WO2022090983A1

WO2022090983A1 - Methods and apparatus for cognitive robotic additive manufacturing system based on the directed energy deposition (ded) technology

Info

Publication number: WO2022090983A1
Application number: PCT/IB2021/059963
Authority: WO
Inventors: Trayana STOYKOVA TANKOVA; Joaquim Norberto CARDOSO PIRES DA SILVA; Amin S. AZAR
Original assignee: Universidade De Coimbra
Priority date: 2020-10-30
Filing date: 2021-10-28
Publication date: 2022-05-05

Abstract

The present disclosure describes a new method and platform for adaptive and controlled material deposition based on directed energy deposition (DED) technology that facilitates development of modern metallic components at a lower cost and waste rate, while increasing the quality, precision and user-friendliness. The platform transforms robotic Direct Energy Deposition (DED) into a user-friendly process, by integrating intelligent software mechanisms into hardware and sensorial systems, including non-destructive tests, to better plan the manufacturing process, predict and dynamically resolve problems on the go. The disclosure delineates a system that implements a data driven DED approach configured to dynamically plan trajectories, collecting real-time information from on-site sensors or from processed database and updated digital-twin simulations. All this behavior may be set by the user either manually or fully automatic, or in a combination of these two, with the goal to shorten the material and product development time.

Description

Methods and apparatus for cognitive robotic additive manufacturing system based on the directed energy deposition (DED) technology

Technical Field

The present disclosure describes a new method and platform for adaptive and controlled material deposition based on directed energy deposition (DED) technology that facilitates development of modern metallic components at a lower cost and waste rate, while increasing the quality, precision and user-friendliness.

Background art

Among the metal Additive Manufacturing (AM) technologies, some approaches have received immense attention such as Laser Powder Bed Fusion (L-PBF), Electron Beam Melting (E-PBF) as well as binder jetting and indirect metal printing. These technologies can produce parts with incontestable level of fine complexities.

However, when it comes to larger components with structural complexities, the former methods are inadequate due to their current slow and costly nature as well as machine size limitations.

This has brought about development of Directed Energy Deposition (DED) technologies such as Wire Arc Additive Manufacturing (WAAM), Laser Metal Deposition (LMD/L-DED) and Electron Beam Additive Manufacturing (EBAM).

Despite certain similarities, high deposition rates and larger local heat input create a new set of specific challenges. Current approaches in preparing the CAD model for DED are based on the simple slicing and generation of hard toolpath.

It is known from US20040133298A1 and US10406760B2 patents that there are systems for controlling the laser cladding process by adjusting the powder injection in real-time. However, the major attempts were towards controlling one parameter at a time to get the process right. Moreover, the classical software tools are developed based on the perception of Computer Numerical Control (CNC), originally developed for machining, where the heat input and temperature dependency of materials are not a concern. Only recently a few offline simulation programs claim to have enriched Computer-Aided Manufacturing (CAM) suits, where the path planning takes in consideration the thermal distortions.

The outcome of these software is still a hard toolpath for the processing machine as there is no option to translate the hardware activities to the software results and vice versa in real-time.

Hard toolpath means that whenever the process faces a glitch, severe distortion, cracking, damage, defect, etc., the process must continue the original toolpath after manual corrections, or another hard toolpath must be generated to compensate for the occurred challenge.

Such toolpaths are material dependent, which means that shifting from one alloy to another calls for long-term studies.

The currently existing approaches limit the impact of DED systems because:

1) Processing, co-processing and adoption of new materials require long and costly R&D activities for a single component that impedes the market growth and unjustifiable qualification period;

2) Large set of skills are required to meet the involved challenges at several fronts, one of which, validated numerical simulation of the entire process;

3) Discrete developments that are not based on interconnected data-driven and intelligent approaches, which result in recursive trial and error attempts.

AM research and global machine manufacturers community are expanding their efforts towards integration of AM technologies into the manufacturing industry. A considerable number of academic research and physics-based computer codes have been developed to cope with the challenges, using computer models to simulate AM phenomena at various scales.

However, many models do not acknowledge the effects of "actual" and "real-time" alloy chemistry and microstructure and "adaptive" process parameters since thermal, computational fluid dynamics, microstructure, solid mechanics have all been developed separately, but are not yet integrated to a ready-to- use framework for AM industry, especially for the DED technologies due to inefficient infrastructure and methods.

Also, there was no solution available that was able to connect product design, with materials databases and additivemanufacturing system.

Consequently, there was the need for a comprehensive software application (app), which links product design, materials modelling, materials databases and translated and representative agile models in the center of a cognitive (intelligent), low- cost and open access Directed Energy Deposition (DED) system. By performing agile corrections in the productive process on the robotic DED cell, present disclosure describes approaches as well as software solutions and hardware configurations to control the process parameters in real-time. This motivated the disclosed development of a sustainable, flexible, and smart app- driven materials processing unit that has an unprecedented level of automation and intelligence.

Summary

Present application describes an Intelligent Robotic Additive- Manufacturing System based on Direct Energy Deposition for adaptive and controlled material deposition of a product, comprising a CAD station, consisting of a 3D vectorial CAD package configured to perform advanced graphic design, path planning and model preparation for material deposition of a product; a modelling station, configured to perform finite-element process simulation of the deposition of a product, evaluating and verifying residual stresses, distortions and microstructures; a 3D printing station, configured to execute the robotic simulated product, comprising at least a laser metal deposition system; a wire arc additive manufacturing system; and a tool-changing system; and a production control station, configured to monitor and control the material deposition and execution of the product on the 3D printing station.

In a proposed embodiment of the system, the CAD station comprises a design-and-produce framework configured to output the design of a product, ensuring the fulfilment of project planning, standards and qualifications. Yet in another embodiment of the system, the finite-element simulation of the modelling station comprises initial process parameters definition, being adapted accordingly with sensor readings and autonomous design of experiment procedures.

Yet in another embodiment of the system, the initial process parameters definition further comprises material definition and characteristics, defined by the sensorial system or in the production control system.

Yet in another embodiment of the system, the production control station, prior to the material deposition of the product, further monitors and controls the slicing of the tasks to execute on the deposition, generates the production instructions, and performs system and production simulation.

Yet in another embodiment of the system, the laser metal deposition system comprises at least a robot-manipulator; a tool-changer; a building platform equipped with an induction heating apparatus for preheating the deposition substrate; a laser power-source and powder-feeder; and a cladding head.

Yet in another embodiment of the system, the wire arc additive manufacturing system comprises at least a robot-manipulator comprising a deposition tool; a power source and welding torch; a conveyor; a building platform equipped with an induction heating apparatus for preheating the deposition substrate; and a powder-feeder. Yet in another embodiment of the system, the tool-changing system comprise deposition-heads, deposition tools and 3D Camera for workpiece reconstruction and product production deviation detection.

Yet in another embodiment of the system, the powder-feeder comprises a at least two-alloy system container characterized by a tunable blending of materials composed at least by powder hopper 1 and powder hopper 2.

Yet in another embodiment of the system, the CAD station, modelling station, production control station and 3D printing station being connected to a local or remote data network, allowing the exchange of data and information between said stations.

Yet in another embodiment of the system, the robot manipulator is mounted on a conveyer and the production control station simultaneously controls the movements of each said robot manipulator independently, each conveyer or any setting in between.

Yet in another embodiment of the system, the CAD station, further comprises a system, that performs a set of fully automated experiments based on which, the process parameters are obtained for the given material, such as deposited bead geometry, heat dissipation and microstructure; and a designated software, that compiles the data in form of parameters and variables for automatic CAD slicing and toolpath planning.

Yet in another embodiment of the system, the production control station further comprises an virtual reality device, augmented reality device or mixed reality device that displays the toolpath in real-time, providing the capacity to the user for start / pause / stop, corrections, redefining the toolpath and controlling the process parameters, in real-time; and an artificial intelligence engine, providing automatic suggestions for the user based on the detected deviations from the nominal geometrical dimensions using the information from the modelling station (102) or other quantified values based on the sensorial system (109) such as, chemical composition, temperature and residual stresses.

It is also described the method of operating the system described where said system acts as interface between each step, translating the results of each step to be used in the next or previous steps, towards establishing the interoperability in the system tasks.

General Description

The present disclosure describes an adaptive and controlled metal deposition apparatus and a system that facilitates the development of new products at a lower cost and rejection rate, while increasing the quality and precision of the final product.

The aim is to transform the often-complicated Directed Energy Deposition (DED) into an accessible process, by integrating control mechanisms into hardware and sensorial systems, including non-destructive tests, to better plan the manufacturing process, predict and dynamically resolve the problems on the go.

This means creating a data driven DED system with the capacity of dynamic trajectory planning, taking information from realtime data provided by on-site sensors, or through previously developed and continuously enriching databases and updated simulations if needed.

This enables the users to include: a) Conventional Computer-Aided Design (CAD) packages for fast programming, simulation, and online control; b) Active and dynamic path planning (soft toolpath), process information and agile materials modelling; c) Capacity to detect defects and deviations and allow automatic and user-defined corrections; d) Provide the capacity, in a later stage, for utilization of augmented reality, for example using holographic projections, to effectively track production and make online adjustments to the process, including visualization of the digital-twin simulation results; e) Further develop the app based on artificial intelligence learning and reasoning that proposes the viable alternatives in the beginning of the procedure and to advise the best course of actions during the manufacturing process.

All this behavior may be set by the user, either manually or in a fully automatically configuration (depending on the user level), with the goal to shorten the material and product development time by a factor of 10, when compared with traditional/actual production systems.

The central objective of the disclosed intelligent robotic additive manufacturing setup for Directed Energy Deposition (IRAMS-DED) is the integration of Artificial Intelligence (Al) and cognitive features into robotic-based additive manufacturing systems, towards improving the overall process efficiency, contributing to the competitiveness of the industry and adoption of robots in increasingly intelligent and autonomous systems. The developed concept considers fusion of materials knowledge and modern robotic manuf cturing methods to achieve a breakthrough improvement in:

1) reducing energy consumption in material processing;

2) shortening the production value chain;

3) reducing waste rate;

4) improve the conditions for agile development of new materials and alloys;

5) improving quality; and

6) eliminating close supervision for long-term periods, large-scale and hard-to-manufacture metallic components.

These objectives are devised based on radically new ways of integrating robotic systems that are informed by the physics of the process in hand, allowing users to focus entirely on resolving the technical and scientific challenges, and preventing distraction by the usability and intricate settings of the particular processing system (metal processing system in this case).

The radical objective is to introduce a robust AM platform that starts on a CAD file at a push of a button (design-and-produce approach), which is capable of designing its own experiments and adjusting the parameters with the least amount of intervention.

It is not a question of better human-machine interfaces (HMI), but instead, the capacity to have a system that learns from experience, designs its own experiments and leads the user through the process, advising the best procedures, system parameters, operational settings and materials.

The current global challenges such as climate change, pandemics, complex supply chain etc., clearly shown that: a) Systems based on cognition, Al and high-level automation are required to perform the task with the least amount of supervision and intervention; b) Several industrial platforms are not prepared to be operated remotely or with reduced staff; c) The decision-making during the materials processing steps often ignores energy consumption and environmental f ctors; d) Local production capacity should be retrieved to enable generating modern and new products in a short period of time; e) long supply-chains are not responsive in short-terms.

Consequently, there is a need to accelerate widespread the use of robots. The disclosed invention has aligned itself with the following:

1. Adoption of a knowledge-based framework that is designed to allow robotic systems for the following: a) Automatic selection of parameters; b) Automatic definition of basic operational setup (related with the selected application scenario); c) Automatic selection of materials; and d) Operate at fully autonomous and cognitive level from the design phase;

2. Adoption of standard architectures to allow industrial systems to operate in parallel with simulation systems that could be used to anticipate the complete manufacturing process (e.g. digital twins) - this is important to fine-tune the operational parameters and setup towards improved efficiency.

3. Widespread the development of digital-twins, 100% equivalent to the real industrial system, to accelerate developments, reduce waste, reduce recursive trial-and-error cycles, and improve quality of final products.

4. Adding mixed-reality features to the robot-based industrial system, as a way to anticipate results, prevent errors and implement more efficient HMI, i.e., interfaces that are available everywhere and whose shape, displayed information and functionality is related to the specific phase of the manufacturing process.

5. Learning and cognition features: a) capacity to learn with experience, building a growing database; b) capacity to suggest experiments to quickly parameterize the AM process.

6. Capacity to command the system from several interface levels, considering that an advanced (high-level or experienced) user may want to tune parameters and other automatic procedures, but that a low level or less experienced user would like to focus on the design of the product/part in hand, relying on automatic selections to start production (similar to the low-end desktop plastic 3D-printers).

The developed and disclosed design-and-produce technology will impact industrial robotic-based AM systems by making them easy to operate. The disclosed approach will also: a) radically improve the development cycle (by effect of digital-twins, automatic selection and simulation environments); b) allow non-technical staff to operate complex industrial systems professionally (by effect of mixed-reality, advanced HMI and learning and cognitive features); and c) significantly improve efficiency since the user will be able to mainly focus on the process in hand, not being hassled by the complex details and knowledge necessary to have things done with the system.

In brief, the IRAMS-DED objectives are obtained through the effective integration of the following items: 1) Directed energy deposition (DED) technologies (additive manufacturing processes) to replace energy intensive processes;

2) Taking advantage of high-performance yet carbon- neutral materials feedstock, prepared through innovative low- energy and environmentally friendly processing routes;

3) Multi-sensorial and artificial intelligence (Al) driven platform for autonomous controllers that contributes to reduction of waste, costs, energy and carbon footprint;

4) Possibility to include simulations in real-time, along with the capacity to closely observe and control all aspects of the manufacturing process, which will significantly decrease the number of production cycles.

The proposed IRAMS-DED system addresses these issues by enabling the users to not just make decisions based on the economy and product, but also on the energy, carbon emission and performance. When developing new materials, it is fundamental to have an advanced production system that can be used to walk the user through the process from the CAD model to functional components. The challenges faced in the R&D and operational phase of producing those parts, require the capacity to simulate, observe the building process and be able to introduce the production changes in real-time fashion.

It is also very important to be able to act in several phases of the production of any part, namely, the CAD design station (where the user designs the part in 3D to be produced), the modelling station (where the user can simulate the printing process and model the materials including their properties), the production control station (where the user prepares the part to be printed and may perform process simulation and update with new parameters any ongoing production process) and the 3D system station (where the user actually grow the desired part and where complementary tasks may be commanded, along with local process monitoring and cell control).

The disclosed system is a digitalized platform, coupled with its digital twin. This means that the user may work with a 3D model of the system, away from the production premises, preparing everything, simulating and operating with the software as if the user was working with the real system, to expedite developments, share the production environment between several projects and tune the process prior to actual production. For this purpose, this project uses the world's most advanced software packages in a worked-out solution fully adapted to the objectives of a robotic DED system.

The developed system is also designed to consider development of complex components in terms of materials such as multi- and functionally graded materials that comprise of at least two types of alloys, which can only be processed using digital manufacturing technologies, or else, tremendous energy should be consumed. In these structures, chemical composition varies gradually across the component, thus determining different functional properties at the extremities of a given section.

These alloys are suitable for a wide range of applications, especially where high performance alloys are set to be combined with low-cost ones.

The AM system has various configurations (meaning physical implementations), depending on the particular DED technology used and even on the type of part being produced.

Brief description of the drawings

For better understanding of the present application, figures representing preferred embodiments are herein attached. However, the provided figures are not intended to limit the technique and setup design disclosed herein. Fig. 1 - represents one possible embodiment of the IRAMS-DED system (100) concept at a glance, illustrating several stations available in the robotic-based AM system. The reference numbers represent:

101 - CAD station;

102 - modeling station;

103 - production control station;

104 - 3D printing system station;

105 - end-user;

106 - conveyor;

107 - Robot manipulator;

108 - Welding / printing table / building platform equipped with induction heating apparatus for preheating the deposition substrate;

109 - sensors / sensorial system;

110 - robot controller;

111 - Cell PLC;

112 - network.

Fig. 2 - represents a two-alloy system container (200) responsible for deposition of multi-materials / Functionally Graded Materials (FGMs). The reference numbers represent:

201 - powder hopper 1;

202 - powder hopper 2;

203 - programmed blending of materials.

Fig. 3 - represents the 3D printing system station (104) of the IRAMS-DED (100) Additive-manufacturing setup, were:

301 - overall overview of the IRAMS-DED system;

302 - Laser Metal Deposition (LMD) system overview;

303 - Wire Arc Additive Manufacturing (WAAM) system overview; 304 - tool-changing system, shared by the LMD system and the deburring robot, which includes deposition-heads, deposition tools and 3D Camera for workpiece reconstruction.

Fig. 4 - represents the LMD system (302) in detail, showing its basic elements:

401 - Robot-manipulator;

402 - Tool-changer;

403 - Laser Power-Source and Powder-feeder;

404 - Cladding head used for LMD operation.

Fig. 5 - represents one possible configuration of the robotic WAAM system (303) in detail showing its basic elements:

107 - Robot manipulator;

501 - Power source and welding torch;

106 - linear track / conveyor;

502 - powder-feeder.

Fig. 6 - represents a possible and specific embodiment of the configuration of configuration of the robotic WAAM system (303) in detail showing its basic elements:

107 - Robot manipulator;

501 - Power source and welding torch;

502 - powder-feeder;

503 - printed product / material being deposited.

Fig. 7 - represents the communication architecture for the IRAMS- DED system (100), based on design-and-produce strategy, considering the several stations and features accordingly with the Figure 1 representation.

Fig. 8 - represents one aspect of the digital-twin Human- Robot/Machine Interface (HMI) developed for the control and management of the IRAMS-DED system (100).

Description of Embodiments

With reference to the figures, some embodiments are now described in more detail, which are however not intended to limit the scope of the present application.

The Intelligent Robotic Additive-Manufacturing System based on Direct Energy Deposition (IRAMS-DED) system (100) is a modern robotic manufacturing platform focused on replacing the currently dominating energy-intensive and untenable metal processing routes.

The developed Intelligent Robotic Additive-Manufacturing System based on Direct Energy Deposition (IRAMS-DED) system (100) is composed by four stations: the CAD station (101), the modelling station (102), the production control station (103) and 3D system station (104).

The CAD Station (101), is responsible for ensuring the product design, project and planning, and the standards and qualifications. CAD Station (101) is where the selected part for production is designed using any 3D CAD package. The proposed platform implements a "design-and-produce" automatic procedure, which it is important to start the process using a conventional CAD package where the user has experience and can master the design process. This is important, because the overall activities start with the STL 3D file (Standard Triangle Language) of the part to produce, which will then be sliced on the production control station (103) for Automatic Mass production. The production process, with the selected DED technology, will be also simulated to obtain the best initial production parameters, task that will be performed on the modelling station (102).

The modelling station (102) is responsible for the process simulation, materials modelling and materials properties. Modelling station (102) is where the system user selects and defines the materials to use, based on the final metallurgical properties and specification needed to obtain. In the modelling station (102) is also performed a previous process simulation before the deployment of the materials. This simulation, based on finite-element computations, execute a physical simulation of the process, enabling us to anticipate the metallurgical properties of the final part result based on the initial parameters for the AM system. Therefore, the platform is able to perform a physical simulation of the DED process, and optimize the robot toolpath accordingly, along with further optimization to guarantee that the proposed path is possible, ensuring that it is the best one for the planned part.

The production control (103) will monitor and control the slicing of the tasks, the code generation, system and production simulation, production monitoring and control. These steps involve the use of Al and mixed reality in order to promote and allow the user to better understand what is being executed in present production phases. This involves understanding data collected from sensors, predict next actions with the aid of mixed reality, get warnings and advices, etc.). The production control station (103) is mainly dedicated to perform the slicing of the overall tasks, code generation for the selected robotbased printing system, simulation of the robotic printing system (resorting to the use of a digital twin in the system - fully built and available in the platform, where the user can transparently select between the digital simulated version and the real implemented version using exactly the same software - this example is represented in figure 8), capacity to monitor and control the printing process (done both using the digital twin or the real system), using advanced Al-based software, mixed reality, etc.. The system was prepared to include more features with time, as mentioned on the data flow chart of Fig.7, allowing the introduction of further Al and learning techniques, and also the inclusion of mixed reality devices that can augment the user perception of what is going on in the actual production phase.

Finally, the 3D System (104) which is the actual printing system, is where the selected production part is printed and composed by the robot-based system, sensors, HMI operational software, etc. The 3D system (104) is also responsible for the cell control, deposition system, multi-sensorial systems, safety and monitoring. It may also have different configurations. In one of the proposed embodiments, as represented in Fig. 5, it includes a robot manipulator(107) mounted on a linear track (106), for WAAM (using CMT welding, a technology that better adapts to AM using welding); another robot (107), also mounted on another linear track (106), for 3D reconstruction (using a 3D camera) and correction tasks (using a subtractive tool), and a separated Robot (401) (represented in Fig.4) for laser metal deposition using metal powder.

The Human-Machine Interface (HMI) software represented in Fig. 8 is an aspect of the software used to control the first robot, responsible for implementing the WAAM (303). The HMI software was designed to allow the users the real time interaction with the system, based on real time sensor (109) information acquisition, both produced manually (high-level users) and/or automatically (low level users);

The process proceeds the End-User phase (105), where the printed part is tested and compared with parts produced by conventional technologies.

With regard to Fig.2, in a possible proposed embodiment, it is represented the real-time material blending system (200) to gradually adjust the supply (203) rate of each alloy (201 / 202) while executing the building/deployment process of a part is in progress.

In Fig. 3 it is represented some aspects of the IRAMS-DED system (100). In fact, it depicts the digital-twin of the real system, which is developed to operate exactly in the same way and using the same software interfaces that are used to operate with the real system. This setup was planned and designed to include several AM technologies, namely LMD (302), WAAM (303), and several mechanisms to simulate, control and observe the printing process.

The flow of information (data and command) between the several components of the IRAMS-DED system (100), along with the basic communication architecture designed to operate with the several sub-systems, generate actions, share definition files, handle specifications, etc., is represented in Fig. 7. This flow of information can either be executed in a local our remote telecom network (112).

Fig.8 shows one visual aspect of the digital-twin human-machine interface (HMI) software, for the case of the WAAM (303) robotic system. It shows the available controls, information being presented to the user and the level of input available to the user.

Claims

1. Intelligent Robotic Additive-Manufacturing System (100) based on Direct Energy Deposition for adaptive and controlled material deposition of a product (503), comprising a CAD station (101), consisting of a 3D vectorial CAD package configured to perform advanced graphic design, path planning and model preparation for material deposition of a product (503); a modelling station (102), configured to perform finite- element process simulation of the deposition of a product (503), evaluating and verifying residual stresses, distortions and microstructures; a 3D printing station (104), configured to execute the robotic simulated product (503), comprising at least a laser metal deposition system (302); a wire arc additive manufacturing system (303); and a tool-changing system (304); and a production control station (103), configured to monitor and control the material deposition and execution of the product (503) on the 3D printing station (104).

2. System according with claim 1, wherein the CAD station (101) comprises a design-and-produce framework configured to output the design of a product (503), ensuring the fulfilment of project planning, standards and qualifications.

3. System according with claim 1, wherein the finite-element simulation of the modelling station (102) comprises initial process parameters definition, being adapted accordingly with sensor (109) readings and autonomous design of experiment procedures.

4. System according with claim 3, wherein the initial process parameters definition further comprises material definition and characteristics, defined by the sensorial system (109) or in the production control system (103).

5. System according with claim 1, wherein the production control station (103) , prior to the material deposition of the product (503), further monitors and controls the slicing of the tasks to execute on the deposition, generates the production instructions, and performs system and production simulation.

6. System according with claim 1, wherein the laser metal deposition system (302) comprises at least a robot-manipulator (401); a tool-changer (402); a building platform equipped with induction heating apparatus for preheating the deposition substrate (108); a laser power-source and powder-feeder (403); and a cladding head (404).

7. System according with claim 1, wherein the wire arc additive manufacturing system (303) comprises at least a robot-manipulator (107) comprising a deposition tool; a power source and welding torch (501); a conveyor (106); a building platform equipped with induction heating apparatus for preheating the deposition substrate (108); and a powder-feeder (502).

8. System according with claim 1, wherein the tool-changing system (304) comprise deposition-heads, deposition tools and 3D Camera for workpiece reconstruction and product production deviation detection.

9. System according with claim 7, wherein the powder-feeder (502) comprises a at least two-alloy system container (200) characterized by a tunable blending of materials (203) composed at least by powder hopper 1 (201) and powder hopper 2 (202).

10. System according with claim 1, characterized by the CAD station (101), modelling station (102), production control station (103) and 3D printing station (104) being connected to a local or remote data network (112), allowing the exchange of data and information between said stations.

11. System according with claim 1, wherein the robot manipulator (107) is mounted on a conveyer (106) and the production control station (103) simultaneously controls the movements of each said robot manipulator independently, each conveyer (106) or any setting in between.

11. System according with claim 1, wherein the CAD station (101), further comprises a system, that performs a set of fully automated experiments based on which, the process parameters are obtained for the given material, such as deposited bead geometry, heat dissipation and microstructure; and a designated software, that compiles the data in form of parameters and variables for automatic CAD slicing and toolpath planning.

12. System according with claim 1, wherein the production control station (103) further comprises an virtual reality device, augmented reality device or mixed reality device that displays the toolpath in real-time, providing the capacity to the user for start / pause / stop, corrections, redefining the toolpath and controlling the process parameters, in real-time; and an artificial intelligence engine, providing automatic suggestions for the user based on the detected deviations from the nominal geometrical dimensions using the information from the modelling station (102) or other quantified values based on the sensorial system (109) such as, chemical composition, temperature and residual stresses.

13. Method of operating the system described in claims 1 to 11, wherein said system acts as interface between each step, translating the results of each step to be used in the next or previous steps, towards establishing the interoperability in the system tasks.