Systems and methods for advanced additive manufacturing

Abstract

A computer-based apparatus for dynamically creating or modifying at least a portion of an additive manufacturing build for manufacturing a part is provided. The apparatus communicates directly or indirectly with one or more additive manufacturing machines using one or more build parameters. The apparatus is configured to analyze a plurality of build information relating to the part, portions of the build information relating to pre-existing data relating to the part and portions of the build information relating to non-pre-existing data relating to the part. The apparatus is further configured to assess whether one or more differences between the pre-existing data and the non-pre-existing data may cause deviation or improvement of the part, the additive manufacturing build, or both; and automatically creating or modifying one or more build parameters of the part, at least a portion of the additive manufacturing build, or a combination thereof based on the evaluation of the one or more differences.

Description

Technical Field

[0001] The field of the present disclosure relates generally to additive manufacturing, and more particularly, to systems and methods for dynamically adapting additive manufacturing of a part, a build, or both. Background Art

[0002] Many additive manufacturing systems (also known as three-dimensional (3D) printers) generate three-dimensional objects through a layer-by-layer process. The system generates objects by placing successive layers of material based on computer control. At least some additive manufacturing systems include stacking powdered materials to form parts. This method can produce complex parts from expensive materials at reduced costs and improved manufacturing efficiency. At least some known additive manufacturing systems, such as direct metal laser melting (DMLM) systems, use a laser device and a powder material such as, but not limited to, powdered metal to manufacture parts. The laser device generates a laser beam that melts the powder material in and around the area where the laser beam is incident on the powder material, thereby generating a melt pool. In some known DMLM systems, part quality may be affected by excess heat and / or changes in heat transferred to the metal powder by the laser device within the melt pool.

[0003] In some known DMLM systems, part surface quality, particularly overhanging or downward-facing surfaces, is reduced due to changes in conductive heat transfer between the powdered metal and the surrounding solid material of the part. As a result, localized overheating may occur, particularly at the overhanging surfaces. The melt pool generated by the laser device may become too large, causing the molten metal to diffuse into the surrounding powdered metal and the melt pool to penetrate deeper into the powder bed, causing additional powder to be drawn into the melt pool. The increased melt pool size and depth and the flow of the molten metal may generally result in a poor surface finish on the overhanging or downward-facing surfaces.

[0004] Other problems may arise during the manufacturing process based on a variety of factors, including material variations and material application, which may render the object unusable. Summary of the invention

[0005] In one aspect, the present invention provides a computer-based device for dynamically creating or modifying at least a portion of an additive manufacturing build for manufacturing a part. The device includes at least one processor in communication with at least one memory device. The device communicates directly or indirectly with one or more additive manufacturing machines using one or more build parameters. The device is configured to analyze a plurality of build information related to the part. Part of the build information is related to pre-existing data about the part, and part of the build information is related to data about the part that is not pre-existing data. The device is also configured to evaluate whether one or more differences between the pre-existing data and the non-pre-existing data will result in a deviation of the part, the additive manufacturing build, or both, or an improvement of the part, the additive manufacturing build, or both; and based on the evaluation of the one or more differences, automatically create or modify one or more build parameters of the part, at least a portion of the additive manufacturing build, or a combination thereof.

[0006] In another aspect, the present invention provides a method for dynamically creating or modifying at least a portion of an additive manufacturing build for manufacturing a part. The method is implemented using a computer device. The computer device includes a processor in communication with a memory. The computer device communicates directly or indirectly with one or more additive manufacturing machines using one or more build parameters. The method includes analyzing, by the processor, a plurality of build information related to the part. Part of the build information is related to pre-existing data about the part, and part of the build information is related to data about the part that is not pre-existing data. The method also includes evaluating, by the processor, whether one or more differences between the pre-existing data and the non-pre-existing data will result in a deviation of the part, the additive manufacturing build, or both, or an improvement of the part, the additive manufacturing build, or both; and automatically creating or modifying one or more build parameters of the part, at least a portion of the additive manufacturing build, or a combination thereof based on the evaluation of the one or more differences.

[0007] In another aspect of an apparatus for dynamically creating or modifying at least a portion of an additive manufacturing build for manufacturing a part, wherein the apparatus may communicate directly or indirectly with one or more additive manufacturing machines using one or more build parameters, the apparatus may be configured to: analyze a plurality of build information about a part, wherein a portion of the build information relates to pre-existing data about the part, and wherein a portion of the build information relates to data about the part that is non-pre-existing data; evaluate whether one or more differences between the pre-existing data and the non-pre-existing data result in a deviation from or improvement to the part, the additive manufacturing build, or both; and automatically create or modify one or more build parameters of the part, at least a portion of the additive manufacturing build, or a combination thereof based on the evaluation of the one or more differences. The evaluation may include a virtual control loop, which may be iterative. In some embodiments, the non-pre-existing data may include measured data, such as data measured by an additive manufacturing machine during an additive manufacturing build of the part. The non-pre-existing data may be beneficial. In some embodiments, the pre-existing data may include measured data, such as data measured by an additive manufacturing machine during an additive manufacturing build of the part. The apparatus may be configured to create or modify one or more build parameters of a part, at least a portion of an additive manufacturing build, or a combination thereof while the part is being built by one or more additive manufacturing machines. The apparatus may also include a communication component for transmitting the created or modified one or more build parameters of a part, at least a portion of an additive manufacturing build, or a combination thereof to the one or more additive manufacturing machines.

[0008] Technical Solution 1. A computer-based device for dynamically creating or modifying at least a portion of an additive manufacturing build for manufacturing a part, the device comprising at least one processor in communication with at least one memory device, the device communicating directly or indirectly with one or more additive manufacturing machines using one or more build parameters, the device being configured to:

[0009] analyzing a plurality of build information associated with the part, wherein a portion of the build information is associated with pre-existing data regarding the part, and wherein a portion of the build information is associated with non-pre-existing data regarding the part;

[0010] evaluating whether one or more differences between the pre-existing data and the non-pre-existing data will result in a deviation or improvement in the part, the additive manufacturing build, or both; and

[0011] Based on the evaluation of the one or more differences, one or more build parameters of the part, at least a portion of the additive manufacturing build, or a combination thereof are automatically created or modified.

[0012] Technical Solution 2. An apparatus according to Technical Solution 1, wherein the evaluation includes a virtual control loop.

[0013] Technical Solution 3. An apparatus according to Technical Solution 2, wherein the virtual control loop is iterative.

[0014] Technical Solution 4. An apparatus according to Technical Solution 1, wherein the non-pre-existing data is measurement data.

[0015] Technical Solution 5. An apparatus according to Technical Solution 4, wherein the non-pre-existing data is measured during the additive manufacturing construction of the part by the additive manufacturing machine.

[0016] Technical Solution 6. The device according to Technical Solution 1, wherein the pre-existing data is measurement data.

[0017] Technical Solution 7. An apparatus according to Technical Solution 6, wherein the pre-existing data is measured during the additive manufacturing construction of the part by the additive manufacturing machine.

[0018] Technical Solution 8. An apparatus according to Technical Solution 1, wherein the non-pre-existing data is artificial.

[0019] Technical Solution 9. An apparatus according to Technical Solution 1, wherein the apparatus is configured to create or modify one or more build parameters of the part, at least a portion of the additive manufacturing build, or a combination thereof while the part is being built by the one or more additive manufacturing machines.

[0020] Technical Solution 10. The device according to Technical Solution 1 also includes a communication component, which is used to transmit one or more build parameters of the created or modified part, at least a part of the additive manufacturing build, or a combination thereof to the one or more additive manufacturing machines.

[0021] Technical solution 11. A method for dynamically creating or modifying at least a portion of an additive manufacturing build for manufacturing a part, the method being implemented using a computer device, the computer device comprising a processor in communication with a memory, the computer device communicating directly or indirectly with one or more additive manufacturing machines using one or more build parameters, the method comprising:

[0022] analyzing, by the processor, a plurality of build information associated with the part, wherein portions of the build information are associated with pre-existing data regarding the part, and wherein portions of the build information are associated with non-pre-existing data regarding the part;

[0023] evaluating, by the processor, whether one or more differences between the pre-existing data and the non-pre-existing data would result in a deviation or improvement in the part, the additive manufacturing build, or both; and

[0024] Based on the evaluation of the one or more differences, one or more build parameters of the part, at least a portion of the additive manufacturing build, or a combination thereof are automatically created or modified.

[0025] Technical Solution 12. A method according to Technical Solution 11, wherein the evaluation also includes executing a virtual control loop to repeatedly evaluate whether one or more differences between the pre-existing data and the non-pre-existing data will cause deviations in the part, the additive manufacturing build, or both, or improvements in the part, the additive manufacturing build, or both.

[0026] Technical Solution 13. The method according to Technical Solution 12 also includes iteratively executing the virtual control loop, wherein each iteration is based on one or more differences of the previous iteration.

[0027] Technical Solution 14. The method according to Technical Solution 11, wherein the non-pre-existing data is measurement data.

[0028] Technical Solution 15. The method according to Technical Solution 14 also includes measuring the non-pre-existing data during the additive manufacturing construction of the part by the additive manufacturing machine.

[0029] Technical Solution 16. A method according to Technical Solution 11, wherein the pre-existing data is measurement data.

[0030] Technical Solution 17. The method according to Technical Solution 16 also includes measuring the pre-existing data during the additive manufacturing construction of the part by the additive manufacturing machine.

[0031] Technical Solution 18. A method according to Technical Solution 11, wherein the non-pre-existing data is artificial data.

[0032] Technical Solution 19. The method according to Technical Solution 11 also includes creating or modifying one or more build parameters of the part, at least a portion of the additive manufacturing build, or a combination thereof while the part is being built by one or more additive manufacturing machines.

[0033] Technical Solution 20. The method according to Technical Solution 11 also includes transmitting one or more build parameters of the created or modified part, at least a portion of the additive manufacturing build, or a combination thereof to one or more additive manufacturing machines via a communication component. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] These and other features, aspects and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings, in which like reference numerals represent like parts throughout the drawings, wherein:

[0035] Figure 1 is a schematic diagram of an exemplary additive manufacturing system illustrated in the form of a direct metal laser melting (DMLM) system;

[0036] Figure 2 is used Figure 1 A flowchart of an exemplary process of manufacturing a part by an additive manufacturing system is shown;

[0037] Figure 3 is used Figure 1 A schematic diagram of an exemplary manufacturing system in which the DMLM system shown dynamically adapts to additive manufacturing of a part;

[0038] Figure 4 It is possible to Figure 3 A schematic diagram of an exemplary configuration of a client system for use with the manufacturing system shown;

[0039] Figure 5 It is possible to Figure 3 A schematic diagram of an exemplary configuration of a server system for use with the manufacturing system shown;

[0040] Figure 6 is used Figure 3 A schematic diagram of an exemplary feed-forward control system for dynamically adapting a manufacturing system for additive manufacturing of a part is shown;

[0041] Figure 7 is used Figure 3 A schematic diagram of another exemplary feed-forward control system in which the manufacturing system is shown dynamically adapting to additive manufacturing of a part;

[0042] Figure 8 is used Figure 6 A flow chart of an exemplary process for a feed-forward control system to dynamically adapt a build file for additive manufacturing of a part is shown;

[0043] Fig. 9 is used Figure 6 A flow chart of another exemplary process for dynamically adapting a build file for additive manufacturing of a part by a feedforward control system is shown;

[0044] Fig.10 is used Figure 7 A flow chart of an exemplary process for dynamically adapting a build file for additive manufacturing of a part by a feedforward control system as shown; and

[0045] Fig.11 is used Figure 7 A flow chart of another exemplary process for a feedforward control system to dynamically adapt a build file for additive manufacturing of a part is shown.

[0046] Fig.12 is a schematic diagram of another exemplary process for dynamically creating or adapting build parameters, builds, and / or build files for additive manufacturing of one or more parts.

[0047] Fig.13 is a schematic diagram of another exemplary process for dynamically creating or adapting build parameters, builds, and / or build files for additive manufacturing of one or more parts.

[0048] Fig.14 is a schematic diagram of another exemplary process for dynamically creating or adapting build parameters, builds, and / or build files for additive manufacturing of one or more parts.

[0049] Unless otherwise indicated, the drawings provided herein are intended to illustrate features of embodiments of the present invention. These features are believed to be applicable to a wide variety of systems including one or more embodiments of the present invention. Thus, the drawings are not intended to include all conventional features known to those skilled in the art required to practice the embodiments disclosed herein. DETAILED DESCRIPTION

[0050] In the following description and claims, reference will be made to several terms which shall be defined to have the following meanings.

[0051] The singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise.

[0052] "Optional" or "optionally" means that the subsequently described event or circumstance may or may not occur, and that the description includes instances where the event occurs and instances where it does not.

[0053] Approximate language, as used herein throughout the specification and claims, may be applied to modify any quantitative representation that may vary in a permissible manner without resulting in a change in the basic function to which it is related. Thus, values ​​modified by words such as "about," "approximately," and "substantially" are not limited to the exact values ​​specified. In at least some cases, approximate language may correspond to the precision of an instrument used to measure the value. Here and throughout the specification and claims, range limitations may be combinatorial and / or interchangeable; unless otherwise indicated by context or language, such ranges are determined and include all subranges contained therein.

[0054] As used herein, the terms "processor" and "computer" and related terms (e.g., "processing device," "computing device," and "controller") are not limited to those integrated circuits known in the art as computers, but broadly represent microcontrollers, microcomputers, programmable logic controllers (PLCs), application specific integrated circuits, and other programmable circuits, and these terms are used interchangeably herein. In the embodiments described herein, the memory may include, but is not limited to, computer-readable media such as random access memory (RAM) and computer-readable non-volatile media such as flash memory. Alternatively, a floppy disk, a compact disk read-only memory (CD-ROM), a magneto-optical disk (MOD), and / or a digital versatile disk (DVD) may also be used. Moreover, in the embodiments described herein, the additional input channels may be, but are not limited to, computer peripherals associated with an operator interface such as a mouse and a keyboard. Alternatively, other computer peripherals may also be used, which may include, for example, but are not limited to, a scanner. Furthermore, in an exemplary embodiment, the additional output channels may include, but are not limited to, an operator interface monitor.

[0055] Additionally, as used herein, the terms "software" and "firmware" are interchangeable and include any computer program stored in memory for execution by personal computers, workstations, clients, and servers.

[0056] As used herein, the term "non-transitory computer-readable medium" is intended to mean any tangible computer-based device implemented in any method or technology for short-term and long-term storage of information such as computer-readable instructions, data structures, program modules and submodules, or other data in any device. Therefore, the methods described herein may be encoded as executable instructions embodied in tangible, non-transitory, computer-readable media including, but not limited to, storage devices and / or memory devices. Such instructions, when executed by a processor, cause the processor to perform at least a portion of the methods described herein. In addition, as used herein, the term "non-transitory computer-readable medium" includes all tangible computer-readable media, including, but not limited to, non-transitory computer storage devices, including, but not limited to, volatile and non-volatile media and removable and non-removable media, such as firmware, physical and virtual storage devices, CD-ROMs, DVDs, and any other digital sources such as networks or the Internet, as well as digital means yet to be developed, with the sole exception of transitory propagation signals.

[0057] In addition, as used herein, the term "real time" refers to at least one of the time of occurrence of associated events, the time of measurement and collection of predetermined data, the time of processing data, and the time of system response to events and environment. In some of the embodiments described herein, these activities and events occur essentially instantaneously. In some embodiments, the term "real time" refers to a real-time control system. A real-time control system is a closed-loop control system in which a process has a tight time window for collecting data, processing the data, and updating the system. If the time window is lost, the stability of the system is potentially destroyed. The size of this time window is determined by the dynamic characteristics of the process under control, the delay time of the system, and the specific control algorithm used.

[0058] As used herein, the term geometry refers to any portion, feature, or characteristic of a part or build. Geometry can be in the following features: a single layer, a build of an individual part, a portion of a part, a scan line, a time series of builds, and geometric features of a part. In some embodiments, for example, a geometry can include a change in powder or material required for an additive manufacturing machine to manufacture the geometry.

[0059] The additive manufacturing system described herein provides a method for dynamically adapting the additive manufacturing of a part based on the performance and / or past performance of the part's build. The system and method can use pre-existing information and non-pre-existing data related to the part to be additively manufactured (including the build of the part). Specifically, one or more embodiments described herein include a computing device that is configured to access information about the part (including the build of the part) and generate modifications to (but not limited to) the build process, build parameters, and build files to improve or change the part and the build of the part. The computing device can use the following build information, such as but not limited to build parameters, geometry, sensor data, material data, system data, quality control or assurance data, and performance data of the part to update the build file, thereby improving the quality of the final part, or improving efficiency, improving or modifying other aspects of the part or build. The computing device can use one or more pre-existing data related to the part (including the build of the part), whether it is measured, modeled, or virtual data, to directly modify the part (including, for example, the starting shape of the part), the build file, or the build. The data provided and / or used can be feedforward, feedback, pre-build or post-build, modeled, and virtual.

[0060] Some embodiments of the computing device divide the build file into several geometric shapes, wherein a build file may include multiple copies of the same geometric shape, and each copy of the same geometric shape is updated based on the previous build of the geometric shape. In some embodiments, the computer device updates the build file in real time while the part is being built. In some other embodiments, the computer device directly updates or modifies the part, build file and / or build before, during or after the part is completed. The system and method described herein partially allow to reduce the number of times a part must be built to obtain an acceptable or ideal part. For any purpose that is beneficial, the system and method allow a part, build or build file to be updated or modified before, during or after the start or end of the build. The computing device can be, for example, the additive manufacturing system itself or another computing device or removable or replaceable digital storage medium that can communicate locally or remotely with the additive manufacturing system.

[0061] Figure 1 is a schematic diagram of an exemplary additive manufacturing system 10 illustrated in the form of a direct metal laser melting (DMLM) system. Although the embodiments herein are described with reference to a DMLM system, the present disclosure is also applicable to other types of additive manufacturing systems and methods, including, for example, but not limited to, slot photopolymerization, powder bed fusion, binder jetting, material jetting, sheet lamination, material extrusion, direct energy deposition, and hybrid systems. These systems and methods may include, for example, but not limited to, stereolithography; digital light processing; scanning, spin and selective photocuring, continuous liquid interface generation; selective laser sintering; direct metal laser sintering; selective laser melting; electron beam melting; selective thermal sintering; multi-nozzle fusion; smooth curvature printing; multi-nozzle modeling; layered object manufacturing; selective deposition lamination; ultrasonic additive manufacturing; bonded wire manufacturing; fused deposition modeling; laser metal deposition; laser near-shape manufacturing; direct metal deposition; hybrid systems; and combinations of these methods and systems. These methods and systems may use, for example, but not limited to, all forms of electromagnetic radiation, heating, sintering, melting, curing, bonding, consolidation, pressing, embedding, and combinations thereof.

[0062] Materials used in these methods and systems include, for example, but not limited to, polymers, plastics, metals, ceramics, sand, glass, wax, fibers, biomass, composites, and mixtures of these materials. These materials can be used in these methods and systems in various forms suitable for the particular material and method or system, including, for example, but not limited to, liquid, solid, powder, plate, foil, ribbon, filament, pellet, liquid, slurry, wire, atomized, paste, and combinations of these forms.

[0063] In the exemplary embodiment, system 10 includes a build platform 12, a laser, a melting or heating device 14 configured to generate one or more laser or electron beams or energies 16, one or more scanning devices 18 configured to selectively direct or move beams or energies 16 through build platform 12, and an optical or other monitoring system 20 for monitoring the melt pool. Exemplary system 10 further includes a computing device 24 and a controller 26 configured to control one or more components of system 10, as described in greater detail herein.

[0064] Build platform 12 includes powdered raw materials that are melted and resolidified during the additive manufacturing process to build solid parts 28. Build platform 12 includes materials suitable for forming these parts, including but not limited to gas atomized alloys of cobalt, iron, aluminum, titanium, nickel, and combinations thereof. In other embodiments, build platform 12 may include any suitable type of powdered metal material. In yet other embodiments, build platform 12 includes any suitable build material and form that enables system 10 to function.

[0065] Device 14 is configured to generate an energy source 16 of sufficient energy to at least partially heat or melt the building material of build platform 12. In this exemplary embodiment, laser device 14 is a yttrium-based solid-state laser configured to emit a laser beam having a wavelength of about 1070 nanometers (nm). In other embodiments, device 14 includes any suitable type of laser or laser fiber connected to an energy source such as a laser diode, which enables system 10 to function as, for example, a carbon dioxide (CO2) laser. In addition, although system 10 is shown and described as including a single device 14, system 10 may include more than one device or array with varying and / or selectable energy levels. System 10 may include any combination of devices that enable system 10 to function.

[0066] like Figure 1 As shown, in one exemplary embodiment, device 14 is optically coupled to optical elements 30 and 32 that facilitate focusing laser beam 16 onto building platform 12. In the exemplary embodiment, optical elements 30 and 32 include a beam collimator 30 disposed between laser device 14 and first scanning device 18, and an F-theta lens 32 disposed between first scanning device 18 and building platform 12. System 10 may include any suitable type and arrangement of optical elements that provide collimated and / or focused energy onto building platform 12.

[0067] First scanning device 18 is configured to direct energy source 16 through a selective portion of build platform 12 to form part 28. In the exemplary embodiment, first scanning device 18 is a galvanometer scanning device that includes a mirror 34 operably coupled to a galvanometer-controlled motor 36 (generally, an actuator). Motor 36 is configured to move (specifically, rotate) mirror 34 in response to a signal received from controller 26 and thereby deflect laser beam 16 through a selective portion of build platform 12. Mirror 34 includes any suitable configuration that enables mirror 34 to deflect energy source 16 toward build platform 12. In some embodiments, mirror 34 includes a reflective coating having a reflectance spectrum corresponding to the wavelength of laser beam 16.

[0068] Although the first scanning device 18 is illustrated as having a mirror 34 and a motor 36, the first scanning device 18 may include any suitable number and type of mirrors or directional devices, stages, and motors that enable one or more scanning devices 18 to operate and move. For example, in one embodiment, the first scanning device 18 includes two mirrors and two galvanometer-controlled motors, each motor operably coupled to one of the mirrors. In yet other embodiments, the first scanning device 18 includes any suitable scanning device that enables the additive manufacturing system 10 to operate as described herein, such as a two-dimensional (2D) scanning galvanometer, a three-dimensional (3D) scanning galvanometer, and a dynamic focus galvanometer.

[0069] Optical system 20 is configured to detect electromagnetic radiation generated by melt pool 22 and transmit information about melt pool 22 to computing device 24. In the exemplary embodiment, optical system 20 includes a photodetector 38 configured to detect electromagnetic radiation 40 (also referred to as "EM radiation") generated by melt pool 22 and a second scanning device 42 configured to direct electromagnetic radiation 40 generated by melt pool 22 to photodetector 38. Second scanning device 42 is separate from first scanning device 18 and is dedicated to directing EM radiation 40 generated by melt pool 22 to photodetector 38, rather than directing laser beam 16 toward build platform 12. Likewise, second scanning device 42 is also referred to herein as a "dedicated" scanning device. In the exemplary embodiment, first scanning device 18 may also be referred to as a dedicated scanning device because it is dedicated to scanning laser beam 16 across build platform 12 and is not used to direct EM radiation 40 generated by melt pool 22. In other embodiments, the first scanning device 18 may also be used to direct the EM radiation 40 generated by the melt pool 22 and may not be a dedicated scanning device. Under normal operation, the optical elements within the optical system 20 do not experience thermal lensing because the EM radiation transmitted through the optical system 20 has a relatively low power.

[0070] The photodetector 38 is configured to detect EM radiation 40 generated by the melt pool 22. More specifically, the photodetector 38 is configured to receive the EM radiation 40 generated by the melt pool 22 and, in response to receiving the EM radiation, generate an electrical signal 44. The photodetector 38 is communicatively coupled to the computing device 24 and is configured to transmit the electrical signal 44 to the computing device 24.

[0071] The photodetector 38 includes any suitable photodetector that enables the optical system 20 to operate as described herein, including, for example, but not limited to, a photomultiplier tube, a photodiode, an infrared camera, a charge coupled device (CCD) camera, a CMOS camera, a pyrometer, or a high-speed visible light camera. Although the optical system 20 is shown and described as including a single photodetector 38, the optical system 20 includes any suitable number and type of photodetectors that enable the system 10 to operate as described herein. In one embodiment, for example, the optical system 20 includes a first photodetector configured to detect EM radiation within the infrared spectrum and a second photodetector configured to detect EM radiation within the visible light spectrum. In embodiments that include more than one photodetector, the optical system 20 includes a beam splitter (not shown) that is configured to separate and deflect the EM radiation 40 from the melt pool 22 to the corresponding photodetector.

[0072] Although the optical system 20 is described as including an “optical” detector for the EM radiation 40 generated by the melt pool 22, it should be noted that the use of the term “optical” is not equivalent to the term “visible”. Rather, the optical system 20 is configured to capture a wide spectral range of EM radiation and will depend on the additive manufacturing method or system used. For example, the first light detector 38 is sensitive to light having wavelengths within the following spectrums: ultraviolet spectrum (approximately 200 to 400 nm), visible spectrum (approximately 400 to 700 nm), near infrared spectrum (approximately 700 to 1,200 nm), and infrared spectrum (approximately 1,200 to 10,000 nm). In addition, because the type of EM radiation 40 emitted by the melt pool 22 depends on the temperature of the melt pool 22, the optical system 20 is able to monitor and measure the size and temperature of the melt pool 22.

[0073] The second scanning device 42 is configured to direct the EM radiation 40 generated by the melt pool 22 to the first light detector 38. In the exemplary embodiment, the second scanning device 42 is a galvanometer scanning device that includes a first reflector 46 operably coupled to a first galvanometer-controlled motor 48 (generally, an actuator), and a second reflector 50 operably coupled to a second galvanometer-controlled motor 52 (generally, an actuator). The first motor 48 and the second motor 52 are configured to move (specifically, rotate) the first reflector 46 and the second reflector 50, respectively, in response to a signal received from the controller 26 to deflect the EM radiation 40 from the melt pool 22 to the first light detector 38. The first reflector 46 and the second reflector 50 have any suitable configuration that enables the first reflector 46 and the second reflector 50 to deflect the EM radiation 40 generated by the melt pool 22. In some embodiments, one or both of the first reflector 46 and the second reflector 50 include a reflective coating having a reflective spectrum corresponding to the EM radiation that the first light detector 38 is configured to detect.

[0074] Although second scanning device 42 is illustrated and described as having two mirrors and two motors, second scanning device 42 includes any suitable number of mirrors and motors that enables optical system 20 to operate as described herein. In addition, second scanning device 42 includes any suitable scanning device that enables optical system 20 to operate as described herein, such as a two-dimensional (2D) scanning galvanometer, a three-dimensional (3D) scanning galvanometer, and a dynamic focus galvanometer.

[0075] The computing device 24 comprises a computer system including at least one processor ( Figure 1 2 ). The computing device 24 includes, for example, a calibration model of the system 10 and an electronic computer build file associated with a component such as the part 28. The calibration model includes, but is not limited to, expected or desired melt pool dimensions and temperatures under a given set of operating conditions of the system 10 (e.g., the power of the laser device 14). In an exemplary embodiment, the melt pool dimensions include one or more dimensions of the melt pool, such as, but not limited to, length, width, depth, area, and volume. In an exemplary embodiment, the melt pool temperature distribution represents the temperature of the melt pool at a specific point in the melt pool, such as the center. In other embodiments, the melt pool temperature distribution represents measured temperatures from a sample of the melt pool or from a function of a 2D / 3D temperature distribution curve.

[0076] The build file includes build parameters that are used to control one or more components of one or more systems (such as but not limited to system 10) or otherwise build a part. The build parameters will depend on the additive manufacturing method or system used and the materials from which the part is constructed. The build parameters may include one or more of the following: (without limitation) power, speed, orientation, location of energy sources, galvanometers, mirrors, scanners, sensors, detectors, conveyors, build plates, and material applicators and removers. The build parameters may also include one or more of the following: (without limitation) materials used by the system to perform the method, such as gases, gas pressures, and gas flows; melt pool size and melt pool temperature distribution; materials that make up the part itself and intermediate part materials; speeds and methods of applying materials during the build; and the starting shape of the part and the intermediate build shapes.

[0077] In an instance of using a DMLM method or system, the build parameters may include one or more of the following: (without limitation) the power of the laser or energy device 14, the scanning speed of the first scanning device 18 (also referred to as the galvanometer speed, the mirror speed and / or the scanning speed of the laser device 14), the position and orientation of the first scanning device 18 (specifically the mirror 34), the scanning speed of the second scanning device 42, the position and orientation of the second scanning device 42 (specifically the first mirror 46 and the second mirror 50), the desired melt pool size, the desired melt pool temperature distribution, gas, gas pressure and gas flow, metal powder and other materials that make up the part itself, intermediate part materials, the speed and method of applying powder and other materials, and the starting and intermediate build shapes of the part.

[0078] In an exemplary embodiment, computing device 24 and controller 26 are shown as separate devices. In other embodiments, computing device 24 and controller 26 are combined into a single device that acts as both computing device 24 and controller 26, as described herein. In other embodiments, the model includes details of the process of manufacturing part 28. In some examples, build parameters are stored separately from multiple geometric shapes (e.g., a CAD file describing a part to be built). In these examples, the build file includes multiple build parameters stored in a memory of computing device 24 or controller 26 and multiple geometric shapes stored separately. In these examples, system 10 combines the build parameters with the multiple geometric shapes when the build is taking place.

[0079] In the exemplary embodiment, computing device 24 is also configured to operate, at least in part, as a data acquisition device and monitor the operation of system 10 during the manufacture of part 28. In one embodiment, for example, computing device 24 receives and processes electrical signals 44 from first light detector 38. Computing device 24 may store information related to melt pool 22 based on electrical signals 44 for use in facilitating control and refinement of system 10 or a build process of a particular part built by system 10.

[0080] In addition, in this example, the computing device 24 is configured to adjust one or more building parameters in real time based on the electrical signal 44 received from the first light detector 38. For example, when the system 10 builds the part 28, the computing device 24 processes the electrical signal 44 from the first light detector 38 using a data processing algorithm to determine the size and temperature of the melt pool 22. The computing device 24 compares the size and temperature of the melt pool 22 with the expected or desired melt pool size and temperature based on the calibration model. The computing device 24 generates a control signal 60, which is fed back to the controller 26 and is used to adjust one or more building parameters in real time to correct for the difference in the melt pool 22. For example, when the computing device 24 detects a difference in the melt pool 22, the computing device 24 and / or the controller 26 adjusts the power of the laser device 14 during the building process to correct for the difference.

[0081] Controller 26 includes any suitable type of controller that enables system 10 to operate as described herein. For example, in one embodiment, controller 26 is a computer system that includes at least one processor and at least one memory device that executes executable instructions to control the operation of system 10 based at least in part on instructions from a human operator. Controller 26 includes, for example, a 3D model of a part 28 to be constructed by system 10. The executable instructions executed by controller 26 include controlling the power output of laser device 14, controlling the position and scanning speed of first scanning device 18, and controlling the position and scanning speed of second scanning device 42.

[0082] Controller 26 is configured to control one or more components of system 10 based on build parameters associated with a build file stored, for example, within computing device 24. In the exemplary embodiment, controller 26 is configured to control first scanning device 18 based on a build file associated with a part to be constructed using system 10. More specifically, controller 26 is configured to control the position, movement, and scanning speed of mirror 34 using motor 36 based on a predetermined path defined by a build file associated with part 28.

[0083] In the exemplary embodiment, the controller 26 is also configured to control the second scanning device 42 to direct the EM radiation 40 from the melt pool 22 to the first light detector 38. The controller 26 is configured to control the position, movement, and scanning speed of the first mirror 46 and the second mirror 50 based on at least one of the position of the mirror 34 of the first scanning device 18 and the position of the melt pool 22. In one embodiment, the position of the mirror 34 at a particular time during the build process is determined using, for example, the computing device 24 and / or the controller 26 based on a determined path of a build file that is used to control the position of the mirror 34. The controller 26 controls the position, movement, and scanning speed of the first mirror 46 and the second mirror 50 based on the determined position of the mirror 34. In another embodiment, the first scanning device 18 is configured to communicate the position of the mirror 34 to the controller 26 and / or the computing device 24, for example by outputting a position signal corresponding to the position of the mirror 34 to the controller 26 and / or the computing device 24. In yet another embodiment, controller 26 controls the position, movement, and scanning speed of first mirror 46 and second mirror 50 based on the position of melt pool 22. For example, based on the position of mirror 34, the orientation of melt pool 22 at a particular time during the build process is determined.

[0084] Controller 26 is also configured to control other components of system 10, including, but not limited to, laser device 14. For example, in one embodiment, controller 26 controls the power output of laser device 14 based on build parameters associated with a build file.

[0085] Figure 2 is used Figure 1 A flow chart of an exemplary process 200 for manufacturing a part 28 using the additive manufacturing system 10 is shown. In the exemplary embodiment, the process 200 is divided into two parts: a setup process 202 and a manufacturing process 204.

[0086] In the setup process 202, a computer-aided design (CAD) file 206 includes a design of a part 28 to be manufactured. In the exemplary embodiment, the CAD file 206 is provided to a computer device, such as Figure 3. The pre-processing computer device 304 shown in . The pre-processing computer device 304 includes a scan path generator 208. The scan path generator 208 is configured to analyze the CAD file and determine how to use the additive manufacturing system 10 to manufacture the part 28. In the exemplary embodiment, the scan path generator 208 determines the material layers that comprise the part 28 and determines the path that the controller 26 will instruct the laser beam 16 to follow. The scan path generator 208 also determines the order of operations and movements that the additive manufacturing system 10 will perform during the manufacturing process. The scan path generator 208 generates a build file 210 based on the CAD file 206. In the exemplary embodiment, the build file 210 is configured for the type and / or model of the additive manufacturing system 10 to be used. In some further embodiments, the build file 210 is configured for a specific machine that is to build the part 28. In the exemplary embodiment, the scan path generator 208 cuts the 3D image of the part into slices or layers. The scan path generator 208 generates one or more laser devices 14 ( Figure 1 The scan path generator 208 calculates one or more parameters for each point along the generated path.

[0087] The build file 210 includes build parameters for controlling one or more components of the system 10. The build parameters include, but are not limited to, the power of the laser device 14, the scanning speed of the first scanning device 18, the position and orientation of the first scanning device 18 (specifically, the reflector 34), the scanning speed of the second scanning device 42, the second scanning device 42 (specifically, the first reflector 46 and the second reflector 50) (all in Figure 1 ), the desired melt pool size and the desired melt pool temperature distribution.

[0088] During the manufacturing process 204, the build file 210 is loaded into the computing device 24 and / or the controller 26 ( Figure 1 ), computing device 24 and / or controller 26 control the operation of system 10. System 10 uses build file 210 to build 212 part 28. While system 10 is building, one or more sensors 216, such as optical system 20 ( Figure 1214 ) monitors part 28 for feedback control 218 of part 28. As described above, sensor 216 monitors the build 212 of part 28 in real time and transmits the results to computing device 24. Computing device 24 uses feedback control information to determine whether to change any current parameters to correct potential problems. As described above, computing device 24 transmits any changes in parameters to controller 26. In some embodiments, computing device 24 uses feedback control information to determine that build 212 should be stopped due to problems discovered by analyzing feedback control data. In some other embodiments, computing device 24 uses feedback control information to determine whether to inspect 214 part 28. For example, computing device 24 may determine that too many errors occurred during the manufacture 212 of part 28, the time spent on inspection 214 should be bypassed, and the part should be discarded.

[0089] The manufacturing process 204 also includes a post-build inspection 214, in which the finished part 28 is analyzed for quality purposes. Such an inspection 214 may include data from sensors 216, computerized tomography (CT) scans, computerized axial tomography (CAT) scans, ultrasonic imaging scans, visual inspections, and / or any other non-destructive scans or analyses of the part 28 to determine the quality and suitability of the part 28 for use. In other embodiments, the inspection 214 may include destructive testing, in which a portion of the part 28 is removed, polished, and analyzed for porosity and other metallurgical properties.

[0090] In an exemplary embodiment, build file 210 includes a plurality of geometries. In some embodiments, the geometries are defined by build file 210. In other embodiments, the geometries are defined by a user. In an exemplary embodiment, different geometries have different heat transfer characteristics and require different levels of laser power to complete based on their surroundings. In some geometries, the previous layer is solid metal that has been lasered. In these geometries, when the powder is lasered, the heat is quickly conducted away by the solid metal. Therefore, when the heat is conducted away, the size and temperature of the melt pool 22 are affected. In some other geometries, there is powder below the point where the laser is applied, for example, in the case of an arch. In these places, the heat is not conducted away as quickly, so it takes less laser power to make the melt pool 22 reach the same size and temperature compared to the area where the heat is conducted away. The amount of solid metal below the point where the laser is applied and the shape of the solid metal may also affect the thermal conductivity of the part 28.

[0091] In the exemplary embodiment, build file 210 includes melt pool size as a build parameter. Computing device 24 adjusts laser power and speed to achieve the desired melt pool size. In the exemplary embodiment, computing device 24 receives sensor information including melt pool size and adjusts laser settings to achieve the desired melt pool size.

[0092] Figure 3 Is to use DMLM system 10( Figure 1 ) is a schematic diagram of an exemplary manufacturing system 300 that dynamically adapts to additive manufacturing of a part. In the exemplary embodiment, the manufacturing system 300 is used to build the part 28 ( Figure 1 ), monitors the build of part 28, and updates the build file 210 associated with part 28 based on the build ( Figure 2 ). The manufacturing system 300 includes a manufacturing control (“MC”) computing device 302 configured to dynamically update the build file 210 of the part 28. The manufacturing system 300 includes a DMLM computing device 306 configured to dynamically update the build file 210 of the part 28 during the manufacturing of the part 28.

[0093] As described in more detail below, the MC computing device 302 is configured to store a model of the part 28 including a plurality of build parameters, receive a melt pool 22 ( Figure 1 ), determining one or more properties of the melt pool 22 based on the current sensor information, calculating at least one invisible property of the melt pool 22, determining an adjusted build parameter based on the at least one invisible property, the one or more properties, and a plurality of build parameters, and transmitting the adjusted build parameter to a machine currently manufacturing a part, for example, the system 10.

[0094] In some embodiments, the MC computer device 302 is also configured to store a build file 210 for building a part 28, the build file 210 including a plurality of geometric shapes, each geometric shape including one or more values ​​of a first build parameter, receive sensor information for building the part 28 by the system 10 using the build file 210, compare the sensor information for each of the plurality of geometric shapes with the one or more values ​​of the first build parameter to determine one or more differences, determine one or more values ​​of a second build parameter for each geometric shape based on the one or more differences, generate an updated build file 210 for the part 28, the build file 210 including one or more values ​​of the second build parameter, and transmit the updated build file 210 to the system 10.

[0095] In some additional embodiments, the MC computer device 302 is further configured to store a build file 210 for building a part 28, the build file 210 including a plurality of geometries, each of the geometries including one or more build parameters, receive sensor information from a first geometry being built during construction of the part 28, determine one or more adjustments to the one or more build parameters of the first geometry based on the sensor information, identify one or more subsequent geometries to be built that are similar to the first geometry of the plurality of geometries, adjust one or more build parameters of the one or more subsequent geometries in the build file 210 based on the one or more adjustments, and transmit the adjusted build file 210 to the system 10 that is currently manufacturing the part 28.

[0096] In still other embodiments, the MC computer device 302 is configured to store a build file 210 (including one or more build parameters) for building a part 28 and receive a plurality of build messages. Each of the plurality of build messages includes sensor information of one of the plurality of machines 10 building the part 28 using the build file 210. The MC computer device 302 is further configured to compare the plurality of sensor information with the one or more build parameters to determine one or more differences, determine adjustments to the one or more build parameters based on the one or more differences, generate an updated build file 210 based on the one or more adjustments, and transmit the updated build file 210 to at least one system 10 of the plurality of systems 10 for manufacturing.

[0097] In the exemplary embodiment, the pre-processing computer device 304 is a computer or computer device configured to process the CAD file 206 ( Figure 2 200). In an exemplary embodiment, the pre-processing computer device 304 includes a scan path generator 208. In an exemplary embodiment, the pre-processing computer device 304 communicates with the MC computer device 302 and the DMLM computer device 306. In some embodiments, the pre-processing computer device 304 communicates with a user computer device (not shown). In some embodiments, the pre-processing computer device 304 is communicatively connected to other computer devices through various wired and wireless interfaces, including but not limited to at least one of a network (e.g., the Internet, a local area network (LAN), a wide area network (WAN), or an integrated services digital network (ISDN)), a dial-up connection, a digital subscriber line (DSL), a cellular phone connection, and a cable modem. The pre-processing computer device 304 can be any device capable of performing the steps described herein, including but not limited to a desktop computer, a laptop computer, a personal digital assistant (PDA), a cellular phone, a smart phone, a tablet computer, or other network-based connectable device.

[0098] In the exemplary embodiment, MC computer device 302 also communicates with DMLM computer device 306, which communicates with computer device 24 ( Figure 1 In the exemplary embodiment, the DMLM computer device 306 is configured to communicate with and control the DMLM controller 308, which controls one or more laser devices 14 ( Figure 1 In the exemplary embodiment, the DMLM controller 308 is similar to the controller 26 ( Figure 1 ). As described above, the DMLM computer device 306 and the DMLM controller 308 control the construction of the part 28 based on the construction file 210. In the exemplary embodiment, during and after the construction of the part 28, the DMLM computer device 306 receives sensor data from the sensor 310. The DMLM computer device 306 is configured to communicate with the MC computer device 302 through a number of interfaces, including but not limited to at least one of the following: a network (such as the Internet, LAN, WAN, or Integrated Services Digital Network (ISDN)), a dial-up connection, a digital subscriber line (DSL), a cellular phone connection, a satellite connection, and a cable modem. The DMLM computer device 306 can be any device capable of accessing a network such as the Internet, including but not limited to a desktop computer, a laptop computer, a personal digital assistant (PDA), a cellular phone, a smart phone, a tablet computer, a tablet phone, embedded in a device such as the system 10, or other network-based connectable devices.

[0099] The sensors 310 are suitable for measuring parameters of interest, such as temperature, distributed temperature, pressure, current, magnetic field, electric field, chemical property, dimension, size, shape, or a combination thereof. Some sensors 310 may include the optical system 20 ( Figure 1 ) and may also include, for example but not limited to: a photomultiplier tube, a photodiode, an infrared camera, a charge coupled device (CCD) camera, a CMOS camera, a pyrometer, or a high-speed visible light camera. In other embodiments, sensor 310 may be coupled to sensor 216 ( Figure 1) and may include a device capable of performing a computerized tomography (CT) scan, a computerized axial tomography (CAT) scan, an ultrasound imaging scan, a visual inspection, and / or any other non-destructive scan or analysis of the part 28 to determine the quality and suitability of the part 28 for use. The sensor 310 is connected to the MC computer device 302 or the DMLM computer device 306 through a number of interfaces, including, but not limited to, a network such as a local area network (LAN) or a wide area network (WAN), a dial-up connection, a cable modem, an Internet connection, wireless, and a dedicated high-speed integrated services digital network (ISDN) line. The sensor 310 receives data about the construction of the part 28 and reports the data to at least the MC computer device 302 or the DMLM computer device 306. In some embodiments, the sensor 310 also communicates with other computer systems (for example, but not limited to, a user computer device).

[0100] Database server 312 is connected to database 314, and database 314 contains information about various things, as described in more detail herein. In one embodiment, centralized database 314 is stored on MC computer device 302. In an alternative embodiment, database 314 is remotely stored relative to MC computer device 302 and can be non-centralized. In some embodiments, database 314 includes a single database with separated sections or partitions, or in other embodiments, database 314 includes multiple databases, each database is separated from each other. Database 314 stores (but is not limited to) data and information such as build file 210, geometry, sensor data and parameter adjustment. In some embodiments, a user can access database 314 by, for example, logging into MC computer device 302 through a user computer device.

[0101] Figure 4 can be used with the manufacturing system 300( Figure 3 Schematic diagram of an exemplary configuration of a client system for use with a DMLM controller 308 (shown in ). Computer device 400 is operated by user 402. Computer device 400 may include, but is not limited to, DMLM controller 308, controller 26, DMLM computer device 306, computer device 24, and a user computer device (not shown). Computer device 400 includes a processor 404 for executing instructions. In some embodiments, executable instructions are stored in a memory area 406 (also referred to as a memory device). Processor 404 may include one or more processing units (for example, in a multi-core configuration). Memory area 406 is any device that allows information such as executable instructions and / or transaction data to be stored and retrieved. Memory area 406 includes one or more computer-readable media. In some embodiments, memory area 406 includes database 314 ( Figure 3In some embodiments, memory area 406 is stored in computer device 400. In alternative embodiments, memory area 406 is stored remotely from computer device 400.

[0102] The computer device 400 also includes at least one media output component 408 for presenting information to the user 402. The media output component 408 is any component capable of conveying information to the user 402. In some embodiments, the media output component 408 includes an output adapter (not shown), such as a video adapter and / or an audio adapter. The output adapter is operably coupled to the processor 404 and is operably coupled to an output device, such as a display device (e.g., a cathode ray tube (CRT), a liquid crystal display (LCD), a light emitting diode (LED) display, or an "electronic ink" display) or an audio output device (e.g., a speaker or headphones). In some embodiments, the media output component 408 is configured to present a graphical user interface (e.g., a web browser and / or a client application) to the user 402. In some embodiments, the computer device 400 includes an input device 410 for receiving input from the user 402. The user 402 can use the user device 410 to select a build file 210 ( Figure 2 ), but is not limited thereto. Input device 410 may include, for example, a keyboard, a pointing device, a mouse, a stylus, a touch panel (such as a touch pad or a touch screen), a gyroscope, an accelerometer, a position detector, a biometric input device, and / or an audio input device. A single component such as a touch screen may serve as both an output device of media output component 408 and an input device 410.

[0103] The computer device 400 may also include a communication interface 412 that can be communicatively coupled to a sensor 310 ( Figure 3 The communication interface 412 may include, for example, a wired or wireless network adapter and / or a wireless data transceiver for use with a mobile communication network or a local area network.

[0104] Stored in memory area 406 are, for example, computer-readable instructions for providing a user interface to user 402 via media output component 408 and optionally receiving and processing input from input device 410. The user interface may primarily include a web browser and / or a client application. A web browser enables a user such as user 402 to display and interact with media and other information typically embedded in a web page or website. The client application allows user 402 to communicate with, for example, MC computer device 302 ( Figure 3 For example, the instructions may be stored by a cloud service, and the output of the execution of the instructions may be sent to the media output component 408.

[0105] The processor 404 executes computer executable instructions for implementing aspects of the present disclosure. In some embodiments, the processor 404 is converted into a special-purpose microprocessor by executing computer executable instructions or by being programmed in other ways. For example, the processor 404 is programmed with instructions discussed further below.

[0106] Figure 5 can be used with the manufacturing system 300 (both in Figure 3 More specifically, the server computer device 500 may include, but is not limited to, the MC computer device 302, the pre-processing computer device 304, the DMLM computer device 306, and the database server 312 (all in Figure 3 500). The server computer device 500 also includes a processor 502 for executing instructions. The instructions may be stored in a memory area 504 (also referred to as a memory device). The processor 502 may include one or more processing units (e.g., in a multi-core configuration).

[0107] Processor 502 is operably coupled to communication interface 506, enabling server computer device 500 to communicate with other devices, such as another server computer device 500, sensor 310 ( Figure 3 ), MC computer device 302, DMLM computer device 306, DMLM controller 308 (shown in Figure 3 For example, the communication interface 506 can receive data from the sensor 310, such as Figure 3 shown.

[0108] Processor 502 is also operatively coupled to storage device 508. Storage device 508 is any computer-operated hardware suitable for storing and / or retrieving data, such as, but not limited to, data associated with database 314 ( Figure 3 508). In some embodiments, the storage device 508 is integrated in the server computer device 500. For example, the server computer device 500 may include one or more hard disk drives as the storage device 508. In other embodiments, the storage device 508 is external to the server computer device 500 and is accessed by multiple server computer devices 500. For example, the storage device 508 may include a storage area network (SAN), a network attached storage (NAS) system, and / or multiple storage units such as hard disks and / or solid state disks in a redundant array of inexpensive disks (RAID) configuration.

[0109] In some embodiments, the processor 502 is operably coupled to the storage device 508 via a storage interface 510. The storage interface 510 is any component capable of providing the processor 502 with access to the storage device 508. The storage interface 510 may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and / or any component that provides the processor 502 with access to the storage device 508.

[0110] The processor 502 executes computer executable instructions for implementing aspects of the present disclosure. In some embodiments, the processor 502 is converted into a special-purpose microprocessor by executing computer executable instructions or by being programmed in other ways. For example, the processor 502 is programmed with instructions further described below.

[0111] Figure 6 is to use the manufacturing system 300( Figure 3 ) is a schematic diagram of an exemplary feedforward control system 600 for dynamically adapting to additive manufacturing of a part 28. In the exemplary embodiment, the feedforward control system 600 is a manufacturing process 204 ( Figure 2 ).

[0112] As described above, in system 10, 212 ( Figure 2 As part 28 is being built 212, sensor 310 monitors part 28 and transmits real-time information about the building process 212 to feedforward control computer device 602. In the exemplary embodiment, MC computer device 302 ( Figure 3 ) includes a feedforward control computer device 602.

[0113] The feedforward control computer device 602 receives real-time sensor information from the sensor 310. In the exemplary embodiment, the feedforward control computer device 602 stores the build file 210 ( Figure 2212 ). As described above, the build file 210 includes a plurality of geometries, wherein the geometries are repeatable portions of the part 28. In some embodiments, the feedforward control computer device 602 analyzes the build file 210 to determine the geometries contained therein. When the feedforward control computer device 602 receives real-time sensor information, the feedforward control computer device 602 analyzes the sensor information for the geometries that the system 10 is currently building 212 . Once the geometries are completed, the feedforward control computer device 602 determines updated build parameters for the particular geometries. The feedforward control computer device 602 determines whether there are other copies of the geometries or similar geometries in the build file 210. The feedforward control computer device 602 applies those determined updated build parameters to any similar geometries in the build file 210 that still need to be built. The feedforward control computer device 602 transmits the updated build parameters in the build file 210 to the system 10 so that the system 10 can use these updated build parameters the next time the system 10 must build 212 the geometries when building the part 28.

[0114] In some further embodiments, the feedforward control computer device 602 also receives adjusted build parameters from the system 10, for example, when using the feedback control 218 ( Figure 2 ) when the system 10 must increase power to keep the melt pool size constant. The feedforward control computer device 602 uses the adjusted build parameters to further update the build parameters for other forms of the geometric shape.

[0115] In some embodiments, the feedforward control computer device 602 generates a model (also referred to as a digital twin) of the process of manufacturing the part 28 (e.g., the melt pool response to the laser and the sensor response to the melt pool). The model simulates the process of manufacturing the part 28. In an exemplary embodiment, the model can be developed from physics, data fitting, and / or a combination thereof. In some embodiments, the model may include, but is not limited to, information from one or more build files 210, sensors 310 during the build 212, and one or more inspections 214 after the build 212 is completed. The information provided or used may be based on data or intelligence that is real, artificial, or virtual. Real information includes, but is not limited to, data or information measured or obtained from a physical part or actual build. Artificial information includes, but is not limited to, data or information that is not measured or obtained from a physical part or actual build. Virtual information includes, but is not limited to, data or information created, modeled, designed, or otherwise derived using a computer. In some further embodiments, the model includes information from multiple builds of the part 28. The model also includes multiple geometries described above. In some embodiments, the model can be adjusted and / or adapted using process measurements. The model may receive build parameter inputs (laser power, scanning device speed, CAD geometry) and predict the responses of the process (e.g., melt pool and sensors). In these embodiments, the feedforward control computer device 602 may use the information in the model to simulate the build 212 of the part 28. The feedforward control computer device 602 may further simulate the results of changes to the build parameters.

[0116] Figure 7 is to use the manufacturing system 300( Figure 3 ) is a schematic diagram of another exemplary feedforward control system 700 that dynamically adapts to additive manufacturing of part 28. In the exemplary embodiment, feedforward control system 700 is a manufacturing process 204 ( Figure 2 ).

[0117] As described above, in system 10, 212 ( Figure 2 As part 28 is being built 212, sensor 310 monitors part 28 and transmits real-time information about the building process 212 to feedforward control computer device 702. In the exemplary embodiment, MC computer device 302 ( Figure 3 ) includes a feedforward control computer device 702. The feedforward control computer device 702 receives information from the sensor 310 regarding one or more completed builds of the part 28. In the exemplary embodiment, the feedforward control computer device 702 stores the build file 210 ( Figure 2As described above, build file 210 includes a plurality of geometries, where a geometry is a repeatable portion of part 28. In some embodiments, feedforward control computer device 702 analyzes build file 210 to determine the geometries contained therein. Feedforward control computer device 702 also receives adjusted build parameters from system 10, such as when system 10 must increase power to keep melt pool size constant.

[0118] When the feedforward control computer device 702 receives the sensor information, the feedforward control computer device 702 analyzes the sensor information for the geometric shape related to the sensor information. For each geometric shape, the feedforward control computer device 702 compares the sensor information, the build parameters from the build file 210, and the adjusted build parameters from the system 10 to determine updated build parameters for the geometric shape. For example, the feedforward control computer device 702 analyzes each instance of a specific geometric shape to see the build parameters and results. The feedforward control computer device 702 determines updated build parameters for the geometric shape and applies these build parameters to all similar geometric shapes in the build file 210.

[0119] In an exemplary embodiment, feedforward control computer device 702 applies the updated build parameters to build file 210 to generate an updated build file 210. In other embodiments, feedforward control computer device 702 generates a deltafile that becomes a companion to build file 210, wherein the deltafile includes changes from build file 210. In some embodiments, build file 210 includes a first build parameter, such as melt pool size or melt pool temperature profile. The deltafile includes a second build parameter, such as first scanning device 18 ( Figure 1 In these embodiments, system 10 uses a variation file in conjunction with build file 210 to build part 28.

[0120] In some embodiments, the feedforward control computer device 702 generates a model (also referred to as a digital twin) of the process of manufacturing the part 28 (e.g., the melt pool response to the laser and the sensor response to the melt pool). The model simulates the process of manufacturing the part 28. In an exemplary embodiment, the model can be developed from physics, data fitting, and / or a combination thereof. In some embodiments, the model includes information from the build file 210, from the sensor 310 during the build 212, and from one or more inspections 214 after the build 212 is completed. In some other embodiments, the model includes information from multiple builds of the part 28. The model also includes multiple geometries described above. In some embodiments, the model can be adjusted and / or adapted using process measurements. The model typically receives build parameter inputs (laser power, speed of the scanning device, CAD geometry), and predicts the response of the process (e.g., melt pool and sensor). In these embodiments, the feedforward control computer device 602 can use the information in the model to simulate the build 212 of the part 28. The feedforward control computer device 602 can also further simulate the results of changes to the build parameters.

[0121] Fig.12 , Fig.13 and Fig.1412 is a schematic diagram of other exemplary systems and methods including an apparatus for dynamically adapting a build, build parameters and / or build file for additive manufacturing of one or more parts. As an example, apparatus 1200 may be used to perform a method for adjusting a designed shape of a part having a desired final shape for additive manufacturing. The method enables, in part, an additive manufacturing system to compensate or adjust for geometric distortion or other changes that may occur during the additive manufacturing process using, for example, a DMLM system 1214 before, during, or after construction. For example, a physical part may be made using a build file that may be associated with a CAD or virtual model 1202 (e.g., a nominal CAD model). The characteristics and physical features of a desired physical or digital part may be measured or otherwise determined and electronically stored before, during, or after construction. Measurements or other determinations 1204 may be made digitally, physically, or virtually using various means 1206, including, but not limited to, inspection / meteorology, computed tomography, x-ray, magnetic resonance, ultrasound, light, electronics, radio frequency, photography, and scanning. Without limitation, means 1206 may be an integral part of or in communication with device 1200, or may be a separate device. Measurements or other determinations of properties and features of a physical or digital part may be compared with a nominal CAD model or known properties or features before, during, or after the build process to identify differences or deviations, such as differences in geometry and differences in material. The nominal CAD model or other digital or virtual model may then be modified / deformed, such as as CAD 1212, using, for example, a geometry compensation function 1210 functionally guided by geometry differences or material differences 1208, so that geometry or other properties or features may be compensated or otherwise modified before, during, or after the build. In some embodiments, a partial or entire build file is generated based on the modification. In some embodiments, without limitation, a portion or the entire build itself, as well as one or more build parameters, is modified directly or indirectly.

[0122] Device 1200 may be configured to serve as a partial or full digital twin or virtual build of a build process or AM machine. Devices 1200, 1300, and 1400 may be separate devices separate from one or more build machines (e.g., build machine 1214 indicated by reference lines A, B, and C). Devices 1200, 1300, and 1400 may communicate with a build machine directly or indirectly via communication means, such as, but not limited to, computer-based means, hardwired or wireless, via a mobile storage device, or the cloud.

[0123] The build parameters used by the apparatus 1200, 1300, 1400 are those build parameters used to control one or more components of one or more systems or otherwise build a part. The build parameters will depend on the additive manufacturing method or system used and the materials from which the part is constructed. The build parameters may include one or more of the following: (not limited to) power, speed, orientation, location of energy sources, galvanometers, mirrors, scanners, sensors, detectors, conveyors, build plates, and material applicators and removers. The build parameters may also include one or more of the following: (not limited to) materials used by the system to perform the method, such as gases, gas pressures, and gas flows; melt pool size and melt pool temperature distribution; materials that make up the part itself and intermediate part materials; speed and method of applying materials during the build; starting shape or shape of the part; and intermediate build shapes of the part. The build parameters may also include one or more of (not limited to) geometry, material properties, process yields, and functional characteristics of the part and build.

[0124] The apparatus 1200 can be configured as an integral part of or in communication with an additive manufacturing system that builds a part, such as a build machine 1214. For example, the learning feedback control is used as initial build parameters on a physical machine, and the learning feedback control can be configured to refine any features or parameters by virtual build via learning feedforward control on the machine or off the machine.

[0125] Fig.13 1 is a schematic diagram of another exemplary system for dynamically adapting a build or build file for an additively manufactured part. An apparatus 1300, such as that shown in the figure, can be used to modify the build file and recalculate the build prediction so that geometric parameters related to the compliance or non-compliance of the local geometry can be determined. The apparatus may include a build prediction tool. The tool may be finite element or rule-based, predicting quantities such as but not limited to distortion or residual stress. These quantities can be used to modify the build file and recalculate the build prediction. Compliance as used herein generally refers to the geometric response to changes in local geometry during the build. These geometric parameters can be used, for example, to calculate a local scale factor map, which can be used in conjunction with measurement data to improve the distortion of any final part of the subsequent build. The system can iterate, for example, until the predicted distortion is less than a tolerance threshold after the part is built. The parameters can be based on real, virtual or artificial data or information or a combination thereof.

[0126] Fig.1414 is a schematic diagram of another exemplary system for writing, creating, modifying or adapting a build or build file for additively manufactured parts. The device 1400 can be used, for example, to dynamically modify the build file and recalculate the build prediction. The system can also include, for example, a build prediction tool, which is based on finite elements or rules. Geometry parameters can be used, for example, to calculate a local scale factor map, which can be combined with the predicted distortion to improve the initial part distortion. The scale factor map can then be used in combination with measurement data for, for example, a subsequent or currently running build to further refine the final part distortion for the subsequent build. The system for adapting a build or build file can use any type of data or information. The system can operate and / or iterate in parallel using pre-existing real, virtual or artificial data or information, real-time data or information, or a combination thereof when one or more parts are being built. The system can also operate and / or iterate before or after the build to adapt to one or more subsequent builds and / or one or more build machines. The build machine can be directly or wirelessly connected to the device 1400, and can be located adjacent to or away from the device 1400, and can communicate through the cloud.

[0127] Any one or more methods, features, or characteristics of apparatus 1200, 1300, or 1400 may be combined with any method or system disclosed herein. Any one or more methods and systems may include a virtual control loop, either alone or in combination with a real-time control loop, any of which may use pre-existing, stored, predicted, and / or real-time data or information. The information may be real, artificial, or virtual.

[0128] Apparatus 1200, 1300, and 1400 may include a computer-enabled device to dynamically create or modify all or a portion of an additive manufacturing build, build parameters, or a combination thereof, in order to manufacture a part. The device may communicate directly or indirectly with an additive manufacturing machine or multiple machines using one or more build parameters. The device may be configured to analyze build information related to a part or a machine, wherein a portion of the build information pertains to pre-existing data related to the part, and wherein a portion of the build information pertains to data that is non-pre-existing data about the part. Non-pre-existing data is data that is not associated with a current build of the part prior to evaluation by the device.

[0129] Pre-existing or non-pre-existing data about a part may include, but is not limited to, build parameters, geometry, and data related to design, materials, post-processing, use, repair, tracking, material properties, process yield, functional characteristics, and cost, and combinations of these types of data. The data may be real, artificial, or virtual, and may be derived from or through a real, artificial, or virtual source or combination of sources. Non-existing data may be data measured or sensed in real time by one or more additive manufacturing machines during the build of one or more parts.

[0130] The device may be configured to evaluate whether one or more differences between pre-existing data and non-pre-existing data will result in a deviation of the part or an improvement of the part. The deviation or improvement may include, but is not limited to, geometry, material properties, yield, functional characteristics, build cost, tracking, and safety. The evaluation may include a virtual control loop that may be iterative. The device may also be configured to automatically create or modify one or more build parameters of the part based on the evaluation of one or more differences.

[0131] Figure 8 is to use a feedforward control system 600 ( Figure 6 ) dynamically adapts to the part 28 ( Figure 1 ) of the additive manufacturing build file 210 (shown in Figure 2 ) is a flowchart of an exemplary process 800. In the exemplary embodiment, the process 800 is performed by the feedforward control computer device 602 ( Figure 6 In other embodiments, process 800 is performed by MC computer device 302 ( Figure 3 ) is executed.

[0132] In the exemplary embodiment, feedforward control computer device 602 stores 802 a build file 210 for building part 28, where build file 210 includes a plurality of geometries, each of which includes one or more build parameters. Examples of build parameters include, but are not limited to, laser device 14 ( Figure 1 ), the scanning speed of the first scanning device 18, the desired melt pool size, and the desired melt pool temperature distribution. In the exemplary embodiment, the part 28 is being processed by the system 10 ( Figure 1 ) actively builds 212 ( Figure 2 The feedforward control computer device 602 receives 804 one or more sensors 310 ( FIG. 1 ) of the first geometry of the part 28 currently being built 212 . Figure 3 Sensor information shown in ).

[0133] In the exemplary embodiment, feedforward control computer device 602 determines 806 one or more adjustments to one or more build parameters of a first geometric shape based on the sensor information. As described above, feedforward control computer device 602 determines 806 one or more adjustments to the power and / or scanning speed of first scanning device 18 to improve the building of the particular geometric shape. Feedforward control computer device 602 identifies 808 one or more subsequent geometric shapes of the plurality of geometric shapes that are to be built that are similar to the first geometric shape. Feedforward control computer device 602 adjusts 810 build file 210 with one or more build parameters of the one or more subsequent geometric shapes based on the one or more adjustments. Feedforward control computer device 602 transmits 812 the adjusted build file 210 to system 10 that is currently manufacturing part 28.

[0134] In some embodiments, the feedforward control computer device 602 receives subsequent sensor information for subsequent copies of similar geometric shapes being built using the adjusted build file 210. The feedforward control computer device 602 also adjusts one or more build parameters for one or more remaining subsequent similar geometric shapes based on the subsequent sensor information. For example, the system 10 builds 212 a first copy of a geometric shape in the part 28. The feedforward control computer device 602 determines a first set of adjustments to the build parameters for the particular geometric shape. After the system 10 uses the first set of adjustments to the build parameters to build a second copy of the geometric shape, the feedforward control computer device 602 analyzes the results and further adjusts the build parameters. The feedforward control computer device 602 transmits the further adjusted build file 210 to the build parameters system 10 for the next time the system 10 builds 212 another copy of the geometric shape.

[0135] In some embodiments, the feedforward control computer device 602 compares sensor information from the first geometric shape with subsequent sensor information from a second positive build 212 of the geometric shape. In some embodiments, this second time is during a build 212 of the same part 28. In other embodiments, the second time is on a subsequent build 212 of a different copy of the part 28. The feedforward control computer device 602 compares one or more build parameters of the first geometric shape with one or more build parameters of the subsequent geometric shape. The feedforward control computer device 602 determines one or more adjustments based on the two comparisons.

[0136] In some further embodiments, the feedforward control computer device 602 receives one or more real-time adjustments to the parameters from the system 10. Figure 22, system 10 makes one or more real-time adjustments while building 212 a first geometric shape of part 28. Feedforward control computer device 602 determines one or more adjustments to one or more build parameters of the first geometric shape based on the sensor information and the one or more real-time adjustments.

[0137] Fig. 9 is to use a feedforward control system 600 ( Figure 6 ) dynamically adapts to the part 28 ( Figure 1 ) of the additive manufacturing build file 210 (shown in Figure 2 ) is a flowchart of another exemplary process 900. In the exemplary embodiment, the process 900 is performed by the feedforward control computer device 602 ( Figure 6 In other embodiments, process 900 is performed by MC computer device 302 ( Figure 3 In some other embodiments, the process 900 is performed by the feedforward control computer device 702 ( Figure 7 ) is executed.

[0138] In the exemplary embodiment, the feedforward control computer device 602 stores 902 a model of a manufacturing process for building the part 28, including a plurality of building parameters. The building parameters may include, but are not limited to, the laser device 14 ( Figure 1 ), the power of the first scanning device 18, the scanning speed of the first scanning device 18, the desired melt pool size and the desired melt pool temperature distribution. In the exemplary embodiment, the manufacturing process model is stored in the database 314 ( Figure 3 The feedforward control computer device 602 receives 904 the build 212 ( Figure 2 The melt pool 22 (shown in Figure 1 Current sensor information of at least one current sensor reading).

[0139] In the exemplary embodiment, the feedforward control computer device 602 determines 906 one or more properties of the melt pool 22 based on the current sensor information. The one or more properties may include, but are not limited to, melt pool width, melt pool height, melt pool temperature distribution, two-dimensional (2D) melt pool shape, or any other directly observable property. The feedforward control computer device 602 calculates 908 at least one invisible property of the melt pool 22. The invisible property represents a property of the melt pool 22 that is not directly observable, such as, but not limited to, melt pool depth and three-dimensional (3D) melt pool shape.

[0140] In the exemplary embodiment, feedforward control computer device 602 determines 910 adjusted build parameters based on the at least one invisible attribute, the one or more attributes, and the plurality of build parameters. Feedforward control computer device 602 transmits 912 the adjusted build parameters to a machine, such as system 10, currently manufacturing part 28.

[0141] In an exemplary embodiment, the model effectively simulates the manufacturing process of building a digital twin of the part 28, including information about the materials, thermal properties, and other attributes of the part 28. In some embodiments, the model also includes inspection 214 ( Figure 2 212 ). In the exemplary embodiment, the model (digital twin) also includes the effects of all movements of the device on manufacturing part 28. This includes the path and power settings of the laser required over time as the laser melts the powder to manufacture 212 part 28. In the exemplary embodiment, DMLM computer device 306 or MC computing device 302 is able to simulate the construction of part 28 using the model. Moreover, when adjustments are made to the process of manufacturing part 28, the model is also adjusted.

[0142] In some further embodiments, the model includes materials, thermal characteristics, and other properties of the part 28 based on previous builds of similar parts. In these embodiments, the pre-processing computer device 304 receives a build file 210 for a new part 28. The pre-processing computer device 304 analyzes the build file 210 and generates a model of the manufacturing process for building the part 28, wherein the manufacturing process model simulates the building of the part 28 based on the received build file 210 and information from the historical build. The pre-processing computer device 304 uses the model to simulate the building of the part 28. During the build, the historical information allows the model to be adaptively modified to react like a physical process during the build. For example, the model can determine the simulated melt pool size based on the laser settings and the historical information. Based on the historical information, the pre-processing computer device 304 can determine which changes in power will be required to build the part based on how other parts have been built in the past. In some further embodiments, the pre-processing computer device 304 repeatedly simulates the building of the part 28 based on adjustments made during manufacturing with the results of one build fed into the model for the next build.

[0143] In some embodiments, the feedforward control computer device 602 generates the model. The feedforward control computer device 602 stores a build file 210 for building the part 28, including a plurality of build parameters. The feedforward control computer device 602 receives a plurality of build information. Each set of build information of the plurality of build information includes sensor information of a part built by a machine using the build file 210. The feedforward control computer device 602 generates a model of the manufacturing process for the part 28 based on the build file 210 and the plurality of build information. In some further embodiments, the feedforward control computer device 602 receives a plurality of sensor information from additional builds of the part 28, and updates the manufacturing process model based on the received plurality of sensor information.

[0144] Fig.10 is to use a feedforward control system 700 ( Figure 7 ) dynamically adapts to the part 28 ( Figure 1 ) of the additive manufacturing build file 210 (shown in Figure 2 1000 is a flowchart of an exemplary process 1000 of the present invention. In the exemplary embodiment, the process 1000 is performed by the feedforward control computer device 702 ( Figure 7 In other embodiments, process 1000 is performed by MC computer device 302 ( Figure 3 ) is executed.

[0145] In the exemplary embodiment, the feedforward control computer device 702 stores 1002 for constructing 212 ( Figure 2 1004 system 10 ( Figure 1 ) uses build file 210 to build 212 sensor information for part 28. Feedforward control computer device 702 compares 1006 the sensor information for each of a plurality of geometries with corresponding one or more values ​​of a first build parameter for those geometries to determine one or more differences.

[0146] Based on the one or more differences, the feedforward control computer device 702 determines 1008 one or more values ​​of a second build parameter for each geometric shape. Examples of build parameters include, but are not limited to, the laser device 14 ( Figure 11010 and the scan speed of the first scanning device 18. The feedforward control computer device 702 generates 1010 an updated build file 210 for the part 28, including one or more values ​​for the second build parameter. The feedforward control computer device 702 transmits 1012 the updated build file 210 to the system 10 for manufacturing.

[0147] In some embodiments, the feedforward control computer device 702 receives subsequent sensor information from the system 10 for a subsequent build of the part 28 using the updated build file 210. The feedforward control computer device 702 compares the subsequent sensor information for each of the plurality of geometric shapes with the corresponding one or more values ​​of the second build parameter to determine one or more additional differences. The feedforward control computer device 702 determines one or more updated values ​​of the second build parameter for each geometric shape based on the one or more additional differences. The feedforward control computer device 702 generates a further updated build file for the part, including the one or more updated values ​​for the second build parameter. The feedforward control computer device 702 transmits the further updated build file to the system 10.

[0148] In some embodiments, the feedforward control computer device 702 receives one or more real-time adjustments to the parameters from the system 10. Figure 2 During the build 212 of the part 28 (shown in FIG. 1 ), the system 10 makes one or more real-time adjustments. The feedforward control computer device 702 determines one or more updated values ​​of the second build parameter for each geometry based on the one or more differences and the one or more real-time adjustments.

[0149] In some further embodiments, the feedforward control computer device 702 compares the plurality of geometric shapes in the build file 210 to determine at least a subset of similar geometric shapes. The feedforward control computer device 702 determines one or more updated values ​​for the second build parameter for one of the subset of geometric shapes. The feedforward control computer device 702 applies the determined one or more updated values ​​to each occurrence of the subset of geometric shapes in the updated build file 210.

[0150] In some embodiments, the first build parameters are stored in build file 210 and the second build parameters are stored in a variation file. Both build file 210 and variation file are transmitted to system 10 to manufacture part 28.

[0151] Fig.11 is to use a feedforward control system 700 ( Figure 7 ) dynamically adapts to the part 28 ( Figure 1 ) of the additive manufacturing build file 210 (shown in Figure 2In an exemplary embodiment, process 1100 is performed by feedforward control computer device 702 ( Figure 7 In other embodiments, process 1100 is performed by MC computer device 302 ( Figure 3 ) is executed.

[0152] In the exemplary embodiment, the feedforward control computer device 702 stores 1102 for constructing 212 ( Figure 2 28) includes one or more build parameters. For example, the build parameters may include at least one of the following: Figure 1 The feedforward control computer device 702 receives 1104 a plurality of build information. Each set of build information includes information generated by a plurality of systems 10 ( Figure 1 ) uses build file 210 to build sensor information for part 28.

[0153] In the exemplary embodiment, feedforward control computer device 702 compares the plurality of sensor information to the one or more build parameters to determine one or more differences. For example, if the build parameter is a desired melt pool size, and the sensor information shows an actual melt pool size, feedforward control computer device 702 can determine the difference between the desired melt pool size and the actual melt pool size. From the one or more differences, feedforward control computer device 702 determines 1108 one or more adjustments to the one or more build parameters to correct the one or more differences.

[0154] For example, the feedforward control computer device 702 compares 1106 sensor information from four different builds 212 of a part 28 on two different systems 10. The feedforward control computer device 702 determines differences for each build based on the corresponding sensor information. The feedforward control computer device 702 compares the various differences to determine which differences indicate a problem with the build file 210, which differences only occur once (e.g., a one-time event), and which differences are specific to the system 10. Based on the differences that indicate a problem with the build file 210, the feedforward control computer device 702 determines 1108 one or more adjustments to one or more build parameters to correct and / or improve the manufacture of the part 28.

[0155] In the exemplary embodiment, feedforward control computer device 702 generates 1110 updated build file 210 based on the one or more adjustments. Feedforward control computer device 702 transmits 1112 updated build file 210 to at least one system 10 for manufacturing.

[0156] In some embodiments, each set of build information also includes one or more real-time adjustments to build parameters for that build 212 from system 10. Figure 2 During a build 212 of a part 28 (shown in FIG. 1 ), the system 10 makes one or more real-time adjustments. The feed-forward control computer device 702 compares the real-time adjustments from each build with the sensor information for the corresponding build 212. The feed-forward control computer device 702 determines one or more adjustments to the build parameters based on the comparison. For example, if each time the system 10 builds a particular part, the system 10 must increase the power level to achieve a desired melt pool size, the feed-forward control computer device 702 determines 1108 one or more adjustments to correct this difference.

[0157] In some embodiments, the feedforward control computer device 702 determines one or more trends based on a comparison of the plurality of sensor information and the one or more build parameters. Examples of trends include, but are not limited to, a sustained rate of change of one or more differences, a sustained deviation of one or more differences, and / or some other sustained observation. The feedforward control computer device 702 determines one or more adjustments to the one or more build parameters based on the one or more trends.

[0158] In some embodiments, the plurality of build information includes build information from the first system 10 and the second system 10. The feedforward control computer device 702 determines which build information is associated with the first system 10 and which build information is associated with the second system 10. The feedforward control computer device 702 compares the two sets of build information to determine which differences in build parameters are machine-independent and which differences in build parameters are associated with a specific machine. In some embodiments, the feedforward control computer device 702 determines these differences based on trends of the plurality of builds 212. The feedforward control computer device 702 determines one or more adjustments to be made based on the differences that are machine-independent. The feedforward control computer device 702 generates an updated build file 210 based on the differences that are machine-independent. The feedforward control computer device 702 also generates a first machine build file 210 that is specific to the first system 10 based on the updated build file 210 and the differences that are specific to the first system 10. The feedforward control computer device 702 also generates a second machine build file 210 that is specific to the second system 10 based on the updated build file 210 and the differences that are specific to the second system 10. The feedforward control computer device 702 transmits the first machine build file 210 to the first system 10 for building the part 28. The feedforward control computer device 702 transmits the second machine build file 210 to the second system 10 for building the part 28. The feedforward control computer device 702 transmits the updated build file 210 to any system 10 that does not have a machine-specific build file 210.

[0159] In some embodiments, the build file 210 includes a plurality of geometric shapes, each of which includes one or more values ​​of a build parameter. The feedforward control computer device 702 compares the plurality of geometric shapes to determine at least a subset of similar geometric shapes. The feedforward control computer device 702 analyzes one or more build parameters associated with each subset of geometric shapes and a plurality of sensor information associated with each subset of geometric shapes. Based on the comparison, the feedforward control computer device 702 determines one or more updated build parameters for the subset of geometric shapes. The feedforward control computer device 702 generates an updated build file 210, which includes one or more updated build parameters for each subset of geometric shapes. For example, the feedforward control computer device 702 analyzes each occurrence of a specific geometric shape on all builds 212 of the part 28. Based on the analysis, the feedforward control computer device 702 determines updated build parameters for the specific geometric shape. The feedforward control computer device 702 generates an updated build file 210, wherein each occurrence of the specific geometric shape is updated with the updated build parameters.

[0160] In some embodiments, the first build parameters are stored in build file 210 and the second build parameters are stored in a variation file. Both build file 210 and variation file are transmitted to system 10 to manufacture part 28.

[0161] The methods and systems described above provide a method for dynamically adapting the additive manufacturing of a part based on the performance of the part's build. Specifically, the embodiments described herein include a computing device configured to receive information about the build of a part and generate an updated build file to improve the build of the part. The computing device updates the build file using sensor data to improve the quality of the finished part. The computing device divides the build file into a number of geometric shapes, wherein the build file may include multiple copies of the same geometric shape, and updates each copy of the same geometric shape based on the previous build of the geometric shape. In some embodiments, the computer device updates the build file in real time while the part is being built. In other embodiments, the computer device updates the build file after the part is completed. The systems and methods described herein allow for a reduction in the number of times a part must be built to obtain an acceptable or ideal part.

[0162] Exemplary technical effects of the methods, systems, and apparatus described herein include at least one of: (a) improving build plans for parts; (b) reducing defects in parts due to manufacturing; (c) determining differences between different machines used to make parts and resolving such differences; (d) reducing the number of iterations required to determine appropriate settings for building a part; and (e) using lessons learned from building previous parts when building future parts.

[0163] The exemplary embodiments of the methods, systems, and apparatus for dynamically adapting additive manufacturing of parts are not limited to the specific embodiments described herein, but rather, components of the systems and / or steps of the methods may be used independently and separately from other components and / or steps described herein. For example, the methods may also be used in conjunction with other systems for manufacturing parts using a machine with multiple inputs, and are not limited to practice with only the systems and methods described herein. Rather, the exemplary embodiments may be implemented and used in conjunction with many other applications, devices, and systems that may benefit from dynamic build files.

[0164] Although specific features of various embodiments of the present disclosure may be shown in some drawings but not in other drawings, this is only for convenience. According to the principles of the present disclosure, any feature of the drawings may be combined with any feature of any other drawings for reference and / or protection.

[0165] Some embodiments include the use of one or more electronic devices or computing devices. Such devices typically include a processor, a processing device or a controller, such as a general-purpose central processing unit (CPU), a graphics processing unit (GPU), a microcontroller, a reduced instruction set computer (RISC) processor, an application-specific integrated circuit (ASIC), a programmable logic circuit (PLC), a field programmable gate array (FPGA), a digital signal processing (DSP) device and / or any other circuit or processing device capable of performing the functions described herein. The methods described herein may be encoded as executable instructions embodied in a computer-readable medium including, but not limited to, a storage device and / or a memory device. Such instructions, when executed by a processing device, cause the processing device to perform at least a portion of the methods described herein. The above examples are exemplary only, and therefore do not wish to limit the definition and / or meaning of the terms processor and processing device in any way.

[0166] Additionally, the computer systems discussed herein may include more, less, or alternative functionality, including functionality discussed elsewhere herein.The computer systems discussed herein may include or be implemented by computer executable instructions stored on a non-transitory computer readable medium or media.

[0167] The processor or processing element may be trained using supervised or unsupervised machine learning, and the machine learning program may use a neural network, which may be a convolutional neural network, a deep learning neural network, or a combined learning module or program that learns in two or more domains or areas of interest. Machine learning may involve identifying and learning patterns in existing data to facilitate making predictions about subsequent data. Models may be created based on example inputs to make effective and reliable predictions about new inputs.

[0168] Additionally or alternatively, a machine learning program may be trained by inputting a sample data set or certain data (e.g., images, mobile devices, vehicle telematics, autonomous vehicles, and / or smart home telematics data) into the program. The machine learning program may utilize deep learning algorithms, which may focus primarily on pattern recognition and may be trained after processing multiple instances. The machine learning program may include individual or combined Bayesian program learning (BPL), speech recognition and analysis, image or object recognition, optical property recognition, and / or natural language processing. The machine learning program may also include natural language processing, semantic analysis, automated reasoning, and / or machine learning.

[0169] In supervised machine learning, a processing element may be provided with implementation inputs and their associated outputs, and may seek to discover general rules that map inputs to outputs, so that when subsequent new inputs are provided, the processing element may accurately predict the correct output based on the discovered rules. In unsupervised machine learning, the processing element may be asked to find its own structure in unlabeled example inputs. In one embodiment, machine learning techniques may be used to extract data about build files, finished parts, and build processes from device details, sensors, post-manufacturing inspection information, image data, and / or other data.

[0170] In one embodiment, the processing element may be trained by providing it with a large sample of build files and inspection information.

[0171] Based on these analyses, the processing element can learn how to identify characteristics or patterns, which can then be applied to analyze sensor data, authentication data, image data, device data, and / or other data. For example, the processing element can learn to adjust manufacturing parameters based on specific geometries. The processing element can also learn how to identify different types of geometries that may be difficult for a specific machine based on the differences in the received sensor data. The processing element can also learn how to prevent defects from being introduced into the manufacture of the component. As a result, updated build files suitable for the part, machine, and materials used are provided during or before manufacturing.

[0172] Although specific features of various embodiments of the present disclosure may be shown in some drawings but not in other drawings, this is only for convenience. According to the principles of the present disclosure, any feature of the drawings may be combined with any feature of any other drawings for reference and / or protection.

[0173] This written description uses examples to disclose embodiments, including the best mode, and also to enable persons skilled in the art to practice the embodiments, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the disclosure is defined by the claims, and may include other examples that occur to persons skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.

Claims

1. A computer-based device (302) for dynamically creating or modifying at least a portion of an additive manufacturing build for manufacturing a part (28), the device (302) comprising at least one processor (502) in communication with at least one memory device (504), the device (302) in direct or indirect communication with at least one additive manufacturing machine (10) using one or more build parameters, in, The at least one additive manufacturing machine (10) further comprises: - a computing device (24) and a controller (26), the controller (26) being configured to control one or more components of the at least one additive manufacturing machine (10), - a first scanning device (18) configured to direct energy through a selective portion of the building platform (12) to create the part (28), - an optical system (20) comprising a light detector (38) configured to detect electromagnetic radiation (40) and to generate an electrical signal (44) in response to the electromagnetic radiation, - a second scanning device (42) configured to direct electromagnetic radiation (40) generated by the melt pool (22) to the light detector (38), a feedforward control system (600) configured to dynamically adapt the additive manufacturing of the part (28), wherein the feedforward control computer means (602) is configured to receive real-time sensor information from the sensor (310) and determine updated build parameters for this particular geometry, and to apply the determined updated build parameters to each similar geometry in the build file (210) that remains to be built, wherein the light detector (38) is communicatively coupled to the computing device (24) and is configured to transmit the electrical signal (44) to the computing device (24), wherein the computing device (24) is configured to process the electrical signal (44) from the first light detector (38) using a data processing algorithm to determine the size and temperature of the melt pool (22), compare the size and temperature of the melt pool (22) to an expected or desired melt pool size and temperature based on a calibration model, and generate a control signal (60) that is fed back to the controller (26) and used to adjust one or more build parameters in real time to correct for differences in the melt pool (22); The device (302) is configured to: analyzing a plurality of build information associated with the part (28), wherein a portion of the build information is associated with pre-existing data regarding the part (28), and wherein a portion of the build information is associated with data that is not pre-existing data regarding the part (28); evaluating whether one or more differences between the pre-existing data and the non-pre-existing data will result in a deviation or improvement in the part (28), the additive manufacturing build, or both; and Based on the evaluation of the one or more differences, one or more of the build parameters of the part (28), including the size and temperature of the melt pool (22), are automatically created or modified.

2. The device (302) according to claim 1, wherein: The evaluation includes a virtual control loop or an iterated virtual control loop.

3. The device (302) according to any one of the preceding claims, wherein: The non-pre-existing data and / or the pre-existing data are measurement data.

4. The device (302) according to claim 3, wherein: The non-pre-existing data and / or the pre-existing data is measured during additive manufacturing build of the part (28) by the additive manufacturing machine.

5. The device (302) according to any one of the preceding claims, wherein: The non-pre-existing data is artificial.

6. The device (302) according to any one of the preceding claims, wherein: The device (302) is configured to create or modify one or more of the build parameters of the part (28), at least a portion of the additive manufacturing build, or a combination thereof while the part (28) is being built by the at least one additive manufacturing machine (10).

7. The apparatus (302) of any one of the preceding claims, further comprising a communication component for transmitting one or more of the build parameters of the created or modified part (28), at least a portion of the additive manufacturing build, or a combination thereof to one or more additive manufacturing machines (10).

8. A method for dynamically creating or modifying at least a portion of an additive manufacturing build for manufacturing a part (28), the method being implemented using a computer device (302) according to any one of the preceding claims, the method comprising: analyzing, by the processor (502), a plurality of build information associated with the part (28), wherein a portion of the build information is associated with pre-existing data associated with the part (28), and wherein a portion of the build information is associated with data that is not pre-existing data associated with the part (28); evaluating, by the processor (502), whether one or more differences between the pre-existing data and the non-pre-existing data will result in a deviation or improvement in the part (28), the additive manufacturing build, or both; and Based on the evaluation of the one or more differences, one or more of the build parameters of the part (28), including the size and temperature of the melt pool (22), are automatically created or modified.

9. The method according to claim 8, wherein: The evaluating further includes executing a virtual control loop to repeatedly evaluate whether one or more differences between the pre-existing data and the non-pre-existing data will result in a deviation or improvement in the part (28), the additive manufacturing build, or both.

10. The method according to claim 9, further comprising: The virtual control loop is executed iteratively, wherein each iteration is based on one or more differences of a previous iteration.

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