strength simulation model during the AM process that may be used by the exemplary inventive computer-based AM system to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. Specifically, Fig. 6A illustrates a diagram of tensile yield strength as a function of laser scan speed obtained from the probabilistic tensile yield strength simulation process as implemented in the exemplary inventive computer-based AM system according to some embodiment. The exemplary inventive computer- based AM system is configured to perform SA, i.e., varying the tensile yield strength modeling assumptions and generating different simulation results accordingly.

[000116] The different simulation results from the probabilistic tensile yield strength simulation as presented in Fig. 6A represent the UQ. On the other hand, Fig. 6B illustrates results obtained from the probabilistic tensile yield strength simulation process as implemented in the exemplary inventive computer-based AM system that is configured to repeat UQ for different plausible groups of probabilistic tensile yield strength simulation modeling assumptions. The simulation results as shown in Fig. 6B enables illustration of a distribution over distributions of uncertainties of the probabilistic tensile yield strength simulations.

[000117] The distribution over distributions of uncertainties of the probabilistic tensile yield strength simulations as presented in Fig. 6B enables the exemplary inventive computer- based AM system to further compare the distribution over distributions with noisy measurements according to some embodiment. Fig. 6C illustrates comparison results obtained from the of the probabilistic tensile yield strength simulations process as implemented in the exemplary inventive computer-based AM system, which is configured to compare the distribution over distributions with noisy measurements, denoted by experimental data points. The experimental data points as shown in Fig. 6C reflect the limited experimental measurements.

[000118] In some embodiments, the results from the comparison of the distribution over distributions with noisy measurements as presented by Fig. 6C may be used to perform an exemplary inventive Bayesian calibration as shown in Fig. 6D to determine a set of assumptions that represent most closely to the actual experiment. Fig. 6D illustrates a sub-process of the probabilistic tensile yield strength simulations process as implemented in the exemplary inventive computer-based AM system that is configured to perform the exemplary inventive Bayesian calibration to determine the set of assumptions that most closely represent the actual experiment.

[000119] The sub-process begins at step 602 in which the exemplary inventive computer- based AM system is configured to run the probabilistic tensile yield strength simulations for a large number of candidate modeling assumptions. Then at step 604, the exemplary inventive computer-based AM system is configured to build statistical surrogate model that approximates the input to output relationships within the simulation.

[000120] Finally, at step 606, the exemplary inventive computer-based AM system is configured to use the surrogate within a Bayesian inference computational engine to find all plausible assumptions that match the level of noise within the measurements.

[000121] Fig. 6E illustrates results obtained from the process as implemented in the exemplary inventive computer-based AM system that is configured to use the Bayesian calibrated probabilistic tensile yield strength simulation model to predict responses at arbitrary input configurations, based on the all plausible assumptions that match the level of noise within the measurements as obtained from the sub-process as described supra in relation with Fig. 6D.

[000122] At least some aspects of the present disclosure will now be described with reference to the following numbered clauses.

1. A method, comprising:

(A) building, by an Additive Manufacture (AM) machine, at least one portion of at least one AM part during an AM build process based at least in part on a digital twin of the at least one AM part;

wherein the digital twin comprises at least one of: i) one or more operational parameters of the AM machine for building the at least one AM part,

ii) one or more properties of a desired design for the at least AM part, or iii) one or more AM build process parameters;

wherein the digital twin is suitable to be used to certify, without a physically inspection of the actual AM part, a compliance of the actual AM part to the desired design;

(B) monitoring, by at least one in-situ monitoring sensor, the AM build process of the at least one AM part, wherein the at least one in-situ monitoring sensor is configured to utilize one or more in-situ monitoring techniques to collect and transmit in-situ monitoring sensing data related to the AM build process of the at least one AM part;

(C) receiving, by the processor, from the at least one in-situ monitoring sensor, first in- situ monitoring sensing data during the AM build process of the at least one portion of the at least one AM part;

(D) determining, by the processor, at least one discrepancy between the at least one portion of the at least one AM part and one or more predefined criteria, by comparing the first in- situ monitoring sensing data to the one or more predefined criteria that has been defined based on the digital twin;

(E) iteratively performing the following to (i) remedy the at least one discrepancy or (ii) determine that the at least one portion of the at least one AM part is to be discarded:

a) simulating, by the at least one processor, at least one AM simulation model, based on the first in-situ monitoring sensing data, to identify:

i) at least one first AM process adjustment or ii) a lack of the at least one first AM process adjustment;

wherein the at least one first AM process adjustment is an adjustment to at least one of:

1) the one or more operational parameters of the AM machine, or

2) the one or more AM build process parameters;

b) causing, by the at least one processor, to implement the at least one first AM process adjustment by the AM machine in the AM build process of the at least one portion of the at least one AM part;

c) receiving, by the at least one processor, from the at least one in-situ monitoring sensor, second in-situ monitoring sensing data related to the AM build process of the at least one portion of the at least one AM part; and

d) determining, by the at least one processor, whether

i) the at least one discrepancy has been remedied or ii) the at least one discrepancy has not been remedied; and

e) when the at least one discrepancy has not been remedied, simulating, by the at least one processor, the at least one AM simulation model, based on the second in-situ monitoring sensing data, to identify:

i) at least one second AM process adjustment or

ii) a lack of the at least one second AM process adjustment; and

(F) instructing, by the at least one processor, to discharge the at least one portion of the at least one AM part when the lack of the at least one first AM process adjustment or the lack of the at least one second AM process adjustment has been determined.

2. A system, comprising: an Additive Manufacturing (AM) machine, configured to build at least one portion of at least one AM part during an AM build process based at least in part on a digital twin of the at least one AM part;

the digital twin of the at least one AM part;

wherein the digital twin comprises at least one of:

i) one or more operational parameters of the AM machine for building the at least one AM part,

at least one in-situ monitoring sensor, configured to utilize one or more in-situ monitoring techniques to collect and transmit in-situ monitoring sensing data related to the AM build process of the at least one AM part;

at least one processor; and

a non-transitory computer readable storage medium storing thereon program logic, wherein, when executing the program logic, the at least one processor is configured to:

(A) receive, from the at least one in-situ monitoring sensor, first in-situ monitoring sensing data during the AM build process of the at least one portion of the at least one AM part;

(B) determine at least one discrepancy between the at least one portion of the at least one AM part and one or more predefined criteria, by comparing the first in- situ monitoring sensing data to the one or more predefined criteria that has been defined based on the digital twin;

(C) iteratively perform the following to (i) remedy the at least one discrepancy or (ii) determine that the at least one portion of the at least one AM part is to be discarded:

a) simulate at least one AM simulation model, based on the first in-situ monitoring sensing data, to identify:

i) at least one first AM process adjustment or

ii) a lack of the at least one first AM process adjustment;

1) the one or more operational parameters of the AM machine, or

2) the one or more AM build process parameters;

b) causing to implement the at least one first AM process adjustment by the AM machine in the AM build process of the at least one portion of the at least one AM part;

c) receiving, from the at least one in-situ monitoring sensor, second in-situ monitoring sensing data related to the AM build process of the at least one portion of the at least one AM part; and

d) determine whether

i) the at least one discrepancy has been remedied or ii) the at least one discrepancy has not been remedied; and e) when the at least one discrepancy has not been remedied, simulate the at least one AM simulation model, based on the second in-situ monitoring sensing data, to identify:

i) at least one second AM process adjustment or

ii) a lack of the at least one second AM process adjustment; and (D) instruct to discharge the at least one portion of the at least one AM part when the lack of the at least one first AM process adjustment or the lack of the at least one second AM process adjustment has been determined.

3. The method of Clause 1 or the system of Clause 2, wherein the one or more in-situ monitoring techniques are at least one of:

i) taking temperature measurements of at least one build plate;

ii) measuring beam power;

iii) taking visual images, thermal images, or both, by a camera;

iv) obtaining acoustic data from the build plate from at least one acoustic emission sensor;

v) performing layer-by-layer optical topography;

vi) obtaining gas flow data from one of more gal flow sensor;

vii) monitoring a molten pool size by the camera, an optical sensor, or both;

viii) monitoring a powder distribution by the camera, the acoustic emission sensor; or ix) monitoring a powder contamination by the camera, the optical sensor, or both.

4. The method of Clause 1 or the system of Clause 2, wherein the first in-situ monitoring sensing data and the second in-situ monitoring sensing data comprise one or more parameters selected from electrical signals, laser powers, laser angles, raster patterns, layer overlap measurements, and any combination thereof. 5. The method of Clause 1 or the system of Clause 2, wherein the compliance of the actual AM part to the desired design is determined based on the in-situ monitoring sensing data related to at least one or more particular points in a build portion of the actual AM part that are determined to deviate from the one or more predefined criteria by more than a predetermined tolerance value.

6. The method of Clause 1, wherein the step (E) is performed in a closed loop.

7. The system of Clause 2, wherein the operation (C) is performed in a closed loop.

8. The method of Clause 1, wherein the method further comprises creating, by the at least one processor, based at least in part on the in-situ monitoring sensing data, in an open loop mode, at least one data record that is representative of a condition of the at least one AM part utilizing the in-situ monitoring sensing data.

9. The system of Clause 2, wherein n the at least one processor is further configured to create, based at least in part on the in-situ monitoring sensing data, in an open loop mode, at least one data record that is representative of at least one condition of the at least one AM part.

10. The method of Clause 1 or the system of Clause 2, wherein the in-situ monitoring sensing data comprises one or more of orientation, volume filling, temperature, cracking, and other parameters.

11. The method of Clause 1, wherein the method further comprises validating, by the at least one processor, one or more engineering material properties.

12. The system of Clause 2, wherein the at least one processor is further configured to validate one or more engineering material properties. 13. The method of Clause 11, wherein the method further comprises verifying, by the at least one processor, that the at least one AM part and the corresponding digital twin meet the one or more predefined criteria.

14. The system of Clause 12, wherein the at least one processor is further configured to verify that the at least one AM part and the corresponding digital twin meet the one or more predefined criteria.

15. The method of Clause 11, wherein the method further comprises utilizing, by the at least one processor, at least one numerical optimization to exhaust a design space for a determination of one or more input conditions for the validating the one or more engineering material properties and the verifying that the at least one AM part and the corresponding digital twin meet the one or more predefined criteria.

16. The system of Clause 14, wherein the at least one processor is further configured to utilize at least one numerical optimization to exhaust a design space for a determination of one or more input conditions for the validating the one or more engineering material properties and the verifying that the at least one AM part and the corresponding digital twin meet the one or more predefined criteria.

17. A method, comprising:

wherein the digital twin comprises at least one of:

i) one or more operational parameters of the AM machine for building the at least one AM part, ii) one or more properties of a desired design for the at least AM part, or

iii) one or more AM build process parameters;

(C) receiving, by the processor, from the at least one in-situ monitoring sensor, the in- situ monitoring sensing data during the AM build process of the at least one portion of the at least one AM part;

(D) determining, by the processor, at least one discrepancy between the at least one portion of the at least one AM part and one or more predefined criteria, by comparing the in-situ monitoring sensing data to the one or more predefined criteria that has been defined based on the digital twin;

a) simulating, by the at least one processor, at least one AM simulation model, based on the in-situ monitoring sensing data, to identify:

i) at least one AM process adjustment or

ii) a lack of the at least one AM process adjustment; and b) causing, by the at least one processor, to i) implement the at least one AM process adjustment by the AM machine or 2) discharge the at least one portion of the at least one AM part when the lack of the at least one AM process adjustment has been determined.

18. A system, comprising:

an Additive Manufacturing (AM) machine, configured to build at least one portion of at least one AM part during an AM build process based at least in part on a digital twin of the at least one AM part;

the digital twin of the at least one AM part;

wherein the digital twin comprises at least one of:

at least one processor; and a non-transitory computer readable storage medium storing thereon program logic, wherein, when executing the program logic, the at least one processor is configured to:

(A) receive, from the at least one in-situ monitoring sensor, the in-situ monitoring sensing data during the AM build process of the at least one portion of the at least one AM part;

(B) determine at least one discrepancy between the at least one portion of the at least one AM part and one or more predefined criteria, by comparing the in-situ monitoring sensing data to the one or more predefined criteria that has been defined based on the digital twin;

a) simulate at least one AM simulation model, based on the in-situ monitoring sensing data, to identify:

i) at least one AM process adjustment or

ii) a lack of the at least one AM process adjustment; and

b) cause to: i) implement the at least one AM process adjustment by the AM machine or ii) discharge the at least one portion of the at least one AM part when the lack of the at least one first AM process has been determined.

[000123] All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the principles of the invention and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same.

Claims

CLAIMS: What is claimed is

1. A method, comprising:

wherein the digital twin comprises at least one of:

i) at least one first AM process adjustment or

ii) a lack of the at least one first AM process adjustment;

1) the one or more operational parameters of the AM machine, or

2) the one or more AM build process parameters;

d) determining, by the at least one processor, whether

i) the at least one discrepancy has been remedied or ii) the at least one discrepancy has not been remedied; and e) when the at least one discrepancy has not been remedied, simulating, by the at least one processor, the at least one AM simulation model, based on the second in-situ monitoring sensing data, to identify:

i) at least one second AM process adjustment or

ii) a lack of the at least one second AM process adjustment; and

2. The method of Claim 1, wherein the one or more in-situ monitoring techniques are at least one of:

i) taking temperature measurements of at least one build plate;

ii) measuring beam power;

iii) taking visual images, thermal images, or both, by a camera;

v) performing layer-by-layer optical topography;

vi) obtaining gas flow data from one of more gal flow sensor;

vii) monitoring a molten pool size by the camera, an optical sensor, or both;

3. The method of Claim 1, wherein the first in-situ monitoring sensing data and the second in-situ monitoring sensing data comprise one or more parameters selected from electrical signals, laser powers, laser angles, raster patterns, layer overlap measurements, and any combination thereof.

4. The method of Claim 1, wherein the compliance of the actual AM part to the desired design is determined based on the in-situ monitoring sensing data related to at least one or more particular points in a build portion of the actual AM part that are determined to deviate from the one or more predefined criteria by more than a predetermined tolerance value.

5. The method of Claim 1, wherein the step (E) is performed in a closed loop.

6. The method of Claim 1, further comprising:

creating, by the at least one processor, based at least in part on the in-situ monitoring sensing data, in an open loop mode, at least one data record that is representative of a condition of the at least one AM part utilizing the in-situ monitoring sensing data.

7. The method of Claim 1, wherein the in-situ monitoring sensing data comprises one or more of orientation, volume filling, temperature, cracking, and other parameters.

8. The method of Claim 1, further comprising:

validating, by the at least one processor, one or more engineering material properties.

9. The method of Claim 8, further comprising:

verifying, by the at least one processor, that the at least one AM part and the corresponding digital twin meet the one or more predefined criteria.

10. The method of Claim 9, further comprising:

utilizing, by the at least one processor, at least one numerical optimization to exhaust a design space for a determination of one or more input conditions for the validating the one or more engineering material properties and the verifying that the at least one AM part and the corresponding digital twin meet the one or more predefined criteria.

11. A system, comprising:

the digital twin of the at least one AM part;

wherein the digital twin comprises at least one of:

at least one processor; and

(A) receive, from the at least one in-situ monitoring sensor, first in-situ monitoring sensing data during the AM build process of the at least one portion of the at least one AM part; (B) determine at least one discrepancy between the at least one portion of the at least one AM part and one or more predefined criteria, by comparing the first in- situ monitoring sensing data to the one or more predefined criteria that has been defined based on the digital twin;

i) at least one first AM process adjustment or

ii) a lack of the at least one first AM process adjustment;

1) the one or more operational parameters of the AM machine, or

2) the one or more AM build process parameters;

d) determine whether

e) when the at least one discrepancy has not been remedied, simulate the at least one AM simulation model, based on the second in-situ monitoring sensing data, to identify:

i) at least one second AM process adjustment or

12. The system of Claim 11, wherein the one or more in-situ monitoring techniques are at least one of:

i) taking temperature measurements of at least one build plate;

ii) measuring beam power;

iii) taking visual images, thermal images, or both, by a camera;

v) performing layer-by-layer optical topography;

vi) obtaining gas flow data from one of more gal flow sensor;

vii) monitoring a molten pool size by the camera, an optical sensor, or both;

13. The system of Claim 11, wherein the first in-situ monitoring sensing data and the second in-situ monitoring sensing data comprise one or more parameters selected from electrical signals, laser powers, laser angles, raster patterns, layer overlap measurements, and any combination thereof.

14. The system of Claim 11, wherein the compliance of the actual AM part to the desired design is determined based on the in-situ monitoring sensing data related to at least one or more particular points in a build portion of the actual AM part that are determined to deviate from the one or more predefined criteria by more than a predetermined tolerance value.

15. The system of Claim 11, wherein the operation (C) is performed in a closed loop.

16. The system of Claim 11, wherein the at least one processor is further configured to create, based at least in part on the in-situ monitoring sensing data, in an open loop mode, at least one data record that is representative of at least one condition of the at least one AM part.

17. The system of Claim 11, wherein the in-situ monitoring sensing data comprises one or more of orientation, volume filling, temperature, cracking, and other parameters.

18. The system of Claim 11, wherein the at least one processor is further configured to validate one or more engineering material properties.

19. The system of Claim 18, wherein the at least one processor is further configured to verify that the at least one AM part and the corresponding digital twin meet the one or more predefined criteria.

20. The system of Claim 19, wherein the at least one processor is further configured to utilize at least one numerical optimization to exhaust a design space for a determination of one or more input conditions for the validating the one or more engineering material properties and the verifying that the at least one AM part and the corresponding digital twin meet the one or more predefined criteria.