Identification markings on 3D printed parts

Abstract

A system and method for post-processing a 3D printed part is described. For example, support structures for a 3D printed part can be removed during post-processing. In the system and method, a marker is placed on the 3D printed part or on a support structure attached to the 3D printed part. The marker can be printed when the 3D printed part and support structure are printed by a 3D printer. After printing, the marker can then be sensed to determine one or more cutting paths located between the 3D printed part and the support structure. The 3D printed part can then be autonomously separated from the support structure by cutting through the cutting paths.

Description

Background Technology

[0001] The present invention generally relates to the 3D printing of parts, and more specifically, to markings placed on or support structures attached thereto in 3D printed parts.

[0002] 3D printing technology has enabled the creation of a wide variety of parts, pushing the boundaries of traditional manufacturing and making it possible to manufacture parts that perfectly match application requirements. However, the level of automation in 3D printing remains very limited because many pre- and post-processing steps must be performed manually. As a result, mass production of custom parts can be very costly, primarily due to rigid production systems and the large number of manual steps required for process design and execution. A key element in increasing automation and autonomy in additive manufacturing is enhancing the flow of information between the actual printing of a 3D-printed part and the manufacturing unit that performs further processing of that part.

[0003] 3D printing technology (for metals and plastics) allows for the manufacture of a wide variety of parts with great flexibility. However, these technologies still involve many manual pre- and post-processing steps. The three most significant drawbacks of current processes include long processing times, inconsistent and unreliable quality, and high prices for the produced parts. Furthermore, to obtain a fully functional product, the combination of different materials and parts with 3D-printed components still requires many design and organization-intensive tasks involving substantial manual labor. For example, it remains difficult to use different materials in the 3D printing process for a specific part. Additionally, many manual steps are still required to assemble previously manufactured parts that are more competitively produced using other manufacturing methods such as electro-machining, casting, or milling. Therefore, 3D-printed parts are often moved to another production line or even another location to assemble various 3D-printed and machined parts into the final product.

[0004] It should also be understood that 3D printing of parts requires some type of support structure to hold the part in place while it is being formed by the 3D printing system. After the part is formed by the 3D printing system, various post-processing steps are required to prepare the part. For example, the part must be separated from the support structure before further processing and / or use. Inspection may also be necessary. Several methods can be used to separate the part and the support structure. For example, a fluid can be used to dissolve the support structure away from the part. Alternatively, if the support structure is made of a poorly soluble material, the support structure can be physically cut to separate the component from it. In this case, automated removal of the support structure for the 3D printed part using a machine (e.g., CNC, robot, etc.) typically relies on determining a pre-programmed path trajectory where the cutting tool will cut through the support structure or part. However, this method can result in cuts at undesirable locations and inconsistent cuts between parts due to unpredictable physical deformations (bending, lifting, cracking, etc.) occurring at various geometric locations on the part and support structure. Additional problems arise during automated support structure removal due to the lack of quality checks on the support structure removal process. Due to these issues, post-processing of 3D printed parts is currently a slow and expensive process. Therefore, it is desirable to remove the support structure precisely and consistently from part to part. Summary of the Invention

[0005] A system and method for post-processing 3D-printed parts are provided, which may include removing a support structure from the 3D-printed part. The system and method include printing markings on the 3D-printed part or on the support structure attached to the 3D-printed part while the 3D-printed part and the support structure are being printed by a 3D printer. The markings are then sensed to determine a cutting path between the 3D-printed part and the support structure. The support structure and the 3D-printed part can then be automatically separated from each other by cutting through the cutting path. The invention may also include any other aspects and any combination thereof described in the following written description or drawings. Attached Figure Description

[0006] The invention can be more fully understood by reading the following description in conjunction with the accompanying drawings, in which:

[0007] Figure 1 This is a schematic diagram of a 3D printing system;

[0008] Figure 2 This is a close-up perspective view of a part of a 3D printed component;

[0009] Figure 3 This is a close-up perspective view of another part of the 3D printed component;

[0010] Figure 4 yes Figure 2 Another magnified perspective view of this part of the 3D printed component;

[0011] Figure 5 This is a schematic diagram of another 3D printed part with identification markings;

[0012] Figure 6 This is a flowchart of one embodiment of the support structure removal system;

[0013] Figure 7 This is a flowchart of another embodiment of the support structure removal system;

[0014] Figure 8 This is a schematic diagram of an autonomous method for post-processing 3D printed parts, illustrating information input and learning;

[0015] Figure 9 This is a schematic diagram of the cell arrangement for autonomous post-processing 3D printed parts; and

[0016] Figure 10 This is a flowchart of a method for post-processing 3D printed parts. Detailed Implementation

[0017] One problem with 3D printing technology is that, due to undesirable events occurring during the printing process, such as thermal stress, impurities, and other disturbances, the construction of 3D-printed parts can hardly match the specifications provided in the corresponding CAD drawings precisely. Therefore, a single CAD drawing is insufficient to generate the cutting paths for the machine used to remove the support structure from the part. Thus, the embodiments described herein can be used to record 3D-printed parts and generate cutting paths for separating the part and the support structure using an autonomous vision system. The autonomous vision system can also utilize machine learning or artificial intelligence algorithms.

[0018] The described embodiments also include an information tagging solution for autonomous identification and manufacturing of 3D printed parts. Additive manufacturing technology enables the production of a wide variety of parts based on their CAD designs. Furthermore, several parts with different designs can be printed on the same substrate. Therefore, it is desirable to be able to automatically identify 3D printed parts to automate post-processing. Post-processing of 3D printed parts may require a large amount of part-related information, such as materials, CAD models, support structure designs, quality specifications, etc. Therefore, it is desirable to incorporate tags on the 3D printed parts, support structures, or substrates during the 3D printing process to encode information about the part within the tags to support post-processing activities.

[0019] like Figure 1As shown, the system may have a base 10 on which a 3D printer 14 forms a 3D printed part 12. The 3D printed part is formed on the base 10 and attached to the base 10 by one or more support structures 18, which are attached to the base 10 and the 3D printed part 12 on opposite sides. Preferably, the support structure 18 is formed by the 3D printer 14 when the 3D printed part 12 is formed. Thus, for example, in the case where the 3D printed part 12 is formed of metal by a 3D metal printer 14, the support structure 18 is also metallic. In any case, preferably, the 3D printed part 12 and the support structure 18 are formed of the same material and have the same curing (e.g., plastic part). The support structure 20 may also be printed to support the internal structure of the 3D printed part 12. That is, the holes or cavities 22 of the 3D printed part 12 may have support structures 20 connected to different parts on opposite sides to support the holes or cavities 22 during formation. The bottom support structure 18 is located at... Figure 2 The internal support structure 20 is shown in more detail below. Figure 3 This is shown in more detail below. For example... Figure 4 As shown, the support structure can be composed of a plurality of support members 22, which are spaced apart from each other and parallel to each other. Preferably, at least five support members 20 are equidistant from each other and parallel to each other, or even more preferably, at least ten support members 20 are equidistant from each other and parallel to each other.

[0020] A vision system 24 with a camera 26 (e.g., a 3D camera, laser sensor, or ordinary RGB camera) mounted on the robotic arm 28 can also be provided to capture images of the 3D printed part 12. The vision system 24 can also be mounted in a stationary position or elsewhere on another movable structure if desired. After the 3D printed part 12 has been formed by the 3D printer 14, the vision system 24 captures one or more images of the 3D printed part 12 and the support structures 18, 20. As further described below, the controller 30 then determines the cutting path 56 between the 3D printed part 12 and the support structures 18, 20. Figure 5 A cutting system 32 with a cutting head 34 and a robotic arm 36 is also provided for cutting along a cutting path 56 through the 3D printed part 12 or support structures 18, 20 to separate the 3D printed part 12 from the support structures 18, 20. The cutting system 32 is considered a post-processing system 32, and it should be understood that other post-processing systems 32 are also possible, such as an inspection system 32. Preferably, the cutting of the 3D printed part 12 or support structures 18, 20 involves using a rotary grinder, laser, or reciprocating saw on the cutting head 34. It should be understood that the methods and systems herein can also be used with other processing systems, such as an inspection system, whereby the system learns and tracks the quality of multiple 3D printed parts 12.

[0021] In one embodiment, the determination of the cutting path 56 can be accomplished in two steps. In the first step, the system can utilize offline learning from the CAD model. In the second step, the system can utilize online learning (“Continuing Education”) from quality assessment. It should be understood that offline learning refers to updates that occur before or independently of the actual use of the 3D printed part 12, while online learning refers to updates that occur during or as a result of the actual use of the 3D printed part 12.

[0022] Offline learning may include generating synthetic images from CAD models (e.g., RGB or point clouds) to construct a representative dataset. It should be understood that the synthetic images are data-defined digital images, which can be recognized by the controller 30 and modified to update the synthetic images based on differences identified between the CAD model and captured images of the 3D printed parts 12, or differences identified between two or more 3D printed parts 12. It may be desirable to generate multiple images of parts 12 and support structures 18, 20 from different viewpoints. It may also be desirable to generate images of parts 12 with partially removed support structures 18, 20 from different viewpoints. It may also be desirable to generate images of parts 12 and / or support structures 18, 20 that deviate from the reference CAD model, such as partial separation of part 12 from support structures 18, 20, deformation due to thermal stress, undesirable porosity extending into both parts 12 and support structures 18, 20, or other defects that may be associated with 3D printing. The CAD model can also be used to generate cutting paths 56 visible on the synthesized image, and / or can generate regions to define parts 12 and support structures 18, 20, which can then be used to generate cutting paths 56. The system is preferably trained using machine learning or artificial intelligence techniques to generate cutting paths 56 from the synthesized image. When the system is used online to separate support structures 18, 20 from multiple 3D printed parts 12, the synthesized model can be updated continuously (i.e., based on each 3D printed part 12 and its captured image) or periodically (i.e., based on images captured from multiple 3D printed parts 12). Therefore, the cutting paths 56 determined by the system are adjusted over time as the system updates the synthesized image based on learning that occurs during online use.

[0023] While offline generation of synthetic images may provide reliable results in many cases, it can present challenges with unexpected deformations in some 3D printed parts 12. Therefore, a system that learns from existing experience is preferred. Online learning can begin with the use of offline-generated synthetic images on new 3D printed parts 12. Actual captured images of the 3D printed parts acquired during the cutting process (after the removal of each support structure 18, 20) can then be stored. The most recent image can then be compared with the original CAD model (i.e., the initial synthetic image) as a quality control action. If a difference from the reference CAD model is detected to exceed a threshold, the synthetic image can be updated to adjust the image and the correct cutting path 56. The quality control action can also be used to confirm that the support structures 18, 20 have been completely and adequately removed, or to analyze whether cracks have appeared on the 3D printed part 12 during the removal of the support structures 18, 20. Retraining of the system and updating of the synthetic images can be performed continuously or periodically. After sufficient updates, the system can be expected to become increasingly reliable and eventually be used solely based on visual input from the 3D printed part 12 (i.e., without referencing the CAD model).

[0024] The system acquires input from camera sensor 26 and stores the captured images in computer memory. Using an autonomous computer vision algorithm, a first determination of the cutting path 56 between the 3D printed part 12 and the support structures 18, 20 can be performed. In a second step, the algorithm can group different regions of the support structures 18, 20 into independent regions. For example, it may be desirable to group the base support structure 18 separately from the inner support structure 20, because cutting different support structures 18, 20 may affect the 3D printed part 12 differently. Therefore, it is desirable that each region consists of closely positioned support structures 18, 20 that will be affected by the cutting operation in that region. In each region, the algorithm detects the cutting path 56 between the support structures 18, 20 and the 3D printed part 12, which are separated from other regions. Optionally, the autonomous computer vision algorithm can compare the determined cutting path 56 in each region with the original CAD model. Therefore, when capturing images of the 3D printed part 12, it may be desirable to capture at least two different images of at least two different regions of the 3D printed part 12, where each image has a different region. Then, the two different regions in the synthesized image corresponding to the two different regions of the 3D printed part 12 can be updated based on the two different captured images. The system can also identify and quantify deviations (differences) between the captured images and the CAD model, or between captured images of different 3D printed parts 12. Preferably, as Figure 5As shown, the autonomously generated cutting path 56 is positioned through the support structures 18 and 20 and slightly spaced from the connection point 54 of the 3D printed part 12 to provide a safety margin with the 3D printed part 12. Finally, this set of cutting paths 56 is sent to the cutting system 34 for the actual cutting and separation of the support structures 18 and 20 from the 3D printed part 12.

[0025] The system comprises a set of steps including capturing images of the CAD model and / or the actual 3D printed part 12. Connection points 54 between the support structures 18, 20 and the 3D printed part 12 can then be autonomously determined using an algorithm. The calculated connection points 54 can then be compared with connection points in the original CAD model. Deviations in the captured images can then be identified and quantified. A cutting path 56, passing through the support structures 18, 20 slightly away from the connection points 54 to provide a safety margin, can then be autonomously generated to provide the actual cutting path 56. The cutting path 56 can then be connected to the cutting system 32 that performs the actual cutting. It is also desirable for the vision system to capture additional images of the region of interest by zooming (e.g., optical or digital zoom) within the region of interest where the support structures 18, 20 are attached to the 3D printed part 12. These images can then be used in a learning algorithm to improve and enhance the accuracy of the generated cutting path 56.

[0026] An exemplary flowchart of the system and method described herein is shown in Figure 6As shown in the figure, a synthetic image is initially generated from the CAD model of the part (38). It should also be understood that an initial image can be captured from the actual 3D printed part 12. The initial image of the part is then stored in memory (40). Multiple images can also be used for the initial image. Machine learning or artificial intelligence can also be used with multiple initial images to learn the position of the connection point 54 between the 3D printed part 12 and the support structures 18, 20. In this case, at least some of the multiple initial images will include both the 3D printed part 12 and the support structures 18, 20 attached thereto. A synthetic image can also be formed from multiple initial images and learning. An image of the actual 3D printed part 12 is then captured, where the 3D printed part 12 corresponds to the part (42) of the initial image. Preferably, the captured image is an RGB image, a depth image, a point cloud, or a line scan. If desired, the initial image can also be an RGB image, a depth image, a point cloud, or a line scan. Multiple images of different actual 3D printed parts 12 can also be captured. Machine learning or artificial intelligence can also be used in conjunction with multiple captured images of the actual part 12 to learn the location of the connection point 54 between the 3D printed part 12 and the support structures 18, 20. In this case, each of the multiple captured images will include both the 3D printed part 12 and the support structures 18, 20 attached thereto. A synthetic image can also be formed from the multiple captured images and the learned images. Based on the initial image (44) of the 3D printed part 12 and the captured images, one or more cutting paths 56 (46) are then determined between the 3D printed part 12 and one or more support structures 18, 20 attached to the 3D printed part 12. The cutting path 56 can be determined using a database of multiple initial images and multiple captured images, as well as machine learning or artificial intelligence of the database. The synthetic image (initial image) (48) can then be updated based on the differences between the synthetic image and the captured images. The cutting system 32 can then use the generated cutting path 56 to cut along the cutting path 56 through the 3D printed part 12 or the support structures 18, 20 to separate the 3D printed part 12 from the support structures 18, 20 (50). It should be understood that the described system and method may be implemented in the controller 30 in the form of a non-transitory computer-readable medium, which includes program code that controls the 3D printer 14, vision system 24 and / or cutting system 32 to autonomously execute the described method.

[0027] like Figure 5As shown, one or more markers 52A-C can also be used to determine the cutting path 56 between the 3D printed part 12 and the support structures 18, 20 attached to the 3D printed part 12. As shown, marker 52A can be embedded in the 3D printed part 12, encoding information about the part 12, substrate 10, support structures 18, 20, and / or required post-processing steps and tools. Marker 52B encoding this information can also be embedded in one or more support structures 18, 20. Marker 52C containing this information can also be embedded in the substrate 10. Preferably, when markers 52A and 52B are embedded in the 3D printed part 12 and / or support structures 18, 20, preferably, when the 3D printed part 12 and support structures 18, 20 are printed by the 3D printer 14, the markers 52A and 52B are printed on the 3D printed part 12 and / or support structures 18, 20 by the 3D printer 14. Although the mark 52C on substrate 10 can be embedded or placed separately from the 3D printing process, the mark 52C can also be 3D printed onto substrate 10 by 3D printer 14 during printing, or substrate 10 and mark 52C can be 3D printed together by 3D printer 14 during printing. Marks 52A-C can also be laser engraved. Marks 52A-C enable flexible, reliable, and independent devices to automatically identify 3D printed parts 12 to fully formulate the entire cascade of post-processing activities, resulting in cost-competitive production of customized 3D printed parts 12.

[0028] By embedding markings 52A-C on the 3D printed part 12, support structures 18, 20, or substrate 10 of the 3D printed part 12, information related to the 3D printed part 12 and the required post-processing steps can be made available along with the part 12. Therefore, machines and equipment for post-processing can read reliable information about the actual part 12 to be processed from the markings 52A-C to achieve autonomous post-processing activities. Furthermore, the required equipment, tools, and machines for post-processing a given part 12 may be part-specific. Therefore, the markings 52A-C for the 3D printed part 12 can be used to contain all the information needed to automate the cell process.

[0029] To complete one or more post-processing steps, such as powder removal, removal of support structures 18, 20, quality inspection, or heat treatment for stress relief, a wide range of relevant information about the 3D printed part 12 may be required, such as material, mechanical and electrical properties, quality specifications, support structures 18, 20, and CAD design. Encoding this information associated with each 3D printed part 12 using markings 52A-C allows information to flow from the actual 3D printed part 12 to other machines performing post-processing operations.

[0030] The types of information that can be provided by markings 52A-C include (but are not limited to): the number and location of parts 12 printed on substrate 10; specifications of the 3D printed parts 12, such as materials, mechanical and electrical properties, and mass limitations; cutting tools required for cutting through support structures 18, 20 and their parameterization (e.g., cutting speed, etc.); dimensions and location of support structures 18, 20; limitations on post-processing steps, such as mechanical limitations of the 3D printed parts 12; information on how to remove powder and support structures 18, 20 left from 3D printing; information on heat treatment parameters used in the furnace; and information on how to assemble the 3D printed parts 12 into the final product (e.g., assembly steps). Where markings 52B are provided on support structures 18, 20 for removal of support structures 18, 20, it may be necessary to provide separate markings 52B on individual support structures 18, 20 to provide independent location information for support structures 18, 20, thereby improving accuracy when removing multiple support structures 18, 20.

[0031] Markings 52A-C can be sensed by vision system 24, and controller 30 can determine the position of cutting path 56 based on the sensed markings 52A-C. Cutting system 32 can then cut along cutting path 56 through the 3D printed part 12 or support structures 18, 20 to separate the 3D printed part 12 from the support structures 18, 20. Markings 52A-C can also encode information associated with a specific 3D printed part 12, support structure 18, 20, and post-processing steps. Markings 52A-C are preferably generated during the 3D printing process using the same printer 14 used to print the 3D printed parts 12 and support structures 18, 20. Markings 52A-C can include various features. For example, markings 52A-C can be printed on the 3D printed part 12, on the support structures 18, 20, or on a substrate 10 containing multiple, optionally different, 3D printed parts 12. Markings 52A-C can encode information in a machine-readable format, such as QR codes, barcodes, notches, a series of notches, engravings, or reliefs. Markings 52A-C can encode information about other markings 52A-C or the 3D printed part 12, such as relative distances to other markings 52A-C, relative distances to the 3D printed part 12, relative distances to support structures 18, 20, relative distances to the connection point 54 between the 3D printed part 12 and support structures 18, 20, relative distances to the cutting path 56 between the 3D printed part 12 and support structures 18, 20, or may include the type of connector between the 3D printed part 12 and support structures 18, 20 to support post-processing of the part. Information about the dimensions of the connection point 54 between support structures 18, 20 and the 3D printed part 12 can also be used to calculate the forces required for cutting. Markings 52A-C can be used to indicate critical points on support structures 18, 20. Markings 52A-C can indicate geometric points requiring quality checks and how such checks should be performed. Markers 52A-C can encode the relative position of markers 52A-C to one or more connection points 54 or to one or more cutting paths 56. Encoded positional information or other component 12 information can be explicitly encoded in markers 52A-C, allowing direct reading of said information without reference to another data source. Alternatively, markers 52A-C can encode pointers to information about the 3D printed part 12, such as the relative position of connection points 54 or cutting paths 56. For example, markers 52A-C can encode a unique identifier or URL that allows access to desired information stored in a database (e.g., a website, web service, cloud). The database may also include other post-processing information, such as powder removal, assembly, polishing, etc. Markers 52A-C (especially marker 52A on the 3D printed part 12) can also encode the identity of the 3D printed part 12 (e.g., a generic part number or a specific serial number). Markers 52A-C can also encode information for end-of-life handling of the 3D printed part 12, such as disassembly, recycling, or ownership instructions.

[0032] When the mark 52A is placed on the 3D printed part 12 itself, preferably, the function of the 3D printed part 12 is not affected by the mark 52A. In this case, the information encoded in the mark 52A can be used not only during manufacturing and assembly, but also during disassembly and recycling at the end of the product's life. This can be used to improve recycling efficiency. For example, if the part 12 or the assembled product has direct information stored thereon about how to optimally disassemble, reuse, and / or recycle components, recycling facilities can recycle in a more efficient manner. If necessary, the information stored on the marks 52A-C can also be encrypted when confidentiality is required.

[0033] Figure 7 An exemplary flowchart of the system and method described herein is shown. As shown, the 3D printer 14 can be used to simultaneously 3D print part 12, support structures 18, 20, and one or more markers 52A-C (58). The markers 52A-C can then be sensed by the vision system 24 or other sensing system (60). Based on information obtained from the manufacturer 52A-C, one or more cutting paths 56 (62) are then determined between the 3D printed part 12 and one or more support structures 18, 20 attached to the 3D printed part 12. The cutting system 32 can then use the generated cutting paths 56 to cut through the 3D printed part 12 or support structures 18, 20 to separate the 3D printed part 12 from the support structures 18, 20 (64). It should be understood that the described system and method can be implemented in a controller 30 in the form of a non-transitory computer-readable medium comprising program code that controls the 3D printer 14, vision system 24, and / or cutting system 32 to autonomously perform the described methods.

[0034] Providing autonomous manufacturing cell units to realize digital designs into functional products is also useful. An autonomous manufacturing cell unit can include two components. On one hand, a set of physical equipment and fixtures, such as, but not limited to, 3D printers, lasers, printers, robotic systems, vision and sensor systems, storage systems, quality inspection, conveyors, fixtures, milling or CNC machines. The primary purpose of these devices includes performing a required set of operations, such as printing, polishing, material removal, inspection, or assembly, to physically transform raw materials and components into functional products. On the other hand, a set of intelligent control systems determines and learns processing steps based on existing knowledge of the product (e.g., CAD of parts, assembly plans, etc.) and / or input data from the physical systems. The control system transforms available data into useful information to perform an optimal set of tasks for creating functional products based on user-defined performance metrics such as cost, production time, or number of steps.

[0035] Manufacturing cell units will also be advantageous, autonomously designing production processes and coordinating, executing, controlling, monitoring, and improving processing steps to obtain fully functional products. Such technology will enable the flexible, reliable, and cost-competitive production of customized parts in large volumes with short delivery cycles. One such manufacturing cell unit is described herein, autonomously designing, implementing, controlling, monitoring, and improving these processing steps to obtain a fully functional component, part, assembly, or device that needs to be manufactured and has specific characteristics (e.g., materials, mechanical and electrical properties, functional characteristics, quality specifications, attached components, or surface finishes). On one hand, the manufacturing cell unit includes hardware such as (but not limited to) production, assembly, and processing machines and equipment, as well as robotic systems, transport, storage, and quality control systems. On the other hand, the equipment and machines within the manufacturing cell unit need to collaborate and work together autonomously to achieve customer-defined goals, such as minimum production time or minimum production cost. The available information in the system may include: prior knowledge of components, parts, assemblies, or equipment and their requirements (CAD models, assembly plans, etc.); input data from the physical systems within the manufacturing unit; and process data generated by the unit. Decision-making tasks include (but are not limited to): manufacturing process design, i.e., the process steps required to obtain the product; allocating production steps to different hardware; production sequence; production activities within each hardware; coordinated control of production activities; monitoring processing steps; learning strategies for production steps; and learning optimal process parameters to maximize, for example, final quality.

[0036] Autonomous manufacturing cell units rely on production hardware (i.e., post-processing tools) as described above, such as 3D printers, lasers, robotic systems, CNC machines, storage racks, and conveyors. The automated system considers various available production information within a set of intelligent learning control systems, including prior knowledge of the product (e.g., CAD of parts, assembly plans, material properties) and data generated by the manufacturing cell unit, to optimally achieve customer-defined goals. This provides manufacturing autonomy generated by the intelligent control systems to design and coordinate manufacturing cell activities and improve the production process through learning. The result of these control systems is a set of processing steps and parameters that produce a fully functional product. A set of learning algorithms and methods encoded in the control systems is responsible for: achieving coordination and communication between different machines and equipment; designing and implementing processing steps and learning the most suitable process parameters for each processing step; coordinating data from machines and equipment; integrating available information into a set of processing steps; and the autonomous execution of the production process. The autonomous post-processing of 3D printed parts described herein can also be extended to other manufacturing operations, such as the autonomous assembly of 3D printed parts and many other operations.

[0037] The intelligent control system embedded in the autonomous manufacturing cell unit belongs to either of two main software blocks: (i) the process design and optimization block, or (ii) the process execution and control block. The former (i) analyzes the manufacturing cell components, performs calculations, and refines steps and parameters to optimally produce the desired product using a learning strategy based on selected performance indicators (KPIs). This learning strategy relies on the manufacturing cell description, including available machines and equipment and their specifications, product information (such as the CAD design of the final product), and process measurements generated by process execution. The second block (ii) controls the execution of manufacturing steps and captures data from sensors and equipment to create new information shared with the process design and optimization block. To create a feasible processing strategy and learn the optimal strategy, the following steps can be used: A list of available machines and equipment in the manufacturing cell unit can be read. Performance indicators can also be selected. Product information (CAD files, parts, materials, characteristics, quality requirements, etc.) can also be read. An initial processing strategy based on the available information can be obtained, satisfying cell boundaries and machine / equipment capabilities, including the required processing steps, sequencing, and machine / equipment parameters. A learning algorithm can be invoked to set up components to be improved and create a representative set of production sequences (training set) to be run in a manufacturing cell unit. Based on the training set provided by the learning algorithm, processing steps can be executed multiple times to collect process measurements from the manufacturing cell unit. Feedback from a continuous quality control system that monitors quality during the build process can also be included. Self-correcting actions can also be included during process execution through the control system to avoid accidents. Taking into account the production boundaries of the manufacturing cell, CAD constraints, product specifications, and selected performance metrics (cost, energy, aging, weight, etc.), the learning algorithm can be solved to obtain generalizations from observed measurements regarding the optimal processing sequence and processing parameters. The results can be refined by repeating the above steps using the learning parameters obtained during the corresponding invocations of the learning algorithm. In subsequent runs of the processing strategy, the learning algorithm can employ different processing tools to better meet the set performance metrics. The learning algorithm can also be invoked to understand the variability caused by varying input parameters by (a) using the solution information obtained from the last invocation of the execution, and (b) by making multiple invocations of a generalized strategy using classification and regression techniques. Figure 8 The diagram illustrates the use of feedback input, learning, execution, and adaptation.

[0038] The task of the 3D printer in the device is to produce most (e.g., 90%) of the individual parts for the final product. Once a part is 3D printed, this information is sent to the production module, which processes it and sends instructions to the robotic system on how to remove the substrate with the 3D printed part on it. The system then autonomously performs post-processing tasks on the part, such as removing support structures, machining surface edges, polishing, heat treatment, and quality inspection.

[0039] The post-processing can begin with a camera recording the 3D-printed part on the substrate. These images are processed by a production block algorithm that distinguishes the support structure from the main 3D-printed part. Based on this information, connection points between the support and the 3D-printed part can be generated, and the trajectory the robot will follow later when removing the support structure can be represented. To achieve this, the production block algorithm plans the removal and sends instructions to the robot system regarding which tools to select (e.g., laser cutting and / or milling tools depending on the material to be removed) and the type of brush to use later for fine surface polishing. Initially, the production module can have presets with different options, and then over time it can learn to select which tools based on experience with support structure removal. During this process, the robot system can use its vision and laser scanner capabilities to record the 3D-printed part and compare the dimensions of the actual 3D-printed part with the CAD drawing to verify the quality of the printed part.

[0040] Following post-processing, the same or another robotic system, or a combination of both, can replace the components and begin assembling the different parts (e.g., made of different materials) into the final product. Here, an assembly block algorithm can be used to control and plan the assembly process. The first input can be taken from the CAD design of the components and the product. The assembly block algorithm can then calculate the steps and identify the tools required for each step of the assembly. The robot can receive this information and begin execution. Furthermore, the robotic system can use its vision capabilities to receive information about the product and its working environment in real time and send this information to the assembly block to update the assembly plan. Input can be provided by cameras and other sensors, such as acoustic measurements, laser measurements, X-rays, etc. The assembly block preferably autonomously determines the order in which the individual components are processed to obtain the final product.

[0041] Therefore, the entire production line can include machines organized into a cell structure. Each cell is equipped with a set of tools (robots, printers, or furnaces plus processing tools such as lasers, grinders, etc., or assembly tools such as clamps and wrenches) and sensors. This organization offers greater flexibility, priority-based production, scalability, and robustness to unplanned downtime of individual cells.

[0042] The cell can perform the tasks required to build a product in the following ways: Additive manufacturing cells can be used to process different materials that can be used anywhere in the product. Conventional production equipment capable of using production tools created by tool printers as needed can be used. Inventory of commodity parts (e.g., screws, etc.) that are not product-specific and are used in any part or component can be used. Storage systems can be used for intermediate parts produced. Systems can be used for transporting parts (e.g., mobile robots, overlay robots, drones, etc.). Robotic cells can be used to assemble all parts together into a finished product. Quality inspection cells can be used to perform various inspections (e.g., surface inspection, tomography, functional testing) on ​​individual parts or assembled products. Figure 9 Examples of unit devices are shown. As shown in one unit device 70, a production unit 78 performing 3D printing, processing, or assembly may have robotic units 72 distributed therein. The robotic units 72 can perform the assembly, processing, inspection, or movement of parts between different production units 78 for multiple 3D printed parts. In another unit device 74, a robotic conveyor 76 may also be used to move parts between production units 7 if needed.

[0043] Figure 10An exemplary flowchart of the system and method described herein is shown. In the first two steps, a user-defined performance metric (80) and a list of available post-processing tools (82) are read from one or more computer memories. The user-defined performance metric may be the performance of post-processing multiple 3D printed parts 12 into finished or partially finished parts, and may include cost, production time, or number of steps. The available post-processing tools may have different processing capabilities and may include reduction manufacturing methods, such as lasers or grinders, and wrenches or any of the tools, machines, etc., described above. The list of available post-processing tools may also include post-processing tools from different production cells 78. For example, the cell assembly may have production cells 78 distributed by robotic cells 72. The robotic cells 72 may perform post-processing steps, such as assembling multiple 3D printed parts 12 together, or may move 3D printed parts between different production cells 78. A conveyor 76 may also move 3D printed parts 12 between production cells 78. The production cell 78 may include one or more 3D printers 14 for printing 3D printed parts 12, and may also include CNC machines or other production equipment as described above. After reading the user-defined performance metrics and a list of available post-processing tools, a first post-processing strategy (84) can be generated in one or more computer processors using the user-defined performance metrics and the list of available post-processing tools. The first post-processing strategy may include computer instructions for operating a first subset of the post-processing tools. The first 3D printed part 12 can then be printed using one or more 3D printers 14 (86). It should be understood that the post-processing strategy can be generated after printing the 3D printed part 12 if necessary. The first 3D printed part 12 is then post-processed using the first post-processing strategy and the first subset of the post-processing tools (88). Post-processing may include removing support structures 18, 20 printed with the 3D printed part 12 or any other post-processing processes described above. For example, removing support structures 18, 20 may include milling, laser cutting, or sawing through the 3D printed part 12 or support structures 18, 20. Support structure 18 may be attached to a base 10 on which the 3D printed part 12 is formed. Then, in response to the post-processing of the first 3D printed part 12, a first performance metric is determined using one or more sensors corresponding to a user-defined performance metric (90). The system can then learn from the difference between the user-defined performance metric and the first performance metric to improve future post-processing strategies (92). For example, a second post-processing strategy can then be generated in one or more computer processors using the user-defined performance metric, the first performance metric, and a list of available post-processing tools (92, 84). The second post-processing strategy may include computer instructions for operating a first subset or a second subset of the post-processing tools, and the second post-processing strategy may differ from the first post-processing strategy.The second post-processing strategy may include computer instructions for operating a second subset of post-processing tools, wherein the first and second subsets of post-processing tools include different post-processing tools. The first and second post-processing strategies may also include quality checks on the first and second 3D printed parts 12. For example, the quality check may compare the 3D printed part 12 with a CAD drawing. The second 3D printed part 12 can then be printed using one or more 3D printers 14 (86), and post-processing may be performed using the second post-processing strategy and the first or second subset of post-processing tools (88). Then, in response to the post-processing of the second 3D printed part 12, a second performance metric may be determined using one or more sensors corresponding to user-defined performance indicators (90). The first and second performance metrics are then compared in one or more computer processors to determine the level of improvement between the first and second performance metrics, and this learning and improvement occurs with each iteration if necessary (92). Preferably, the entire method operates autonomously.

[0044] While preferred embodiments of the invention have been described, it should be understood that the invention is not limited thereto and modifications can be made without departing from the invention. Although each embodiment described herein may relate only to certain features and may not specifically relate to every feature described with respect to other embodiments, it should be recognized that the features described herein are interchangeable unless otherwise described, even without reference to specific features. It should also be understood that the foregoing advantages are not necessarily the only advantages of the invention, and it is not necessarily contemplated that each embodiment of the invention will achieve all of the described advantages. The scope of the invention is defined by the appended claims, and all apparatuses and methods falling within the meaning of the claims, whether literal or equivalent, are intended to be included therein.

Claims

1. A method for post-processing 3D printed parts (12), comprising: Marks (52) are printed on the 3D printed parts (12) or the support structures (18, 20) using a 3D printer (14) for forming the 3D printed parts (12) and the support structures (18, 20). Sensing the markers (52) and (60), and based on the markers (52) and (62), determining the cutting path (56) between the 3D printed part (12) and the support structure (18, 20) attached to the 3D printed part (12); and The cutting path is output to the cutting system (32) for cutting along the cutting path through the 3D printed part (12) or the support structure (18, 20) to separate the 3D printed part (12) from the support structure (18, 20) (64). The position of the connection point (54) between the support structure (18, 20) and the 3D printed part (12) relative to the mark (52) or the position of the cutting path (56) relative to the mark (52) is encoded in the mark (52).

2. The method according to claim 1, wherein the position of the connection point (54) between the support structure (18, 20) and the 3D printed part (12) relative to the mark (52) or the position of the cutting path (56) relative to the mark (52) is explicitly encoded in the mark (52).

3. The method of claim 1, wherein a pointer to a database is encoded in the tag (52), the database comprising: The position of the connection point (54) between the support structure (18, 20) and the 3D printed part (12) relative to the mark (52) or the position of the cutting path (56) relative to the mark (52).

4. The method according to any one of claims 1-3, wherein the mark (52) is a barcode or a QR code, and / or wherein the mark (52) is printed on the 3D printed part (12).

5. The method according to any one of claims 1-3, wherein the identity of the 3D printed part (12) is further encoded in the mark (52).

6. The method according to any one of claims 1-3, wherein the cutting path (56) passes through the support structure (18, 20) and is spaced apart from the 3D printed part (12).

7. The method according to any one of claims 1-3, wherein the support structure (18) is attached to the base (10), the 3D printed part (12) is formed on the base (10), or wherein the support structure (20) is attached to different portions of the 3D printed part (12) on opposite sides.

8. The method according to any one of claims 1-3, further comprising a plurality of said support structures (18, 20), wherein one of the marks (52) is printed on each support structure (18, 20).

9. The method according to any one of claims 1-3, wherein the cutting comprises milling, laser cutting or sawing through the 3D printed part (12) or the support structure (18, 20).

10. The method according to any one of claims 1-3, wherein both the 3D printed part (12) and the support structure (18, 20) are metal.

11. The method according to any one of claims 1-3, wherein both the 3D printed part (12) and the support structure (18, 20) are made of the same material having the same curing properties.

12. The method according to any one of claims 1-3, wherein the support structure (18, 20) comprises a plurality of support members (22) spaced apart from and parallel to each other.

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