EP4689817A1

EP4689817A1 - System and method for performing virtual orchestration for managing and using a machining equipment

Info

Publication number: EP4689817A1
Application number: EP24720857.2A
Authority: EP
Inventors: Chethan Ravi B R; Armin Roux; O.V.R. Krishna CHAITANYA; Vinay Ramanath; Srividhya SINGAM; Basil CARDOZ
Original assignee: Siemens AG; Siemens Corp
Current assignee: Siemens AG; Siemens Corp
Priority date: 2023-05-10
Filing date: 2024-04-24
Publication date: 2026-02-11
Also published as: CN121511432A; EP4462204A1; WO2024231108A1

Abstract

A system (100) and method for performing virtual orchestration for managing and using a machining equipment (105) is disclosed herein. The method comprises receiving, by a proces sing unit, a request from a user interface (165), for performing virtual orchestration of the machining equipment (105). Further, at least one virtual replica of a spindle bearing assembly suit able for the machining equipment (105), is selected from a plurality of virtual replicas stored in a digital library within a virtual layer, upon receiving the request. Further, a digital replica of at least the spindle bearing assembly suitable for the machining equipment (105) within the virtual layer by updating the selected virtual replica based on the input data. Based on execution of at least one simulation using the configured digital replica, one or more workflow parame ters associated with the spindle bearing assembly are computed. Further, at least one action is performed based on the one or more workflow parameters to generate an outcome.

Description

202301865 1 Description SYSTEM AND METHOD FOR PERFORMING VIRTUAL ORCHESTRATION FOR MANAGING AND USING A MACHINING EQUIPMENT The present invention relates to machining equipment, and more particularly relates to a system and a method for performing virtual orchestration for managing and using a machining equip- ment. Traditionally, a consumer purchases a machining equipment from an Original Equipment Manufacturer (OEM) by paying a price upfront. Upon purchase, the consumer assumes responsibility for the whole lifespan of the machining equipment. Conse- quently, the consumer in addition keeps paying for repairs, maintenance and spare parts over a lifespan of the machining equipment. As an alternative, the consumer may be allowed to take the machining equipment based on a free-care package from the OEM, wherein the consumer may pay a usage fee associated with the machining equipment based on attributes such as per- formance, availability, uptime etc. Such a mechanism offer flexibility to the consumer to make the payment based on a nature of usage of the machining equipment. Therefore, there is a need for a mechanism that predicts such attributes associated with a machining equipment for enabling charging of the consumer based on usage of the machining equip- ment. Disclosed herein is a system and a method for performing vir- tual orchestration for managing and using a machining equip- ment. The method includes receiving, by a processing unit, a request for performing virtual orchestration of a machining equipment, from a user interface. The request comprises input 202301865 2 data indicative of one or more requirements associated with performing the virtual orchestration of the machining equip- ment. The method further includes selecting at least one virtual replica, from a plurality of virtual replicas stored in a digital library within a virtual layer, upon receiving the request. Each of the virtual replicas correspond to a static mathematical representation of a spindle bearing assembly suitable for the machining equipment. In an embodiment, se- lecting the at least one virtual replica from the plurality of virtual replicas includes identifying, from the input data, metadata indicative of the one or more requirements for per- forming virtual orchestration within the virtual layer. Fur- ther, a search logic is generated based on the metadata iden- tified. Further, a search is performed in the digital library comprising the plurality of virtual replicas, based on the generated search logic, for selecting the virtual replica. In an embodiment, wherein the virtual replica comprises an inte- grated dynamic-thermal model, comprising at least a one-dimen- sional dynamic model operably coupled to a one-dimensional thermal model of the spindle bearing assembly. In a further embodiment, the virtual replica further comprises a motion simulation model operably coupled to the integrated dynamic- thermal model, wherein the at least one motion simulation model is configured for dynamically computing con-tact forces in the spindle bearing assembly. The method further includes configuring a digital replica of at least the spindle bearing assembly suitable for the machin- ing equipment within the virtual layer, by updating the se- lected virtual replica based on the input data. The digital 202301865 3 replica is a dynamic mathematical representation of the spin- dle bearing assembly corresponding to the selected virtual replicas in the digital library. The method further includes computing one or more workflow parameters associated with the spindle bearing assembly based on execution of at least one simulation using the configured digital replica. In an embodiment, computing the one or more workflow parameters includes executing one or more simulation instances of the configured digital replica in a simulation environment to generate one or more simulation results indic- ative of the one or more workflow parameters. The method further includes performing at least one action based on the one or more workflow parameters to generate an outcome. In an embodiment, the outcome of performing the at least one action is an optimal configuration for the spindle bearing assembly, wherein performing the at least one action includes determining whether the one or more workflow parame- ters meet a predetermined criterion. If at least one of the workflow parameters fail to meet the predetermined criterion, the one or more other virtual replicas are selected from the digital library. Further, the one or more workflow parameters are recomputed based on digital replicas configured based on each of the one or more other virtual replicas, until the one or more workflow parameters meet the predetermined criterion. In an embodiment, if the one or more workflow parameters meet the predetermined criterion, a configuration of the spindle bearing assembly corresponding to the selected virtual replica is identified. In another embodiment, the outcome of performing the at least one action is an optimal operating condition for the spindle bearing assembly, wherein the optimal operating condition is 202301865 4 associated with one of autonomous operation and manual opera- tion of the machining equipment, and wherein performing the at least one action includes using an optimization algorithm to compute the optimal operation condition for the spindle bear- ing assembly based on the one or more workflow parameters. In yet another embodiment, the outcome of performing the at least one action is deviation between at least one of the workflow parameters and a corresponding measured parameter, and wherein performing the at least one action includes com- puting the deviation between the at least one of the workflow parameters and the corresponding measured parameter. In a further embodiment, the outcome of performing the at least one action is a control parameter for controlling an operation of the machining equipment, and wherein performing the at least one action includes calculating the control parameter based on the computed deviation if the deviation is greater than a predefined value. The calculated control parameter is adapted to reduce the deviation between the at least one workflow parameter and the measured parameter when applied to the ma- chining equipment. In an embodiment, the deviation is associ- ated with a production time of the machining equipment. The method further includes executing a secure wallet that is adapted based on an outcome of performing the at least one action. The secure wallet is a piece of code executable on network for controlling a transaction between a consumer node and a manufacturer node, wherein the consumer node is a node associated with a consumer of the machining equipment, and the manufacturer node is associated with a manufacturer of the machining equipment. In an embodiment, executing the secure wallet configured based on the outcome, further includes iden- 202301865 5 tifying a template based on the one or more requirements re- ceived with the request. Further, the secure wallet is config- ured using the selected template based on the outcome of per- forming the at least one action for virtual orchestration within the virtual layer of the machining equipment. In an embodiment, the method further includes generating a notification indicative of the outcome of performing the at least one action on a user interface. Disclosed herein is also a computer system arranged and con- figured to execute the steps of the computer-implemented method according to any one of the preceding method steps. Disclosed herein is also a computer-readable medium, on which program code sections of a computer program are saved, the program code sections being loadable into and/or executable by a processing unit which performs the method as described above when the program code sections are executed. The realization of the invention by a computer program product and/or a non-transitory computer-readable storage medium has the advantage that computer systems can be easily adopted by installing computer program to work as proposed by the present invention. The computer program product can be, for example, a computer program or comprise another element apart from the computer program. This other element can be hardware, for example a memory device, on which the computer program is stored, a hardware key for using the computer program and the like, and/or software, for example a documentation or a software key for using the computer program. The above-mentioned attributes, features, and advantages of this invention and the manner of achieving them, will become 202301865 6 more apparent and understandable (clear) with the following description of embodiments of the invention in conjunction with the corresponding drawings. The illustrated embodiments are intended to illustrate, but not limit the invention. The present invention is further described hereinafter with reference to illustrated embodiments shown in the accompanying drawings, in which: FIG 1A illustrates a block-diagram of a system for perform- ing virtual orchestration for managing and using at least one machining equipment, in accordance with an embodiment of the present invention; FIG 1B illustrates a block-diagram of an apparatus for per- forming virtual orchestration for managing and using the at least one machining equipment, in accordance with an embodiment of the present invention; FIG 2 illustrates a spindle bearing assembly associated with a machining equipment, in accordance with an embodiment of the present invention; FIGS 3A-B illustrates a block diagram of a digital replica of a spindle bearing assembly associated with a machin- ing equipment, in accordance with an embodiment of the present invention; FIG 4 depicts a flowchart of an exemplary method for per- forming virtual orchestration for managing and using a machining equipment, in accordance with an embod- iment of the present invention; FIG 5 illustrates an exemplary method of computing and maintaining optimal operating condition for the 202301865 7 spindle bearing assembly, in accordance with an em- bodiment of the present invention; and FIG 6 shows a flowchart of an exemplary method for pre- dicting production time associated with a machining equipment, in accordance with an embodiment of the present invention. Hereinafter, embodiments for carrying out the present inven- tion are described in detail. The various embodiments are de- scribed with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purpose of explanation, numerous specific details are set forth to provide a thorough under- standing of one or more embodiments. It may be evident that such embodiments may be practiced without these specific de- tails. FIG 1A illustrates a block-diagram of a system 100 for per- forming virtual orchestration for managing and using at least one machining equipment 105, in accordance with an embodiment of the present invention. More specifically, system 100 man- aging utilization of the machining equipment 105. The system 100 comprises an apparatus 110 communicatively coupled to a controller 115 associated with the machining equipment 105, through a network 120. In the present embodiment, the apparatus 110 is an edge computing device. It must be understood by a person skilled in the art that the apparatus 110 may be com- municatively coupled to a plurality of controllers, in a sim- ilar manner. It must be understood that each controller among the plurality of controllers may be associated with one or more machining equipment. It may be understood by a person skilled in the art that the apparatus 110 may combine func- tionalities of the edge computing device and the controller 115. In an alternate embodiment, the apparatus 110 is a cloud 202301865 8 platform. For example, the cloud platform may be communica- tively coupled to the controller 115 via an edge computing device. The controller 115 may enable an operator of the machining equipment 105 to define operating conditions for performing a machining operation. As may be understood by a person skilled in the art, the operating conditions may be defined by an operator before starting a machining operation or during op- eration of the machining equipment 105. For example, the op- erating conditions correspond to type of machining operation, type of cutting tool, tool settings, Automatic Tool Changer settings, feed rate, cutting speed, NC code and material test- ing data associated with a workpiece mounted on the machining equipment 105. The controller 115 may be further communicatively coupled to one or more sensing units 125 associated with the machining equipment 105. The one or more sensing units 125 include least one sensor such as an accelerometer, rotary encoder, dynamom- eter, current transformer, thermistor, and the like, config- ured to measure operational parameters associated with the machining equipment 105. The accelerometer is configured for measuring vibrations at one or more locations on the machining equipment 105. In the present embodiment, the accelerometer is installed on a structure of the machining equipment 105. For example, the accelerometer may be attached to a bed or column of the machining equipment 105. The current transformer is configured for measuring current associated with a servo mech- anism that controls motion of the spindle of the machining equipment 105. The thermistor is configured for measuring tem- perature at one or more locations on the machining equipment 105. The outputs from each of the sensor are henceforth col- lectively referred to as sensor data. 202301865 9 The controller 115 comprises a trans-receiver 130, one or more processors 135 and a memory 140. The trans-receiver 130 is configured to connect the controller 115 to a network interface 145 associated with the network 120. The controller 115 trans- mits real-time operational data to the apparatus 110 in through the network interface 145. The real-time operational data in- cludes the operating conditions set on the controller 115 and sensor data received from the one or more sensing units 125. The apparatus 110 may be a (personal) computer, a workstation, a virtual machine running on host hardware, a microcontroller, or an integrated circuit. As an alternative, the apparatus 110 may be a real or a virtual group of computers (the technical term for a real group of computers is “cluster”, the technical term for a virtual group of computers is “cloud”). The apparatus 110 includes a communication unit 150, one or more processing units 155, a display 160, a user interface 165 and a memory unit 170 communicatively coupled to each other as shown in FIG 1B. In one embodiment, the communication unit 150 includes a transmitter (not shown), a receiver (not shown) and Gigabit Ethernet port (not shown). The memory unit 170 may include 2 Giga byte Random Access Memory (RAM) Package on Package (PoP) stacked and Flash Storage. The one or more pro- cessing units 155 are configured to execute the defined com- puter program instructions in the modules. Further, the one or more processing units 155 are also configured to execute the instructions in the memory unit 170 simultaneously. The dis- play 160 includes a High-Definition Multimedia Interface (HDMI) display and a cooling fan (not shown). Additionally, control personnel may access the apparatus 110 through the user interface 165. In an embodiment, the user interface 165 may be associated with a remote device communicatively coupled 202301865 10 to the apparatus 110. For example, the remote device may pro- vide access to the apparatus via a web-based interface, a web- based downloadable application interface, and so on. The term ‘processing unit’ as used herein, means any type of computational circuit, such as, but not limited to, a micro- processor, microcontroller, complex instruction set computing microprocessor, reduced instruction set computing microproces- sor, very long instruction word microprocessor, explicitly parallel instruction computing microprocessor, graphics pro- cessor, digital signal processor, or any other type of pro- cessing circuit. The processing unit 155 may also include em- bedded controllers, such as generic or programmable logic de- vices or arrays, application specific integrated circuits, single-chip computers, and the like. In general, the processing unit 155 may comprise hardware elements and software elements. The processing unit 155 can be configured for multithreading, i.e., the processing unit 155 may host different calculation processes at the same time, executing the either in parallel or switching between active and passive calculation processes. The memory unit 170 may be volatile memory and non-volatile memory. The memory unit 170 may be coupled for communication with the processing unit 155. The processing unit 155 may execute instructions and/or code stored in the memory unit 170. A variety of computer-readable storage media may be stored in and accessed from the memory unit 170. The memory unit 170 may include any suitable elements for storing data and machine- readable instructions, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, a hard drive, a remov- able media drive for handling compact disks, digital video disks, diskettes, magnetic tape cartridges, memory cards, and the like. 202301865 11 The memory unit 170 further comprises a virtual orchestration module 175 in the form of machine-readable instructions on any of the above-mentioned storage media and may be in communica- tion to and executed by the processing unit 155. The virtual orchestration module 175 further comprises a request pro- cessing module 177, a virtual replica selection module 180, a digital replica configuration 182, a simulation module 185, an action module 187, a notification module 190 and a secure wallet module 192. The apparatus 110 may further comprise a storage unit 198. The storage unit 198 may include a database comprising a digital library. The digital library comprises data corresponding to a plurality of virtual replicas associated with one or more types of spindle bearing assemblies suitable for the machining equipment 105 or one or more other machining equipment similar to the machining equipment 105. The following description ex- plains functions of the modules when executed by the processing unit 155. The request processing module 177 is configured for receiving a request from the user interface, for performing virtual or- chestration of the machining equipment 105. The term ‘virtual orchestration’ as used herein refers to at least one of test- ing, verification, and validation, for enabling management and use of the machining equipment 105, by using a digital replica of at least the spindle bearing assembly suitable for the machining equipment 105. The request comprises one or more input data indicative of a type of the virtual orchestration, and information relevant for performing the virtual orchestration. The virtual replica selection module 180 is configured for selecting at least one virtual replica, from a plurality of virtual replicas stored in a digital library within a virtual layer, upon receiving 202301865 12 the request. The term ‘virtual layer’ as used herein refers to a logical abstraction of physical resources, such as compute, network, and storage that enables a single hardware resource to support multiple concurrent instances of the apparatus 110 or multiple hardware resources to support single instance of the apparatus 110. Herein, concurrent instances of the appa- ratus 110 refer to multiple occurrences of the same apparatus 110 running simultaneously, often on different machines or threads. Each concurrent instance of the apparatus 110 oper- ates independently of the others and may perform tasks simul- taneously. This allows for multiple users or operators of the machining equipment 105 to access the apparatus 110 at the same time and perform the virtual orchestration simultaneously without interfering with each other’s tasks. Herein, each of the virtual replicas correspond to a static mathematical representation of a spindle bearing assembly (shown in FIG 2) suitable for the machining equipment 105. The digital replica configuration module 182 configures a digital replica of at least the spindle bearing assembly suitable for the machining equipment 105 within the virtual layer, by up- dating the selected virtual replica using the input data. Herein, the digital replica is a dynamic mathematical repre- sentation of at least the spindle bearing assembly correspond- ing to the selected virtual replica. The digital replica con- figuration module 182 calibrates the digital replica to rep- licate substantially similar responses of the spindle bearing assembly. In other words, the digital replica is calibrated to ensure a certain degree of fidelity with the spindle bearing assembly. The simulation module 185 is configured for computing one or more workflow parameters associated with the spindle bearing assembly based on execution of at least one simulation using 202301865 13 the configured digital replica. The action module 187 is con- figured for performing at least one action based on the one or more workflow parameters to generate an outcome. The notifi- cation module 190 is configured for generating notifications associated with an outcome of performing the at least one action on the display 160. The secure wallet module 192 is configured for identifying a secure wallet template based on the outcome of the at least one action for virtual orchestration of the machining equip- ment 105. Herein, the term ‘outcome of the at least one action’ may include at least one of a deterministic value and proba- bilistic value indicative of an operational attribute of the spindle bearing assembly. Non-limiting examples of operational attributes include, optimal operating conditions, energy-ef- ficient operating conditions, optimal configuration, predicted machining time, availability, performance, uptime etc. Fur- ther, the secure wallet module 192 further configures a secure wallet based on the selected secure wallet template using the one or more workflow parameters, wherein the secure wallet is a piece of code executable on a decentralized network (not shown) for controlling a financial transaction between a con- sumer and an Original Equipment Manufacturer of the machining equipment 105. Those of ordinary skilled in the art will appreciate that the hardware depicted in FIGS 1A and 1B may vary for different implementations. For example, other peripheral devices such as an optical disk drive and the like, Local Area Network (LAN)/ Wide Area Network (WAN)/ Wireless (e.g., Wi-Fi) adapter, graphics adapter, disk controller, input/output (I/O) adapter, network connectivity devices also may be used in addition or in place of the hardware depicted. The depicted example is provided for the purpose of explanation only and is not meant 202301865 14 to imply architectural limitations with respect to the present disclosure. In the present embodiment, the machining equipment 105 is con- sidered to a CNC lathe, for ease of explanation. The CNC lathe is a three-axis machine comprising the spindle attached to a tool holder. The tool holder holds cutting tools required for machining a workpiece. The spindle is supported by an Angular Contact Ball Bearing (ACBB) assembly mounted on a shaft of the spindle. FIG 2 illustrates an ACBB assembly 200 of a spindle associated with a machining equipment 105, in accordance with an embodi- ment of the present invention. The ACBB assembly 200, hence- forth called the spindle bearing assembly 200, comprises a housing 205 that houses an outer race 210, an inner race 215, and a plurality of balls 220 of uniform size disposed between the outer race 210 and the inner race 215. FIGS 3A & 3B illustrate a digital replica 300 associated with the spindle bearing assembly 200, in accordance with an embod- iment of the present invention. Herein, the digital replica 300 is based on a virtual replica comprising a one-dimensional (1D) dynamic model 305 of the spindle bearing assembly 200. The 1D dynamic model 305 is built using a 1D simulation soft- ware that uses codes and equations of motions to solve mathe- matical problems. In particular, the 1D dynamic model 305 is a 5 Degree of Freedom (DOF) model of the spindle bearing as- sembly 200. The 5 DOF model include 2 planar DOFs of inner race 215, 2 planar DOFs for the outer race 210, and 1 planar DOF for the housing 205. Further, inputs to the 1D dynamic model 305 comprises product specifications such as geometric parameters 310 (such as mass, diameter etc., joint stiffnesses, damping values), material properties 315, applied preload 320, radial load and operating speed associated with the spindle 202301865 15 bearing assembly 200. A further input to the 1D dynamic model 305 comprises contact forces 330 between each of the balls 220 and the inner race 215 of the spindle bearing assembly 200. The outputs from the 1D dynamic model 305 comprises performance metrics of the spindle bearing assembly 200 such as component displacements 335, velocities and acceleration plots 340 of the housing 205, the outer race 210, and the inner race 215. The virtual replica further comprises a 1D thermal model 350 of the spindle bearing assembly 200. The 1D dynamic model and the 1D thermal model are operably coupled as shown in FIG 3B to form an integrated dynamic-thermal model. In an example, the integrated dynamic-thermal model may provide a mathemati- cal relationship between dynamic and thermal nature of the spindle bearing assembly. The inputs to the 1D thermal model 350 comprise contact forces 330 computed by a motion simulation model 345. The output from the 1D thermal model 350 includes the heat generated in the spindle bearing assembly 200 or a bearing temperature 360. Herein, the term ‘bearing tempera- ture’ may refer to a temperature of the outer race 210 and/or the inner race 215. The bearing temperature 360 is further used to calibrate values of lubrication parameters 355 and material properties or geometric parameters 310 of the spindle bearing assembly 310, using predetermined mathematical equa- tions. Based on the calibrated material properties or geomet- ric parameters 310, an induced thermal preload may be computed. The induced thermal preload is further used to calibrate the applied preload 320. The motion simulation model 345, operably coupled to the in- tegrated dynamic-thermal model as shown in FIG 3B, is config- ured to dynamically compute the contact forces 330 in the spindle bearing assembly 200. The motion simulation model 345 computes the contact forces 330 based on the applied preload 202301865 16 320, the radial load and the operating speed 325 of the shaft on which the spindle bearing assembly 200 is mounted. In an implementation, the motion simulation model 345 comprises em- pirical equations that may be solved using a 1D simulation software. In another implementation, the motion simulation model 345 comprises a 3D motion simulation model built using a three-dimensional (3D) CAD software. In the 3D motion simu- lation model, 3D contacts are defined between the balls 220 and, the inner race 215 and the outer race 210, based on stiffness and damping values computed using Hertzian contact theory. The inputs to the 3D motion simulation model includes bearing component displacements 335, the applied preload 320, the radial load and the operating speed 325 of the shaft. In an embodiment, the integrated dynamic-thermal model is co- simulated with the 3D motion simulation model 345 as shown in FIG 3B. Herein, the 1D simulation software acts as a master program and the 3D CAD software acts as a slave program. The 1D simulation software further generates main outputs such as component displacements 335, velocities and acceleration plots 340, heat generated in the bearing, bearing temperature 360, induced thermal preload, lubrication parameters 355 etc. Advantageously, the 3D CAD software allows modeling of faults, defects and wears in the spindle bearing assembly 200 for precise calculation of the contact forces 330, which is oth- erwise not possible using only 1D simulation software. For example, a fault in the spindle bearing assembly 200 may be modelled as a localized rectangular depression in the inner race 215 or the outer race 210, in the 3D motion simulation model 345. Further, the 3D motion simulation model 345 may also be converted to a finite element mesh to identify contact stresses at the fault. In particular, the workflow parameters 202301865 17 required for estimating a realistic state of the spindle bear- ing assembly 200 includes the velocities and acceleration plots 340, the calibrated preload 320, the contact forces 330 and the bearing temperature 360. FIG 4 depicts a flowchart of an exemplary method 400 for vir- tual orchestration for managing and using the machining equip- ment 105, in accordance with an embodiment of the present invention. At step 405, a request for performing virtual orchestration of the machining equipment 105, by the processing unit 155, is received from the user interface 165. The request comprises input data indicative of one or more requirements associated with the virtual orchestration. For example, the input data may include a type of virtual commissioning to be performed, and information relevant for performing the virtual orchestra- tion. The type of virtual commissioning may be for example, one of virtual testing and virtual designing of a spindle bearing assembly associated with the machining equipment 105. Further, the virtual testing may be associated with identify- ing an optimal configuration for spindle bearing assembly of the machining equipment 105, with identifying optimal operat- ing conditions for the bearing assembly, predicting machining time for the test inputs etc. The input data may include, for example, initial values for operating conditions, a configuration of the bearing assembly an expected production time, an expected energy efficiency, operational data etc. for predicting a behavior of the bearing assembly in a hypothetical scenario. The operational data may be obtained from, for example, real-time or historic data as- sociated with the machining equipment 105 or another similar machining equipment, simulated operational data or manually 202301865 18 entered by a human operator. In a further embodiment, the apparatus 110 may be further configured to acquire real-time operational data from, for example, the sensing units 125, the controller 115 etc. associated with the machining equipment 105 or another similar machining equipment. For example, the operational data may be associated with acceleration, thermal profile, operating shaft speed, NC program etc. In an embodiment, a user interface of the apparatus 110 may be dynamically configured for receiving the request from the hu- man operator based on a type of virtual orchestration selected by the human operator from a set of virtual orchestration options. For example, if the human operator chooses virtual testing for determining optimal operating conditions of the bearing assembly, the user interface may be dynamically con- figured to receive input data such as initial values of the operating conditions. At step 410, at least one virtual replica is selected, from a plurality of virtual replicas stored in a digital library within a virtual layer, upon receiving the request. Each of the virtual replicas correspond to a static mathematical rep- resentation of a spindle bearing assembly suitable for the machining equipment 105. In the present embodiment, the vir- tual replica comprises an integrated dynamic-thermal model as shown in FIG 3B. In an embodiment, each of the plurality of virtual replicas correspond to different types of spindle bear- ing assemblies suitable for the machining equipment 105. For example, the different types of spindle bearing assemblies may include angular contact ball bearing, deep groove ball bear- ing, self-aligning ball bearing etc. In an embodiment, selecting the at least one virtual replica from the plurality of virtual replicas stored in the digital 202301865 19 library, includes identifying metadata indicative of the one or more requirements for performing the virtual orchestration within the virtual layer, from the input data. For example, the metadata may be indicative of the type of virtual orches- tration to be performed. Further, the metadata may indicate that the spindle bearing assembly is an angular contact ball bearing and the type of virtual orchestration may correspond to determining an optimal configuration of the spindle bearing assembly. Further, a search logic is generated based on the identified metadata. For example, the search logic may be generated by embedding the metadata into a predefined search string. In an example, the search logic may be an updated search string. In another example, the search logic may be a sequence of selec- tions within different categories of virtual replicas. Fur- ther, a search is performed in the digital library comprising the plurality of virtual replicas, based on the generated search logic, for selecting the virtual replica. Upon perform- ing the search, a model file comprising model data associated with the selected virtual replica is retrieved. For example, the model file may be in formats such as XML, STL, OBJ, FBX, and DAE. In a preferred embodiment, the digital library may be stored on a cloud platform. Further, a reduced order model of the selected virtual replica may be deployed on the apparatus 110, upon selection based on the search logic, for enabling faster on-premises simulations. At step 415, a digital replica of at least the spindle bearing assembly suitable for the machining equipment 105 is config- ured within the virtual layer, by updating the selected virtual replica based on the input data. The digital replica is a 202301865 20 dynamic mathematical representation of at least the spindle bearing assembly corresponding to the selected virtual rep- lica. In an embodiment, updating the digital replica comprises updating the selected virtual replica based on the input data, (for e.g., simulated or real-time operational data) to gener- ate one or more simulation instances. In the present example, the operational data is provided as inputs to the integrated dynamic-thermal model (i.e., the digital replica) to generate the one or more simulation instances corresponding to the spindle bearing assembly. At step 420, one or more workflow parameters associated with the spindle bearing assembly are computed based on execution of at least one simulation using the configured digital rep- lica. In an embodiment, computing the one or more workflow parameters based on execution of the at least one simulation includes executing the one or more simulation instances of the configured digital replica in a simulation environment to gen- erate one or more simulation results indicative of the one or more workflow parameters. For example, referring to FIG 3B, vibration response and temperature response at one or more locations on the spindle bearing assembly may be virtually sensed by defining ‘virtual sensors’ at the respective loca- tions on the 3D motion simulation model of the spindle bearing assembly. The one or more locations where virtual sensors are defined may be spatial positions on the spindle bearing assem- bly where direct measurement of parameters using sensors is not possible. The outputs of such virtual sensors constitute the workflow parameters. In the case of the spindle bearing assembly, the workflow parameters may include, for example, parameters such as vibrations, stiffness, preload, contact forces, temperatures and stresses at one or more locations on the spindle bearing assembly. 202301865 21 At step 425, at least one action is performed based on the one or more workflow parameters to generate an outcome. The at least one action is determined based on the one or more re- quirements indicated by the input data. In an embodiment, the outcome of performing the at least one action is identification of the optimal configuration of the spindle bearing assembly. In the present embodiment, perform- ing the at least one action based on the one or more workflow parameters includes determining whether the one or more work- flow parameters meet a predetermined criterion. For example, the workflow parameter may be temperature at a location inside the housing of the spindle bearing assembly. Here, the prede- fined criterion may include a maximum allowable temperature for a given spindle bearing assembly. If the computed temper- ature (workflow parameter) is greater than the maximum allow- able temperature, it damages the spindle bearing assembly. Therefore, it may be necessary to choose a spindle bearing assembly that may withstand the computed temperature. Alter- natively, it may also be necessary to choose a spindle bearing assembly that generates less heat. In other words, it is nec- essary to identify configuration of the spindle bearing assem- bly most suited for the machining equipment 105. In an embod- iment, if the one or more workflow parameters fail to meet the predefined criterion, then the spindle bearing assembly, one or more other virtual replicas are selected from the digital library. Further, steps 415 to 425 are repeated each of the one or more other virtual replicas until the predefined cri- terion is met. In other words, multiple virtual replicas are used to compute the one or more workflow parameters based on the input data until the predefined criterion is met. Alternatively, if the one or more workflow parameters meet the predefined criterion, a configuration of the spindle bearing 202301865 22 assembly corresponding to the selected virtual replica is iden- tified from the selected virtual replica. For example, model data corresponding to the virtual replica may indicate the configuration as an angular contact ball bearing. The config- uration thus identified represents the optimal configuration of the spindle bearing assembly. In another embodiment, the one or more requirements may be associated with determining optimal operating conditions for the spindle bearing assembly. In this case, the outcome of performing the at least one action is an optimal operating condition for the spindle bearing assembly. The optimal oper- ating condition is associated with one of autonomous operation and manual operation of the machining equipment 105. The op- timal operating condition may be indicated by a set of opera- tional parameters and/or control parameters to be used for operation of the machining equipment 105. In this embodiment, performing the at least one action based on the one or more workflow parameters includes performing the at least one ac- tion based on the one or more workflow parameters comprises using an optimization algorithm to compute the optimal oper- ating condition for the spindle bearing assembly based on the one or more workflow parameters. FIG 5 illustrates an exemplary method of computing and maintaining optimal operating condi- tion for the spindle bearing assembly, in accordance with an embodiment of the present invention. In yet another embodiment, the outcome of performing the one or more actions include a control parameter for controlling an operation of the machining equipment 105. In the present embodiment, performing the at least one action includes com- puting the deviation between at least one of the workflow parameters and a corresponding measured parameter. In an ex- ample, the machining equipment 105 is in use and the measured 202301865 23 parameter is measured using the one or more sensing units 125. In another example, the measured parameter may be manually measured by a human operator. Further, the measured parameter may be provided to the apparatus 110 as part of the input data. In an embodiment, the measured parameter is an actual produc- tion time associated with the machining equipment 105 or an- other machining equipment similar to the machining equipment 105, and the workflow parameter is a production time predicted based on simulations performed using the digital replica of at least the spindle bearing assembly. In an example, the term ‘deviation’ may refer to a standard deviation between a mean value of the measured parameter and the workflow parameter. Further, if the computed deviation is greater than a predefined value, the control parameter is calculated based on the com- puted deviation. The calculated control parameter is adapted to reduce the deviation between the at least one workflow parameter and the measured parameter when applied to the ma- chining equipment 105. In an embodiment, the deviation is as- sociated with a production time of the machining equipment 105. More specifically, the deviation may be computed based on a machining time predicted based on the simulations performed at step 420, and a measured machining time. An example of a workflow for predicting the production time for a machining equipment is shown in FIG 6. At step 430, a secure wallet configured based on the outcome of performing the at least one action is executed. The secure wallet is a piece of code executable on the decentralized network for controlling a transaction between a consumer node and a manufacturer node, wherein the consumer node is a node associated with a consumer of the machining equipment 105, and wherein the manufacturer node is a node associated with a manufacturer of the machining equipment 105. The term ‘node’ as used herein refers to a processing device such as a computer 202301865 24 that participates in the decentralized network, configured to run a protocol software for validating transactions on the decentralized network. In an embodiment, executing the secure wallet includes, firstly, identifying a secure wallet template based the one or more requirements indicated by the input data. In an example, a first secure wallet template may be associated with the category of determining optimal configuration for the spindle bearing assembly. The corresponding secure wallet, when executed, may initiate a financial transaction between the manufacturing node and the consumer node for making a payment to the manufacturer for availing the optimal configu- ration of the spindle bearing assembly for the machining equip- ment 105. The payment may be based on for example, a pay-per- use basis thereby enabling the manufacturer to provide the machining equipment 105 as a service to the consumer. In another example, a second secure wallet template may be associated with the category of determining deviation between a workflow parameter and a measured parameter. Further, based on the nature of the parameter and a predetermined cause of the deviation, the secure wallet is executed to enable the manufacturer (or consumer) to penalize or reward the consumer (or manufacturer) for the deviation. In an embodiment, the cause of the deviation may be determined based on a predefined algorithm, for example, based on root cause analysis. For ex- ample, if the workflow parameter is availability, and if the actual availability of the machining equipment 105 when in use by the consumer is lesser than the predicted availability, the consumer may be penalized if the cause of low availability is attributed to poor use of the machining equipment by the con- sumer. Alternatively, if the cause of the low availability is attributed to a manufacturing defect, then the manufacturer may be penalized. 202301865 25 In an example, the decentralized network may be a private blockchain associated with a financial service provider that enables quantification of operational expenditure associated with the machining equipment 105 using the secure wallet exe- cuted based on the outcome of the at least one action. In an embodiment, the secure wallet may be updated by modifying codes in the secure wallet, based on the outcome of the at least one action performed. For example, the workflow parameters com- puted may be indicative of performance or availability asso- ciated with the machining equipment 105 predicted for a period of lease, say 20 years. In another embodiment, the method 400 further comprises gen- erating a notification indicating the outcome of performing the at least one action on the user interface 160. For example, the user interface 160 may display values of the workflow parameters in different formats including, but not limited to, text, graphics, augmented reality, virtual reality etc. FIG 5 shows a flowchart of an exemplary workflow 500 of com- puting and maintaining optimal operating conditions for a spin- dle bearing assembly associated with a machining equipment, in accordance with an embodiment of the present invention. At step 505, a digital replica 502 of the spindle bearing assembly is used to simulate a behavior of the spindle bearing assembly for a given set of input data. Based on the simula- tion, workflow parameters such as vibrations, temperature, stiffness, preload, contact forces and stresses associated with the spindle bearing assembly are predicted, for example, in the form of time-series data. 202301865 26 At step 510, the workflow parameters are preprocessed. The preprocessing may include conversion of the workflow parame- ters in time-series format to frequency spectrums. For exam- ple, feature extraction and feature selection may be performed over the workflow parameters in frequency-domain format. Fea- ture extraction may be performed using statistical analysis methods such as, but not limited to, mean, standard deviation, Root Mean Square, skewness, kurtosis, maximum, minimum, and crest factor. Feature selection may be performed using neural network-based techniques such as, but not limited to, modified distance discriminant technique, distance evaluation tech- nique, and discrete wavelet. It must be understood that feature selection is employed to preprocess the data to improve accu- racy of response prediction in step 515. Herein, the term ‘response’ may indicate at least one statistical parameter indicative of vibration, temperature or remaining useful life of one or more bearing assemblies of the spindle bearing as- sembly. At step 515, the response associated with the spindle bearing assembly is predicted based on the preprocessed data obtained at step 510 using an Artificial Intelligence-based technique such as Support Vector Machines. In an alternate embodiment, other techniques based on Fuzzy logic may also be used for predicting the response. In a preferred embodiment, an opti- mization algorithm based on combination of Artificial Intel- ligence techniques and fuzzy logic is used to predict the response with a desired level of accuracy. For example, vibra- tion data in frequency domain (i.e., vibration spectrum) may be processed using a neuro-fuzzy logic to predict the response of the spindle bearing assembly. Upon optimization, a set of workflow parameters associated with the spindle bearing assem- bly is generated. The workflow parameters include values of 202301865 27 vibration, temperature, preload, stiffness etc. that are in- dicative of a characteristic response of the spindle bearing assembly, at one or more locations or points of interest. At step 520, the workflow parameters are analyzed to identify an optimal operating condition of the spindle bearing assem- bly, using machine learning and artificial intelligence-based search methods based on, for example, gradient descent, ge- netic algorithm, particle swarm optimization etc. The optimal operating condition may include specific values of load, speed, preload etc. In an embodiment, the optimal operating condition is associated with an autonomous or unmanned operation of the machining equipment. In another embodiment, the optimal oper- ating condition is associated with manual operation of the machining equipment. At step 525, the optimal operating condition of the spindle bearing assembly is further provided as input to a controller associated with the machining equipment. The controller fur- ther computes control parameters corresponding to the optimal operating condition. The computed control parameters are fur- ther used to generate control commands for the machining equip- ment. Upon implementing the control commands, operating con- dition of the spindle bearing assembly is modified to match the optimal operating condition computed at step 530. At step 530, operational parameters of the machining equip- ment, upon implementing the control commands, are further used as feedback to reconfigure the digital replica 502. Further steps 505 to 530 are repeated to ensure that the spindle bear- ing assembly continues to operate under optimal operating con- ditions. 202301865 28 FIG 6 shows a flowchart of an exemplary method 600 for pre- dicting production time associated with a machining equipment, in accordance with an embodiment of the present invention. At step 605, a request for computing production time associated with a machining equipment is received. The request comprises input data indicative of requirements such as load, workpiece- related information, a type of spindle bearing assembly etc. At step 610, a virtual replica corresponding to a spindle bearing assembly of the machining equipment is selected from a digital library. The virtual replica may be an integrated dynamic-thermal model as explained earlier with reference to FIGS 3A & 3B. Further, the virtual replica is updated based on the input data to configure a digital replica of the spindle bearing assembly. At step 615, the digital replica is executed in a simulation environment used to execute a simulation, in a simulation en- vironment. Upon executing the simulation, simulation results indicative of changes in temperature, applied preload and stiffness relative to at least one location on the spindle bearing assembly are generated. Further, a machining time of the machining equipment is computed based on the simulation results, for example, using predetermined mathematical models. At step 620, the computed machining time is compared with a measured machining time corresponding to the machining equip- ment, to determine a deviation. The measured machining time represents an actual machining time measured by an operator when the same set of requirements are applied to the actual machining equipment. If the determined deviation is greater than a predefined value, the virtual replica is recalibrated based on the deviation, and steps 610 to 620 are repeated. The 202301865 29 recalibrated virtual replica may be further updated in the digital library. If the deviation is less than a predefined value, then step 625 is performed. At step 625, the computed machining time is used to calculate a production time of the machining equipment. Advantageously, the present invention enables manufacturer of machining equipment to provide the machining equipment-as-a- service to potential consumers, thereby eliminating the need for purchasing of the machining equipment by the consumer by paying a price upfront. More specifically, the present enables virtual orchestration of a machining equipment for predicting attributes (outcomes) associated with the machining equipment, and thereby enables quantification of an operational expendi- ture associated with the machining equipment with the help of the secure wallets. Further, the use of secure wallets enables automatic penalizing or rewarding of consumer or manufacturer based on performance of the machining equipment, depending on factors affecting such performance. The use of digital repli- cas helps in accurate modeling of a real-time condition of the spindle bearing assembly for performing the simulation, in place of simulation models that rely on idealistic conditions. Consequently, error associated with prediction of performance metrics associated with the spindle bearing assembly is mini- mized. Furthermore, the present invention enables consumers to perform virtual orchestration of a machining equipment, with- out requiring physical access to the machining equipment. More specifically, the present invention enables consumers to make quick decisions on selection of a machining equipment or one or more components thereof suitable for a particular applica- tion without the need for physical tests. In addition, the consumer may also perform virtual orchestration of the machin- ing equipment to predict operational behavior of the machining 202301865 30 equipment to determine conditions for optimal operation in any given scenario. The present invention is not limited to a particular computer system platform, processing unit, operating system, or net- work. One or more aspects of the present invention may be distributed among one or more computer systems, for example, servers configured to provide one or more services to one or more client computers, or to perform a complete task in a distributed system. For example, one or more aspects of the present invention may be performed on a client-server system that comprises components distributed among one or more server systems that perform multiple functions according to various embodiments. These components comprise, for example, executa- ble, intermediate, or interpreted code, which communicate over a network using a communication protocol. The present inven- tion is not limited to be executable on any particular system or group of system, and is not limited to any particular dis- tributed architecture, network, or communication protocol. While the invention has been illustrated and described in de- tail with the help of a preferred embodiment, the invention is not limited to the disclosed examples. Other variations can be deducted by those skilled in the art without leaving the scope of protection of the claimed invention.

Claims

202301865 31 Patent claims 1. A computer-implemented method comprising: a) receiving, by a processing unit (155), a request for performing virtual orchestration of a machining equip- ment (105), from a user interface (165), wherein the request comprises input data indicative of one or more requirements associated with the virtual operating of machinery equipment; b) selecting at least one virtual replica, from a plurality of virtual replicas stored in a digital library within a virtual layer, upon receiving the request, wherein each of the virtual replicas correspond to a static mathematical representation of a spindle bearing assem- bly suitable for the machining equipment (105); c) configuring a digital replica of at least the spindle bearing assembly suitable for the machining equipment (105) within the virtual layer by updating the selected virtual replica based on the input data, wherein the digital replica is a dynamic mathematical representation of at least the spindle bearing assembly corresponding to the selected virtual replica; d) computing one or more workflow parameters associated with the spindle bearing assembly based on execution of at least one simulation using the configured digital replica; e) performing at least one action based on the one or more workflow parameters to generate an outcome; and f) generating a notification indicative of the outcome of performing the at least one action on a user interface (165). 202301865 32 2. The method according to claim 1, further comprising: executing a secure wallet adapted based on the outcome of performing the at least one action, wherein the secure wallet is a piece of code executable on a decentralized network for controlling a transaction between a consumer node and a manufacturer node, wherein the consumer node is a node associated with a consumer of the machining equip- ment (105), and wherein the manufacturer node is a node associated with a manufacturer of the machining equipment (105). 3. The method according to claim 1, wherein selecting the at least one virtual replica from the plurality of virtual replicas stored in the digital library, comprises: identifying, from the input data, metadata indicative of the one or more requirements for performing the virtual orchestration within the virtual layer; generating a search logic based on the metadata iden- tified; and performing a search in the digital library comprising the plurality of virtual replicas, based on the generated search logic, for selecting the virtual replica. 4. The method according to claim 1, wherein the virtual replica comprises an integrated dynamic-thermal model, comprising at least a one-dimensional dynamic model operably coupled to a one-dimensional thermal model of the spindle bearing assembly. 5. The method according to claim 4, wherein the virtual rep- lica further comprises a motion simulation model operably coupled to the integrated dynamic-thermal model, wherein the at least one motion simulation model is configured for 202301865 33 dynamically computing contact forces in the spindle bearing assembly. 6. The method according to any of the preceding claims, wherein computing the one or more workflow parameters associated with the spindle bearing assembly based on execution of the at least one simulation using the configured digital rep- lica, comprises: executing one or more simulation instances of the configured digital replica in a simulation environment to generate one or more simulation results indicative of the one or more workflow parameters. 7. The method according to any of the preceding claims, wherein the outcome of performing the at least one action is an optimal configuration for the spindle bearing assembly, wherein performing the at least one action based on the one or more workflow parameters comprises: determining whether the one or more workflow parameters meet a predetermined criterion; if at least one of the workflow parameters fail to meet the predetermined criterion: selecting one or more other virtual replicas from the digital library; and repeating steps (c) through (e) of claim 1, for each of the one or more other virtual replicas until the one or more workflow parameters meet the predetermined cri- terion. 8. The method according to claims 1 and 7, further comprising: if the one or more workflow parameters meet the predeter- mined criterion: identifying a configuration of the spindle bearing assembly corresponding to the selected virtual replica, 202301865 34 wherein the identified configuration represents the op- timal configuration of the spindle bearing assembly. 9. The method according to any of the preceding claims, wherein the outcome of performing the at least one action is an optimal operating condition for the spindle bearing assem- bly, wherein the optimal operating condition is associated with one of autonomous operation and manual operation of the machining equipment (105), and wherein performing the at least one action based on the one or more workflow pa- rameters, comprises: using an optimization algorithm to compute the optimal operating condition for the spindle bearing assembly based on the one or more workflow parameters. 10. The method according to any of the preceding claims, wherein the outcome of performing the at least one action is deviation between at least one of the workflow parameters and a corresponding measured parameter, and wherein per- forming the at least one action based on the one or more workflow parameters, comprises: computing the deviation between the at least one of the workflow parameters and the corresponding measured parame- ter. 11. The method according to claims 1 and 10, wherein the outcome of performing the at least one action is a control parameter for controlling an operation of the machining equipment (105), and wherein performing the at least one action based on the one or more workflow parameters com- prises: if the deviation is greater than a predefined value: 202301865 35 calculating the control parameter based on the computed deviation, wherein the calculated control pa- rameter is adapted to reduce the deviation between the at least one workflow parameter and the measured param- eter when applied to the machining equipment (105). 12. The method according to claim 10 or 11, wherein the de- viation is associated with a production time of the machin- ing equipment (105). 13. The method according to claims 1 and 2, wherein executing the secure wallet adapted based on the outcome of performing the at least one action, further comprises: identifying a secure wallet template based on the one or more requirements received with the request; and configuring the secure wallet using the selected se- cure wallet template based on the outcome of performing the at least one action for virtual orchestration of the machining equipment (105). 14. An apparatus (110) comprising: one or more processing units (155); and a memory unit (170) communicatively coupled to the one or more processing units (155), wherein the memory unit (170) comprises a virtual orchestration module (175) stored in the form of machine-readable instructions exe- cutable by the one or more processing units (155), wherein the virtual orchestration module (175) is configured to perform method steps for performing virtual orchestration for managing and using the machining equipment (105), according to any of the claims 1 to 13. 202301865 36 15. A computer-program product having machine-readable in- structions stored therein, which when executed by one or more processing units, cause the processing units to per- form a method according to any of the claims 1 to 13.