Build line prediction system and method for additive manufacturing parts

Abstract

A method for predicting build line locations in a part prior to additive manufacturing of the part includes: obtaining a sliced three-dimensional model of the part for additive manufacturing; generating a face count difference for each adjacent layer pair of the plurality of layers; for each adjacent layer pair, generating a surface area difference; based on a determination that the surface count difference is less than zero and the surface area difference is greater than zero, predicting, for each adjacent layer pair, a presence of a first layer in each adjacent layer pair including a build line; storing a predicted build line layer list, the predicted build line layer list including one or more layers predicted to include the presence of the build line; and adjusting a size of the part for additive manufacturing, the size corresponding to a layer in the predicted build line layer list.

Description

Technical Field

[0001] This specification generally relates to systems, methods, and computer program products for generating in-plane offsets in additive manufacturing. Background Technology

[0002] Additive manufacturing (AM) processes are used to create sophisticated three-dimensional parts based on digital models. These parts are manufactured using additive processes, in which continuous layers of material are bonded together, one after another, on a build plate within an additive manufacturing machine (AMM). Additive manufacturing processes include powder bed melting, binder jetting, direct energy deposition, material extrusion, material jetting, sheet lamination, and photopolymerization.

[0003] Digital models manufactured via additive manufacturing processes are subject to thermal, mechanical, and / or printing effects, resulting in manufactured parts with geometries that differ from the nominal geometry of the digital model. In other words, build-up lines, deformations, cracks, etc., may appear in the manufactured parts. There has always been a desire to mitigate and eliminate the negative impacts generated during additive manufacturing of parts. Attached Figure Description

[0004] The embodiments illustrated in the accompanying drawings are illustrative and exemplary in nature and are not intended to limit this disclosure. The following detailed description of the illustrative embodiments will be understood when read in conjunction with the following drawings, wherein similar structures are indicated by similar reference numerals.

[0005] Figure 1 An illustrative block diagram depicts the process of constructing line prediction.

[0006] Figure 2 An illustrative 3D model generated by the slicer module and the corresponding illustrative slice model are depicted.

[0007] Figure 3 An illustrative cross-section of the 3D model and a pair of layers analyzed by the construction line prediction process are depicted.

[0008] Figure 4 An illustrative cross-section of the 3D model and another pair of layers analyzed by the construction line prediction process are depicted.

[0009] Figure 5 An illustrative example of ray-shooting analysis is depicted for predicting the location of construction lines using prediction layers of a 3D model.

[0010] Figure 6 An example method is described for predicting build lines in a 3D model of a part for additive manufacturing.

[0011] Figure 7 An example device configured to perform the build-line prediction process as described herein is depicted. Detailed Implementation

[0012] This disclosure relates to techniques for predicting the occurrence and location of build lines based on models of parts used in additive manufacturing. Build lines are one of many types of additive manufacturing variations that can occur in additively manufactured parts. Current processes for determining whether a build line will form due to additive manufacturing, and more specifically, where a build line occurs, involve manufacturing a test part and observing the results of that manufacturing (e.g., the formation of a build line in the test part). Current processes are costly, time-consuming, and error-prone. Therefore, the aspects described herein provide technical solutions for predicting the presence of build lines and, in some respects, for predicting the location of build lines. These technical solutions offer several technical benefits for improving the additive manufacturing of parts. These benefits include reducing or eliminating the need for prototype parts to determine the presence and location of build lines. Furthermore, the technical benefits include providing a tool capable of predicting the location of build lines, which can guide compensatory tools used to adjust build parameters and even design features of parts used in additive manufacturing. The build line tool described herein provides an effective and efficient mechanism for evaluating design changes to eliminate build lines. If build-up lines cannot be designed by adjusting the part design, the build-up line tool described herein provides guidance for simulation and measurement-based compensation tools to refine compensation parameters in the build-up line region, for example, by adjusting tool paths, the energy used to cure and form the part, and similar build parameters, thereby achieving more accurate compensation.

[0013] Reference will now be made in detail to embodiments of this disclosure, examples of which are shown in the accompanying drawings. Wherever possible, similar reference numerals will be used to refer to similar parts or components.

[0014] Additive manufacturing machines (AMMs) or additive manufacturing equipment (AMAs) include rapid prototyping, rapid manufacturing apparatuses, or additive manufacturing devices, such as binder jetting additive manufacturing, fused filament fabrication (FFF), fused deposition modeling (FDM), stereolithography (SLA), digital light processing (DLP), selective laser sintering (SLS), selective laser melting (SLM), laminated object fabrication (LOM), electron beam melting (EBM), etc. Generally, AMMs include build planes (BP, Figure 5 A working material is deposited on the build plane, and an energy source, such as a laser, heat source, ultraviolet (UV) light, or other type of directional energy source, is applied to the working material to bond or transform it into a rigid material. This is achieved by depositing a working material in a build direction (Z-axis, ) that is typically perpendicular to the build plane BP. Figure 5 The process involves adding layer after layer of working material to the previous layer to repeat the process. However, this is just a general example of the additive manufacturing process, as there are many different combinations of working materials, adhesives, and / or energy sources used for additively manufactured parts.

[0015] This document illustrates and describes various aspects of systems, methods, and computer program products for predicting the presence and location of build lines in computer-modeled parts prior to additive manufacturing. In the accompanying drawings, similar figures refer to similar structures.

[0016] Figure 1 An illustrative block diagram depicting the process of constructing line prediction is provided. (Reference) Figure 2-5 The illustrative 3D model, sliced ​​model, and annotation layers depicted in the diagram describe the line prediction process. At box 110, the line prediction process yields a 3D model (e.g., 3D model 210). Figure 2 The 3D model is prepared for additive manufacturing. Preparation for additive manufacturing may include calling slicer module 122 at box 120.

[0017] The Additive Manufacturing Machine (AMM) operates based on a set of control commands, typically generated by a slicing tool. The slicer module 122 receives a 3D model of the part, for example, as a stereolithography (STL) file and / or other 3D model files, and applies a slicing algorithm that divides the 3D model of the part into multiple build layers of predefined thicknesses based on the geometry of the part and the AMM used to manufacture it. The slicer module 122 generates a sliced ​​model 130 (e.g., sliced ​​model 230) at frame 120. The sliced ​​model 230 includes multiple layers 280 stacked in the build direction. Each of the multiple layers 280 has a layer defined relative to the build plane BP (…). Figure 5 The thickness, depth, and width of the parallel plane. Furthermore, in some respects, the slicer module 122 generates a set of additive manufacturing machine control commands at box 120, for example, embodied in a g-code file that defines a series of commands and associated values ​​for various parts of the AMM to operate and manufacture the part.

[0018] The construction line prediction process implements the construction line analyzer module 140, which references... Figure 2-7 Detailed Description. The construction line analyzer module 140 can be a paired layer analysis process. Paired refers to analyzing a pair of adjacent (also called neighboring) layers among multiple layers constituting a slice model. The construction line analyzer module includes querying the slice geometry of the layers based on the number of faces between the paired layers and variations in the scanned area to identify regions at risk of forming construction lines. Aspects of the construction line analyzer module may also include ray-firing analysis to determine the location and characteristics of components leading to construction lines. The construction line analyzer module may also include determining the relative intensity values ​​of the predicted construction lines.

[0019] Figure 2An illustrative 3D model 210 and a corresponding illustrative slice model 230 generated by the slicer module 122 are depicted. The slice model 230 includes multiple slice layers 280. Figure 3 and Figure 4 Each depicts a single pair of adjacent layers analyzed by the construction line analyzer module 140. Figure 3 The image depicts a cross-section 312 of the 3D model 310. This cross-section depicts a ZY slice of the 3D model 310. The first layer 381 and the second layer 382 include adjacent layer pairs queried by the construction line analyzer module 140. For context, a 3D model 381a depicting the first layer 381 and a 3D model 382a depicting the second layer 382 are shown. The construction line analyzer module 140 queries the surface 381b of the first layer 381 and the surface 382b of the second layer 382.

[0020] For example, a query in the line analyzer module 140 includes determining the face count for each of a pair of adjacent layers. A face refers to a closed-loop shape. The process for determining the faces and subsequently counting the total number of faces may include tracing the periphery of features present on the XY plane defining surfaces 381b and 382b. For each instance where the periphery trajectories intersect, a closed-loop shape is identified. For example, surface 381b of the first layer 381 includes four faces: first face 393a, second face 393b, third face 393c, and fourth face 393d. Similarly, surface 382b of the second layer 382 includes two faces: first face 394a and second face 394b.

[0021] The query in the line analyzer module 140 also includes determining the surface area of ​​each of the pairs of adjacent layers. Surface area is the sum of the surface areas of each face of each of the pairs of adjacent layers relative to the XY plane. For example, the surface area of ​​the first layer 381 is the sum of the surface areas of its four faces: face 393a, face 393b, face 393c, and face 393d. For the second layer 382, ​​the surface area is the sum of the surface areas of its two faces: face 394a and face 394b. In this example, the surface area of ​​the first layer 381 is smaller than the surface area of ​​the second layer 382.

[0022] After the build line analyzer module 140 queries the first layer 381 and the second layer 382, the build line analyzer module 140 predicts whether a build line will exist in the top layer of the pair of adjacent layers being queried. The prediction process includes comparing the face count of each layer in the pair of adjacent layers and the surface area of each layer in the pair of adjacent layers. When the difference in face count (ΔF) between the second layer 382 (the nth layer) and the first layer 381 (the n - 1th layer) is less than a predetermined face count value and the difference in surface area (ΔS) between the second layer 382 and the first layer 381 is greater than a predetermined surface area value, a build line is predicted in the second layer 384 (e.g., the top layer of the pair of adjacent layers being queried). For example, the predetermined face count value (FCV) can be 0, 1, , 4 or another predefined value. The predetermined surface area value (SAV) can be 0, 1, , 4 or another predefined value. Thus, when ΔF < FCV and ΔS > SAV, a build line in the nth layer can be predicted, where and

[0023] In<( Figure 3 the example depicted, the build line analyzer module 140 determines that the difference in face count is less than zero and the difference in surface area is greater than 0. When the predetermined value is 0, the build line analyzer module 140 predicts that a build line may exist in the second layer 382 (the nth layer).

[0024] The build line analyzer module 140 continues to query each pair of layers in the entire slice model 230. Figure 4 A cross - section 312 of the 3D model 310 is depicted again. This cross - section depicts a Z - Y slice of the 3D model 310. The first layer <(

[0025] The query by the build line analyzer module 140 includes determining the face count of each layer in the pair of adjacent layers. For example, the surface 481b of the first layer 481 includes two faces: a first face 493a and a second face 493b. Similarly, the surface 482b of the second layer 482 includes one face: a first face 494a.

[0026] The query of the line analyzer module 140 then determines the surface area of ​​each of the pair of adjacent layers. Surface area is the sum of the surface areas of each face of each of the pair of adjacent layers relative to the XY plane. For example, the surface area of ​​the first layer 481 is the sum of the surface areas of its two faces: first face 493a and second face 493b. For the second layer 482, the surface area is the surface area of ​​first face 494a. In this example, the surface area of ​​the first layer 481 is less than the surface area of ​​the second layer 482.

[0027] exist Figure 4 In the depicted example, the construction line analyzer module 140 determines that the difference in face counts is less than zero and the difference in surface areas is greater than 0. When the predetermined value is 0, the construction line analyzer module 140 predicts that a construction line may exist in the second layer 482 (the nth layer).

[0028] The construction line analyzer module 140 may also include a process for predicting the position of the construction line within a prediction layer of the 3D model. The process for predicting the position utilizes ray-shooting analysis. Figure 5 An illustrative example of ray-shooting analysis is depicted for predicting the location of construction lines using prediction layers of a 3D model.

[0029] Figure 5 A cross section 312 of the 3D model 310 on the virtual build plate (BP) is depicted. Ray casting analysis involves projecting an array of rays extending vertically upward from the virtual build plate onto the sliced ​​3D model. Figure 5 Several illustrative rays extending from the construction plate are depicted. For example... Figure 5 As shown, the rays in the multiple rays with solid circles represent the intersections with the 3D model (e.g., rays 501, 502, 503). Figure 3 Rays 504, 505, 506, 507, and 508 (e.g., rays 504, 505, 506, 507, and 508) indicate that they do not intersect with the 3D model or at least have no intersection points. It should be understood that... Figure 5 Compared to the few example rays depicted, more rays can be projected through ray shooting analysis.

[0030] exist Figure 5 In the middle layer, rays 501 and 502 intersect with the second layer 382, ​​which is predicted by the construction line analysis module 104 to have a construction line. Similarly, rays 510 and 512 intersect with the second layer 482, which is also predicted by the construction line analysis module 104 to have a construction line. (Reference) Figure 3 and Figure 4 The ray intersects with the layer predicted to have a building line, and the intersection location provides a more accurate prediction and indication of the location where the layer will have building line features. For example, in Figure 3In the diagram, the surface 382b of the second layer 382 depicts the intersection of rays 501, 502, and 503. These locations are where construction lines may appear within the layer. These locations can be quantified using the X, Y, and Z positions of the 3D model. For example, in Figure 4 In the illustration of surface 482b of the second layer 482, the intersections of rays 510, 512 and other rays not shown but depicted by the dashed loop shown on surface 482b are depicted. These locations are also where construction lines may appear within the layer.

[0031] The construction line analyzer module 140 can also determine the relative strength of the construction line. The relative strength of the construction line can be based on the minimum wall thickness within a pair of adjacent layers being queried, and the nth and (n-1)th layers (e.g., ...). Figure 3 The second layer 382 and the first layer 381, and Figure 4 The difference (ΔS) in surface area between the second layer 482 and the first layer 481. For example, the line analyzer module 140 can determine the minimum wall thickness of each face within the layer, and then determine which of the groups has the minimum thickness value. For example, as Figure 3 As shown, the construction line analyzer module 140 can determine the following portions (T1, T2, T3, T4) as the minimum wall thickness with each corresponding face. Then, the minimum wall thickness of this group can be determined as the portion (T3) corresponding to the second face 394b.

[0032] The relative intensity (RI) of the predicted constructive line (BL) in the second layer 382 is determined by the following function: Where T i =T3.

[0033] For example, the line analyzer module 140 can determine the minimum wall thickness of each face within a layer, and then determine which of the groups has the minimum thickness value. For example, as Figure 4 As shown, the build line analyzer module 140 can determine the following portions (T5, T6, T7) as having the minimum wall thickness for each corresponding face. Then, the minimum wall thickness of this group can be determined as the portion (T5) corresponding to the second face 493b. The relative intensity (RI) of the predicted build line (BL) in the second layer 482 is determined by the following function: Where T i =T5.

[0034] Return to reference Figure 1At box 150, the buildline prediction process outputs the predictions made by the buildline analyzer module 140 as a buildline report. The buildline report may include the predicted layer of the buildline, the coordinates (e.g., X, Y, Z) of the buildline against the background of the 3D model, where Z represents the height value from the base or build plate of the 3D model, and / or the relative intensity of the buildline. For example, at boxes 110 or 170, the buildline report may be provided to a computer-aided drafting program, where information from the buildline report is used to adjust the 3D model, modify toolpaths, etc., to reduce or eliminate one or more predicted buildlines. For example, at box 170, a computer-aided drafting program or similar design program may implement an adjustment tool module 172. The adjustment tool module 172 may include automatic or manual design tools and / or automatic or manual compensation tools. Automatic or manual design tools and / or automatic or manual compensation tools can be configured to receive build line reports from box 150 and automatically adjust the 3D model to change design parameters such as part dimensions, part orientation (e.g., tilt or rotation relative to the build plate), tool paths, etc. For example, this adjustment can change the face count and / or surface area of ​​each layer, such as the predicted build line layer or one or more adjacent layers of the corresponding 3D model.

[0035] Once the 3D model is designed to have reduced or no predicted build line opportunities, it can be processed at box 120 for manufacturing, and then loaded into the AMM at box 160 for manufacturing.

[0036] Example method for predicting build line positions in parts used for AM

[0037] Figure 6 An example method is described for predicting build lines in a 3D model of a part for additive manufacturing.

[0038] In this example, method 600 begins at step 602, obtaining a sliced ​​3D model of the part for additive manufacturing, wherein the sliced ​​3D model comprises multiple layers stacked in a build direction extending from a virtual build plate, and each of the multiple layers includes a predefined height. For example, step 602 may be referenced as follows: Figure 7 The aforementioned device 700 is executed.

[0039] Then, method 600 continues to step 604, whereby for each pair of adjacent layers in the plurality of layers, a face count difference is generated, wherein the face count difference is the difference between the first face count of the first layer and the second face count of the preceding layer, and the preceding layer is closer to the virtual build board than the first layer. For example, step 604 can be referenced as follows. Figure 7 The aforementioned device 700 is executed.

[0040] Then, method 600 continues to step 606, whereby for each pair of adjacent layers, a surface area difference is generated, where the surface area difference is the difference between the first surface area of ​​the first layer and the surface area of ​​the preceding layer. For example, step 606 can be referenced as follows. Figure 7 The aforementioned device 700 is executed.

[0041] Then, method 600 continues to step 608, based on the determination that the face count difference is less than zero and the surface area difference is greater than zero, predicting for each adjacent layer pair that the first layer in each adjacent layer pair includes the presence of a construction line. For example, step 608 can be referenced as follows. Figure 7 The aforementioned device 700 is executed.

[0042] Then, method 600 continues to step 610, which projects an array of rays extending vertically upward from the virtual building plate onto the sliced ​​3D model. For example, step 610 can be referenced as follows. Figure 7 The aforementioned device 700 is executed.

[0043] Then, method 600 continues to step 612, identifying one or more layers from the predicted list of build-line layers, with the rays in the array first intersecting the one or more layers. For example, step 612 can be referenced as follows. Figure 7 The aforementioned device 700 is executed.

[0044] Then, method 600 proceeds to step 614, determining the location where rays intersect in the array of one or more identified layer rays, where the location is defined by three-dimensional coordinates. For example, step 614 can be referenced as follows: Figure 7 The aforementioned device 700 is executed.

[0045] Then, method 600 continues to step 616, storing in one or more memories a predicted list of build-line layers, the predicted list of build-line layers including one or more layers predicted to include the presence of a build-line. For example, step 616 can be referenced as follows. Figure 7 The aforementioned device 700 is executed.

[0046] Method 600 continues to step 618, adjusting at least one dimension of the part used for additive manufacturing, the at least one dimension corresponding to a layer in the predicted build line layer list. For example, step 618 can be referenced as follows. Figure 7 The device 700 performs this step. Step 618 can be performed using the adjustment tool module 172 as described herein or another module configured to receive build-line reports and automatically adjust the 3D model to change design parameters such as part dimensions, toolpaths, etc. For example, the adjustment can change the face count and / or surface area of ​​each layer, such as predicted build-line layers or one or more adjacent layers of the corresponding 3D model.

[0047] In some aspects, the method also includes, for each layer in the predicted list of build line layers, determining a portion with a minimum wall thickness, and determining a thickness value for the portion with the minimum wall thickness.

[0048] In some respects, the method also includes determining the relative intensity for each layer in the predicted list of build-line layers based on the ratio of surface area to thickness values.

[0049] In some respects, methods for obtaining sliced ​​3D models include receiving a 3D model of a part for additive manufacturing and slicing the 3D model into multiple layers along the additive manufacturing build direction.

[0050] In some aspects, the method also includes determining the face count of each of the multiple layers, wherein the face count is the number of faces defined by the closed-loop shape of the layers.

[0051] In some respects, the list of predicted build line layers stored in one or more memories is limited by a height value.

[0052] Example device for predicting build-up lines

[0053] Figure 7 An example device 700 configured to perform the methods described herein is depicted.

[0054] Device 700 includes one or more processors 702. Typically, processor 702 may be configured to execute computer-executable instructions (e.g., software code) to perform various functions as described herein.

[0055] Device 700 also includes network interface 704, which typically provides data access to any type of data network, including personal area networks (PANs), local area networks (LANs), wide area networks (WANs), the Internet, etc.

[0056] Device 700 also includes inputs and outputs 706, which typically provide means for providing data to and from device 700, such as via connections to peripheral devices of a computing device, including user interface peripheral devices.

[0057] The device 700 also includes a memory 710, which is configured to store various types of components and data.

[0058] In this example, the memory 710 includes an acquisition component 721, a surface counting component 722, a surface area generating component 723, a prediction component 724, a projection component 725, an identification component 726, a determination component 727, a storage component 728, and an adjustment component 729.

[0059] Component 721 is configured to execute reference. Figure 6 Step 602 of the method of depiction and description 600.

[0060] The face counting component 722 is configured to perform the reference. Figure 6 Step 604 of the method of depiction and description 600.

[0061] Surface area generation component 723 is configured as an execution reference. Figure 6 Step 606 of the method of depiction and description 600.

[0062] Prediction component 724 is configured as an execution reference. Figure 6 Step 608 of the method of depiction and description 600.

[0063] Projection component 725 is configured as an execution reference. Figure 6 Step 610 of the method of depiction and description 600.

[0064] Identification component 726 is configured to perform reference Figure 6 Step 612 of the method of depiction and description 600.

[0065] Determine that component 727 is configured to perform reference. Figure 6 Step 614 of the method of depiction and description 600.

[0066] Storage component 728 is configured as an execution reference. Figure 6 Step 616 of the method of depiction and description 600.

[0067] Adjustment component 729 is configured to perform reference. Figure 6 Step 618 of the method of depiction and description 600.

[0068] In this example, the memory 710 also includes 3D model data 740, slice model data 741, face count data 742, surface area data 743, construction line prediction data 744, construction line location data 745, relative intensity data 746, and layer data 747 as described herein.

[0069] Device 700 can be implemented in various ways. For example, device 700 can be implemented in a field, remotely, or in a cloud-based processing facility.

[0070] Device 700 is just an example, and other configurations are possible. For example, in alternative embodiments, aspects described regarding device 700 may be omitted, added, or replaced as alternative aspects.

[0071] The aspects described herein provide a technical solution to the technical problems associated with build lines from additive manufacturing. In particular, determining the location of build lines is currently a manual and time-consuming process involving prototyping and manual observation of the build lines. However, the methods described herein and systems configured to implement these methods provide a technical solution for predicting the presence and location of build lines. Therefore, the aspects described herein offer technical benefits, including reducing or eliminating the need for prototype parts to determine the presence and location of build lines. Furthermore, the technical benefits include providing a tool capable of predicting the location of build lines, which can guide compensation tools used to adjust build parameters and even design features of parts for additive manufacturing. The build line tool described herein provides an effective and efficient mechanism for evaluating design changes to eliminate build lines. If build lines cannot be designed in by adjusting the part design, the build line tool described herein guides simulation- and measurement-based compensation tools to refine compensation parameters for the build line region, for example, by adjusting tool paths, the energy used for curing and forming the part, and similar build parameters, thereby achieving more accurate compensation.

[0072] While specific embodiments have been shown and described herein, it should be understood that various other changes and modifications may be made without departing from the spirit and scope of the claimed subject matter. Furthermore, although various aspects of the claimed subject matter have been described herein, these aspects need not be used in combination. Therefore, the appended claims are intended to cover all such changes and modifications falling within the scope of the claimed subject matter.

[0073] Example items

[0074] Implementation examples are described in the following numbered entries:

[0075] Clause 1: An apparatus configured to predict the location of build lines in an additively manufactured part prior to such prediction, comprising: one or more memories; and one or more processors coupled to the one or more memories, configured to cause the apparatus to: obtain a sliced ​​3D model of the part for additive manufacturing, wherein the sliced ​​3D model comprises a plurality of layers stacked in a build direction extending from a virtual build board, and each of the plurality of layers comprises a predefined height; for each adjacent pair of the plurality of layers, generate a face count difference, wherein the face count difference is the difference between a first face count of a first layer and a second face count of a preceding layer, and the preceding layer is closer to the virtual build board than the first layer; for each adjacent pair of layers, generate a surface area difference, wherein the surface area difference is the difference between a first surface area of ​​the first layer and the surface area of ​​the preceding layer; based on a determination that the face count difference is less than zero and the surface area difference is greater than zero, for each adjacent pair of layers, predict that the first layer in each adjacent pair includes the presence of a build line; and store in the one or more memories a predicted list of build line layers, the predicted list of build line layers comprising one or more layers predicted to include the presence of the build line.

[0076] Clause 2: The device according to Clause 1, wherein the one or more processors are configured to further cause the device to: project an array of rays extending vertically upward from the virtual build plate onto the sliced ​​3D model; identify one or more layers from the predicted build line layer list, wherein rays in the array of rays first intersect the one or more layers; and determine the location where the rays in the array of rays intersect on the identified one or more layers, wherein the location is defined by a 3D coordinate position.

[0077] Clause 3: The device according to Clause 2, wherein the one or more processors are configured to further cause the device to: for each layer in the predicted list of build line layers, determine a portion having a minimum wall thickness; and determine a thickness value for the portion having the minimum wall thickness.

[0078] Clause 4: The device according to Clause 3, wherein the one or more processors are configured to further enable the device to determine relative intensity for each layer in the predicted list of build line layers based on the ratio of the surface area to the thickness value.

[0079] Clause 5: The apparatus according to any one of Clauses 1-4, wherein obtaining the sliced ​​three-dimensional model comprises: receiving a three-dimensional model of the part for additive manufacturing; and slicing the three-dimensional model into the plurality of layers along an additive manufacturing build direction.

[0080] Clause 6: The device according to any one of Clauses 1-5, wherein the one or more processors are configured to further cause the device to determine a face count for each of the plurality of layers, wherein the face count is the number of faces defined by the closed-loop shape of the layer among the plurality of layers.

[0081] Clause 7: The device according to any one of Clauses 1-6, wherein the predicted list of build line layers stored in the one or more memories is defined by a height value.

[0082] Item 8: A method for predicting the location of a build line in an additively manufactured part prior to such prediction, comprising: obtaining a sliced ​​three-dimensional model of the part for additive manufacturing, wherein the sliced ​​three-dimensional model comprises a plurality of layers stacked in a build direction extending from a virtual build board, and each of the plurality of layers comprising a predefined height; for each adjacent pair of the plurality of layers, generating a face count difference, wherein the face count difference is the difference between a first face count of a first layer and a second face count of a preceding layer, and the preceding layer being closer to the virtual build board than the first layer; for each adjacent pair of layers, generating a surface area difference, wherein the surface area difference is the difference between a first surface area of ​​the first layer and the surface area of ​​the preceding layer; predicting, for each adjacent pair of layers, the presence of a build line in the first layer of each adjacent pair based on a determination that the face count difference is less than zero and the surface area difference is greater than zero; and storing in one or more memories a predicted list of build line layers, the predicted list of build line layers comprising one or more layers predicted to include the presence of the build line.

[0083] Clause 9: The method according to Clause 8 further includes: projecting an array of rays extending vertically upward from the virtual build plate onto the sliced ​​3D model; identifying one or more layers from the predicted build line layer list, wherein the rays in the array of rays first intersect the one or more layers; and determining the location where the rays in the array of rays intersect on the identified one or more layers, wherein the location is defined by a 3D coordinate position.

[0084] Clause 10: The method according to Clause 9 further includes: for each layer in the predicted list of build line layers, determining a portion having a minimum wall thickness; and determining a thickness value for the portion having the minimum wall thickness.

[0085] Clause 11: The method according to Clause 10 further includes determining the relative intensity for each layer in the predicted list of build line layers based on the ratio of the surface area to the thickness value.

[0086] Clause 12: The method according to any one of Clauses 8-11, wherein obtaining the sliced ​​three-dimensional model comprises: receiving a three-dimensional model of the part for additive manufacturing; and slicing the three-dimensional model into the plurality of layers along an additive manufacturing build direction.

[0087] Clause 13: The method according to any one of Clauses 8-12 further includes determining a face count for each of the plurality of layers, wherein the face count is the number of faces defined by the closed-loop shape of the layer among the plurality of layers.

[0088] Clause 14: The method according to any one of Clauses 8-13, wherein the predicted list of build line layers stored in the one or more memories is defined by a height value.

[0089] Clause 15: A computer program product including one or more memories storing instructions that, when executed by one or more processors, cause the one or more processors to perform a method, the method comprising: obtaining a sliced ​​three-dimensional model of a part for additive manufacturing, wherein the sliced ​​three-dimensional model includes a plurality of layers stacked in a build direction extending from a virtual build board, and each of the plurality of layers includes a predefined height; for each adjacent layer pair of the plurality of layers, generating a face count difference, wherein the face count difference is the difference between a first face count of a first layer and a second face count of a preceding layer, and the preceding layer is closer to the virtual build board than the first layer; for each adjacent layer pair, generating a surface area difference, wherein the surface area difference is the difference between a first surface area of ​​the first layer and the surface area of ​​the preceding layer; based on a determination that the face count difference is less than zero and the surface area difference is greater than zero, for each adjacent layer pair, predicting that the first layer in each adjacent layer pair includes the presence of a build line; and storing in one or more memories a predicted list of build line layers, the predicted list of build line layers including one or more layers predicted to include the presence of the build line.

[0090] Clause 16: The computer program product according to Clause 15, wherein the instructions, when executed by one or more processors, further cause the one or more processors to: project an array of rays extending vertically upward from the virtual build plate onto the sliced ​​3D model; identify one or more layers from the predicted build line layer list, wherein rays in the array of rays first intersect the one or more layers; and determine the location where the rays in the array of rays intersect on the identified one or more layers, wherein the location is defined by a 3D coordinate position.

[0091] Clause 17: The computer program product according to Clause 16, wherein the instructions, when executed by one or more processors, further cause the one or more processors to: for each layer in the predicted list of build line layers, determine a portion having a minimum wall thickness; and determine a thickness value for the portion having the minimum wall thickness.

[0092] Clause 18: The computer program product according to Clause 17, wherein the instructions, when executed by one or more processors, further cause the one or more processors to perform: determining a relative intensity for each layer in the predicted list of build line layers based on the ratio of the surface area to the thickness value.

[0093] Clause 19: A computer program product according to any one of Clauses 15-18, wherein obtaining the sliced ​​three-dimensional model comprises: receiving a three-dimensional model of the part for additive manufacturing; and slicing the three-dimensional model into the plurality of layers along an additive manufacturing build direction.

[0094] Clause 20: A computer program product according to any one of Clauses 15-19, wherein the instructions, when executed by one or more processors, further cause the one or more processors to perform: determining a face count for each of the plurality of layers, wherein the face count is the number of faces defined by the closed-loop shape of the layer among the plurality of layers.

[0095] Article 21: A processing system comprising: a memory including computer-executable instructions; and a processor configured to execute the computer-executable instructions and cause the processing system to perform a method according to any one of Articles 1-20.

[0096] Article 22: A processing system comprising means for performing a method according to any one of Articles 1-20.

[0097] Article 23: A non-transitory computer-readable medium comprising computer-executable instructions, which, when executed by a processor of a processing system, cause the processing system to perform the method according to any one of Articles 1-20.

[0098] Other precautions

[0099] The terminology used herein is for descriptive purposes only and is not intended to be limiting. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms, including “at least one,” unless the context explicitly states otherwise. “Or” means “and / or.” As used herein, the term “and / or” includes any one and all combinations of one or more of the associated listed items. It should also be understood that when the terms “comprising” or “including” are used in this specification, the presence of the stated features, areas, integers, steps, operations, elements, and / or components is specified, but the presence or addition of one or more other features, areas, integers, steps, operations, elements, components, and / or groups thereof is not excluded. The term “or a combination thereof” refers to a combination that includes at least one of the foregoing elements.

[0100] It will be apparent to those skilled in the art that various modifications and variations can be made to this disclosure without departing from the spirit or scope thereof. Therefore, this disclosure is intended to cover such modifications and variations as long as they fall within the scope of the appended claims and their equivalents.

[0101] While various aspects of this disclosure have been described above, it should be understood that they are provided by way of example only and not as limitations. It will be apparent to those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of this disclosure. Therefore, the breadth and scope of this disclosure should not be limited by any of the exemplary aspects described above, but should be defined solely by the following claims and their equivalents.

Claims

1. An apparatus configured to predict the location of build-up lines in an additively manufactured part prior to such prediction, characterized in that, include: One or more memory units; and One or more processors, connected to the one or more memories, are configured to enable the device to: Obtain a sliced ​​3D model of the part for additive manufacturing, wherein the sliced ​​3D model comprises multiple layers stacked in a build direction extending from a virtual build plate, and each of the multiple layers comprises a predefined height; For each pair of adjacent layers in the plurality of layers, a face count difference is generated, wherein the face count difference is the difference between the first face count of the first layer and the second face count of the preceding layer, and the preceding layer is closer to the virtual building board than the first layer; For each pair of adjacent layers, a surface area difference is generated, wherein the surface area difference is the difference between the first surface area of ​​the first layer and the surface area of ​​the preceding layer; Based on the determination that the face count difference is less than zero and the surface area difference is greater than zero, for each of the adjacent layer pairs, the first layer in each of the adjacent layer pairs is predicted to include the presence of a construction line to form a predicted list of construction line layers, the predicted list of construction line layers including one or more layers predicted to include the presence of the construction line; The predicted list of build-line layers is stored in one or more memories, the predicted list of build-line layers including one or more layers predicted to include the presence of the build-line; and Adjust at least one dimension of the part used for additive manufacturing, the at least one dimension corresponding to a layer in the predicted build line layer list.

2. The device according to claim 1, characterized in that, The one or more processors mentioned above are configured to further enable the device to: An array of rays extending vertically upward from the virtual building block will be projected onto the sliced ​​3D model; Identify one or more layers from the predicted list of build-line layers that first intersect with the rays in the array of rays; and Determine the location where the rays intersect in an array of rays on one or more identified layers, wherein the location is defined by three-dimensional coordinate positions.

3. The device according to claim 2, characterized in that, The one or more processors mentioned above are configured to further enable the device to: For each layer in the predicted list of build-line layers, a subset of the corresponding layers with the minimum wall thickness is determined; and Determine the thickness value of the portion having the minimum wall thickness.

4. The device according to claim 3, characterized in that, The one or more processors are configured to further enable the device to determine the relative intensity for each layer in the predicted list of build-line layers based on the ratio of the surface area to the thickness value.

5. The device according to claim 1, characterized in that, The process of obtaining the sliced ​​3D model includes: Receive a three-dimensional model of the part for additive manufacturing; and The three-dimensional model is cut into the multiple layers along the additive manufacturing construction direction.

6. The device according to claim 1, characterized in that, The one or more processors are configured to further enable the device to determine the face count of each of the plurality of layers, wherein the face count is the number of faces defined by the closed-loop shape of the layer among the plurality of layers.

7. The device according to claim 1, characterized in that, The list of predicted build-line layers stored in the one or more memories is limited by a height value.

8. A method for predicting the location of build lines in an additively manufactured part before the part is manufactured, characterized in that, include: Obtain a sliced ​​3D model of the part for additive manufacturing, wherein the sliced ​​3D model comprises multiple layers stacked in a build direction extending from a virtual build plate, and each of the multiple layers comprises a predefined height; For each pair of adjacent layers in the plurality of layers, a face count difference is generated, wherein the face count difference is the difference between the first face count of the first layer and the second face count of the preceding layer, and the preceding layer is closer to the virtual building board than the first layer; For each pair of adjacent layers, a surface area difference is generated, wherein the surface area difference is the difference between the first surface area of ​​the first layer and the surface area of ​​the preceding layer; Based on the determination that the face count difference is less than zero and the surface area difference is greater than zero, for each of the adjacent layer pairs, the first layer in each of the adjacent layer pairs is predicted to include the presence of a construction line to form a predicted list of construction line layers, the predicted list of construction line layers including one or more layers predicted to include the presence of the construction line; The predicted list of build-line layers is stored in one or more memories, the predicted list of build-line layers including one or more layers predicted to include the existence of the build-line; and Adjust at least one dimension of the part used for additive manufacturing, the at least one dimension corresponding to a layer in the predicted build line layer list.

9. The method according to claim 8, characterized in that, Further includes: An array of rays extending vertically upward from the virtual building block will be projected onto the sliced ​​3D model; One or more layers are identified from the predicted list of build line layers, and the rays in the array of rays first intersect the one or more layers; and Determine the location where the rays intersect in an array of rays on one or more identified layers, wherein the location is defined by three-dimensional coordinate positions.

10. The method according to claim 9, characterized in that, Further includes: For each layer in the predicted list of build line layers, a portion of the corresponding layer with the minimum wall thickness is determined; Determine the thickness value of the portion having the minimum wall thickness; and Based on the ratio of the surface area to the thickness value, the relative intensity is determined for each layer in the predicted list of build line layers.

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