Multi-machine collaborative printing path planning method for additive manufacturing and additive manufacturing printing system

Abstract

The invention provides a multi-machine collaborative printing path planning method for additive manufacturing and an additive manufacturing printing system. The multi-machine collaborative printing path planning method comprises the following steps: acquiring input three-dimensional model data of a printing part; slicing is conducted according to the three-dimensional model data of the printed part, and n layers of equal-thickness layers and the outline of each layer are obtained; generating transverse and longitudinal filling paths for the contour of each layer; from the first layer to the nth layer, a multi-machine collaborative printing path is planned for the contour of each layer; and outputting a global multi-machine collaborative printing path. By means of the method, all the printing robots complete the printing work of the same layer at the same time, multi-machine efficient collaborative printing of MCAN manufacturing is achieved, and the part printing efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of additive manufacturing, and in particular to a multi-machine collaborative printing path planning method and an additive manufacturing printing system for additive manufacturing. Background Art

[0002] Additive Manufacturing (AM), also known as 3D printing, refers to the manufacture of three-dimensional parts by depositing materials layer by layer directly from digital images. Compared with traditional manufacturing technologies, AM shows great potential in manufacturing, biotechnology and other fields. However, the size of printable objects of 3D printers is still a problem that hinders the development and application of this technology. Because AM usually requires a fixed-size printer with limited space, the size of the manufacturable parts is strictly limited by the volume of the printer cavity. This means that the size of the parts that the printer can print must not be larger than this volume.

[0003] Currently, two methods are commonly used to solve the problem of printer cavity volume and printing object size. The first method is to design a printer large enough to manufacture the required model, but in most cases, it is impractical and costly to expand the scale of the printer. The second method is to divide the model into small pieces, and perform subsequent assembly after each piece is printed, such as welding and processing to combine into a whole part. The second method has been proven to be feasible, but it requires additional preliminary design and cumbersome process flow.

[0004] In order to break through the limitation of printing efficiency and improve the residual stress and structural properties of parts, multi-machine collaborative rapid prototyping manufacturing (MCAM) has gradually become a research hotspot in the field of AM. Its essence is multi-heat source collaborative additive manufacturing, that is, multiple independent devices are equipped with the same or different types of energy sources, and under specific process parameters and time and space coordination conditions, materials are deposited synchronously to achieve additive manufacturing. MCAM manufacturing technology is not a simple process superposition, but is based on the mass transfer and heat transfer mechanism under the conditions of multi-energy field coupling. It improves the performance of molded components by presetting the heat transfer, mass transfer and stress-strain behavior of materials in the additive manufacturing process. Summary of the invention

[0005] The purpose of the present invention is to provide a partition-based multi-machine collaborative printing path planning method suitable for additive manufacturing. Each printing robot completes the printing operation of the same layer at the same time, realizing multi-machine efficient collaborative printing of MCAN manufacturing and significantly improving the efficiency of printing parts.

[0006] According to the purpose of the present invention, it is also to provide an additive manufacturing printing system, which performs efficient collaborative operations based on the multi-machine collaborative printing path planning method proposed in the present invention to improve the efficiency of printing parts.

[0007] According to a first aspect of the present invention, a multi-machine collaborative printing path planning method for additive manufacturing is proposed, comprising:

[0008] Obtaining input three-dimensional model data of printed parts;

[0009] Slice the printed parts according to the 3D model data to obtain n layers of equal thickness and the outline of each layer;

[0010] Generate horizontal and vertical filling paths for each layer's outline;

[0011] Starting from the 1st layer to the nth layer, a multi-machine collaborative printing path is planned for each layer of contour;

[0012] Output the global multi-machine collaborative printing path.

[0013] As an optional implementation, planning a multi-machine collaborative printing path for each layer of contour includes:

[0014] For any i-th contour from the 1st layer to the nth layer, as an independent contour generation path process, it includes the following steps:

[0015] According to the contour of the i-th layer, the contour circumscribed rectangle is preliminarily divided into M total areas, and the total number of areas M corresponds to the number of printing robots in multi-level collaborative printing;

[0016] Divide each of the M total areas into sub-areas and plan the paths within the sub-areas;

[0017] Calculate the path length of each sub-area according to the filled path;

[0018] Calculate the arithmetic mean of the path lengths of all sub-areas contained in the M total areas;

[0019] According to the arithmetic mean, the sub-area division and the paths within the sub-areas are optimized according to the first strategy;

[0020] After the optimization is completed, the sub-areas in each total area are transitionally connected using empty trips;

[0021] Output the optimized path data of the i-th layer contour.

[0022] As an optional implementation manner, the first strategy is set to:

[0023] For each of the M total areas, the sum of the path lengths of the sub-areas contained in it is calculated as the total area path length;

[0024] Based on the M total area path lengths corresponding to the M total areas, when the difference between any two total area path lengths is less than or equal to a preset first threshold, the optimization is terminated.

[0025] As an optional implementation manner, the first strategy is set to:

[0026] When the number of iterative optimizations reaches a preset second threshold, the optimization is terminated.

[0027] As an optional implementation manner, the sub-area division and the paths within the sub-areas are optimized according to the arithmetic mean and the first strategy, and the optimization method includes:

[0028] Based on the arithmetic mean and taking it as a benchmark, the size of the path length of the sub-area and the arithmetic mean is determined:

[0029] When the path length of a sub-area exceeds the arithmetic mean, the sub-area is reduced;

[0030] When the path length of a sub-area does not reach the arithmetic mean, the sub-area will be expanded.

[0031] As an optional implementation, planning a multi-machine collaborative printing path for each layer of contour includes:

[0032] For any i-th contour from the 1st layer to the nth layer, as an independent contour generation path process, it includes the following steps:

[0033] According to the contour of the i-th layer, the contour circumscribed rectangle is preliminarily divided into M total areas, and the total number of areas M corresponds to the number of printing robots in multi-level collaborative printing;

[0034] Divide each of the M total areas into sub-areas and plan the paths within the sub-areas;

[0035] Calculate the path length of each sub-area according to the filled path;

[0036] For each total area, the sum of the path lengths of the sub-areas it contains is calculated as the total area path length;

[0037] The sub-area division and the paths within the sub-areas are optimized based on a preset iterative optimization algorithm until the number of iterations reaches a preset value or the difference between the lengths of any two total area paths is less than or equal to a preset first threshold, then the iterative process ends;

[0038] After the optimization is completed, the sub-areas in each total area are transitionally connected using empty trips;

[0039] Output the optimized path data of the i-th layer contour.

[0040] As an optional implementation, planning a multi-machine collaborative printing path for each layer of contour includes:

[0041] For any i-th contour from the 1st layer to the nth layer, as an independent contour generation path process, it includes the following steps:

[0042] According to the contour of the i-th layer, the contour circumscribed rectangle is preliminarily divided into M total areas, and the total number of areas M corresponds to the number of printing robots in multi-level collaborative printing;

[0043] Divide each of the M total areas into sub-areas and plan the paths within the sub-areas;

[0044] Calculate the path length of each sub-area according to the filled path;

[0045] For each total area, the sum of the path lengths of the sub-areas it contains is calculated as the total area path length;

[0046] A deep learning algorithm model based on machine learning is used to optimize the sub-area division and the paths within the sub-areas, so that the difference between the lengths of any two total area paths is less than or equal to a preset first threshold;

[0047] After the optimization is completed, the sub-areas in each total area are transitionally connected using empty trips;

[0048] Output the optimized path data of the i-th layer contour.

[0049] According to a second aspect of the present invention, an additive manufacturing printing system is also provided, comprising:

[0050] Multiple printing robots set up for collaborative printing;

[0051] The printing control system is configured to control the collaborative printing operation of the printing robot according to the global multi-machine collaborative printing path determined by the aforementioned multi-machine collaborative printing path planning method for additive manufacturing.

[0052] It should be understood that all combinations of the aforementioned concepts and the additional concepts described in more detail below can be considered as part of the inventive subject matter of the present disclosure as long as such concepts are not mutually inconsistent. In addition, all combinations of the claimed subject matter are considered as part of the inventive subject matter of the present disclosure.

[0053] The foregoing and other aspects, embodiments and features of the present invention can be more fully understood from the following description in conjunction with the accompanying drawings. Other additional aspects of the present invention, such as the features and / or beneficial effects of the exemplary embodiments, will be apparent from the following description or learned from the practice of the specific embodiments according to the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings are not intended to be drawn to scale. In the accompanying drawings, each identical or nearly identical component shown in each figure may be represented by the same reference numeral. For clarity, not every component is labeled in each figure. Embodiments of various aspects of the present invention will now be described by way of example and with reference to the accompanying drawings.

[0055] Figure 1 It is a flowchart of a multi-machine collaborative printing path planning method according to an embodiment of the present invention.

[0056] Figure 2 Schematic diagram of a filling path according to an embodiment of the present invention.

[0057] Figure 3 It is a schematic diagram of planning a multi-machine collaborative printing path for each layer contour according to an embodiment of the present invention.

[0058] Figure 4 is a schematic diagram of collaborative path planning of four printing robots according to an embodiment of the present invention.

[0059] Figure 5 is a schematic diagram of generating a sub-area path according to an embodiment of the present invention.

[0060] Figure 6 This is a schematic diagram of a printing test using a single printing robot.

[0061] Figure 7 This is a schematic diagram of the printing path of a general area A in which four printing robots perform collaborative printing according to an embodiment of the present invention.

[0062] Figure 8 is based on Figure 7 The other three printing robots in the embodiment are schematic diagrams of collaborative printing, corresponding to the general areas B, C, and D respectively.

[0063] Fig. 9 This is an example of test results of a printing robot completing printing of the total area A according to an embodiment of the present invention.

[0064] Fig.10 This is an example of test results of four printing robots completing printing of total areas A, B, C, and D according to an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to better understand the technical content of the present invention, specific embodiments are given and described as follows in conjunction with the accompanying drawings.

[0066] Various aspects of the present invention are described in this disclosure with reference to the accompanying drawings, in which many illustrative embodiments are shown. The embodiments of the present disclosure are not necessarily intended to include all aspects of the present invention. It should be understood that the various concepts and embodiments introduced above, as well as those described in more detail below, can be implemented in any of many ways, because the concepts and embodiments disclosed by the present invention are not limited to any implementation. In addition, some aspects disclosed by the present invention can be used alone or in any appropriate combination with other aspects disclosed by the present invention.

[0067] {Example 1}

[0068] Combination Figure 1 The multi-machine collaborative printing path planning method for additive manufacturing of the illustrated embodiment includes:

[0069] Obtaining input three-dimensional model data of printed parts;

[0070] Slice the printed parts according to the 3D model data to obtain n layers of equal thickness and the outline of each layer;

[0071] Generate horizontal and vertical filling paths for each layer's outline;

[0072] Starting from the 1st layer to the nth layer, a multi-machine collaborative printing path is planned for each layer of contour;

[0073] Output the global multi-machine collaborative printing path.

[0074] In an embodiment of the present invention, in order to achieve the highest efficiency of the collaborative system, the multi-machine collaborative printing path planning method proposed by the present invention implements the path planning of the collaborative robot based on partitioning, and each printing robot completes the printing operation of the same layer at the same time. Since the printing time is determined by the length of the printing path, and the printing path can be represented by the printing area, the present invention divides the area based on the method of equal division of the area, making the partitioning method more intuitive.

[0075] The input of the multi-machine collaborative printing path planning method is the 3D model data of the printed parts, especially the 3D model data of metal parts, which is stored and transmitted in the format of STL files. The triangle pieces stored in the STL file fit the 3D structure of the part model.

[0076] In an optional embodiment, slicing is performed according to the three-dimensional model data of the printed part to obtain n layers of equal thickness and the contour of each layer, including:

[0077] According to the 3D model data of the printed part, a set of equal-thickness planes are used to intersect the model to obtain multiple layers and the contours of each layer to complete the slicing process.

[0078] It should be understood that there is no limit to the horizontal and vertical filling paths that can be used in the path planning method of the embodiment of the present invention, for example, a straight line filling path, a parallel scanning filling path, a contour parallel scanning filling path, and a space filling curve scanning filling path, such as straight line path filling, Zigzag path filling, Contour-parallel path filling, Spiral curve path filling, Fermat Spiral path filling, Connected Fermat Spiral path filling, Piano curve filling, and Hilbert curve.

[0079] like Figure 2 As shown, a typical schematic diagram of various filling paths is exemplified.

[0080] In the following examples of the present invention, the straight line method is taken as an example.

[0081] As a first example of the present invention, a multi-machine collaborative printing path is planned for each layer of contour, including:

[0082] For any i-th contour from the 1st layer to the nth layer, as an independent contour generation path process, it includes the following steps:

[0083] According to the contour of the i-th layer, the contour circumscribed rectangle is preliminarily divided into M total areas, and the total number of areas M corresponds to the number of printing robots in multi-level collaborative printing;

[0084] Divide each of the M total areas into sub-areas and plan the paths within the sub-areas;

[0085] Calculate the path length of each sub-area according to the filled path;

[0086] Calculate the arithmetic mean of the path lengths of all sub-areas contained in the M total areas;

[0087] According to the arithmetic mean, the sub-area division and the paths within the sub-areas are optimized according to the first strategy;

[0088] After the optimization is completed, the sub-areas in each total area are transitionally connected using empty trips;

[0089] Output the optimized path data of the i-th layer contour.

[0090] As an optional method, for each layer's contour, the minimum circumscribed rectangle can be determined by its contour coordinates, that is, an AABB-type bounding box can be constructed. Since the contour is on the layered plane, the bounding box corresponding to its circumscribed rectangle is a two-dimensional rectangle.

[0091] On this basis, for the rectangle corresponding to the bounding box of each layer's contour, the rectangle is initially divided evenly according to its area, a rectangular isolation area is reserved in the middle of the rectangle, and the areas on both sides of the isolation area are equally divided to form multiple partitions as the total area, and the area of ​​each partition is the same. Furthermore, mature path strategies such as straight path, reciprocating path, contour path, spiral path, etc. are filled in the area where the divided total area is located.

[0092] As an optional implementation manner, the sub-area division and the paths within the sub-areas are optimized according to the arithmetic mean and the first strategy, and the optimization method includes:

[0093] Based on the arithmetic mean and taking it as a benchmark, the size of the path length of the sub-area and the arithmetic mean is determined:

[0094] When the path length of a sub-area exceeds the arithmetic mean, the sub-area is reduced;

[0095] When the path length of a sub-area does not reach the arithmetic mean, the sub-area will be expanded.

[0096] Therefore, for the total area and the sub-area division, the path length of each sub-area is calculated according to the filled path, and the arithmetic mean of the total path length of all sub-areas is used as the standard to compare the path lengths of all sub-areas and optimize them. The principle of optimization is: if the path length is long, the sub-area will be reduced, and if the path length is short, the sub-area will be expanded.

[0097] Take linear filling or reciprocating filling as an example. Figure 4 As shown in the figure, the single arrow in the second step is the direction of the path stacking, and the double arrow is the direction in which the sub-area boundary can move. After each optimization, when the sub-area size changes, the arithmetic mean of the path lengths of all sub-areas and the comparison process with the path length of the sub-area are recalculated, and then iterative optimization is performed accordingly.

[0098] As an optional embodiment, the gradient of expanding the sub-area or shrinking the sub-area is set to a fixed value, which can be obtained based on an empirical value or a test.

[0099] As an optional implementation manner, the first strategy is set to:

[0100] For each of the M total areas, the sum of the path lengths of the sub-areas contained in it is calculated as the total area path length;

[0101] Based on the M total area path lengths corresponding to the M total areas, when the difference between any two total area path lengths is less than or equal to a preset first threshold, the optimization is terminated.

[0102] In this example, the difference in the total area path lengths between the total areas is used as the condition for determining the end of the optimization. This is because the total area is evenly divided into rectangular areas. In principle, we hope that the total lengths of the printing paths of each total area are as close or the same as possible. However, due to factors such as the number of algorithm iterative optimizations and the optimization calculation efficiency, we limit the difference between them to a certain extent in this example.

[0103] As an optional implementation manner, the first strategy is set to:

[0104] When the number of iterative optimizations reaches a preset second threshold, the optimization is terminated.

[0105] like Figure 3 As shown, an example of using the number of iterations k as the end condition is exemplarily shown. As an optional implementation, the number of iterations k is selected to be greater than or equal to 50.

[0106] In the example of the present invention, the number of iterations k is selected to be 100 times.

[0107] In this example, in order to avoid excessive consumption of computing resources, excessive time consumption, and optimization falling into an infinite loop, we set the maximum number of iterations, that is, the aforementioned k value.

[0108] As a second example of the present invention, planning a multi-machine collaborative printing path for each layer of contour includes:

[0109] For any i-th contour from the 1st layer to the nth layer, as an independent contour generation path process, it includes the following steps:

[0110] According to the contour of the i-th layer, the contour circumscribed rectangle is preliminarily divided into M total areas, and the total number of areas M corresponds to the number of printing robots in multi-level collaborative printing;

[0111] Divide each of the M total areas into sub-areas and plan the paths within the sub-areas;

[0112] Calculate the path length of each sub-area according to the filled path;

[0113] For each total area, the sum of the path lengths of the sub-areas it contains is calculated as the total area path length;

[0114] The sub-area division and the paths within the sub-areas are optimized based on a preset iterative optimization algorithm until the number of iterations reaches a preset value or the difference between the lengths of any two total area paths is less than or equal to a preset first threshold, then the iterative process ends;

[0115] After the optimization is completed, the sub-areas in each total area are transitionally connected using empty trips;

[0116] Output the optimized path data of the i-th layer contour.

[0117] It should be understood that in this example, the best partition is iteratively optimized by an iterative optimization algorithm. The aforementioned iterative process can select at least one of a genetic algorithm, a particle swarm algorithm, an ant colony algorithm, a simulated annealing algorithm, a fish swarm algorithm, and a whale algorithm to optimize the partition size by optimization.

[0118] As an optional implementation, the setting of the aforementioned number of iterations may be the same as the aforementioned k, and the value may be greater than 50.

[0119] As an optional third example, planning a multi-machine collaborative printing path for each layer of contour includes:

[0120] For any i-th contour from the 1st layer to the nth layer, as an independent contour generation path process, it includes the following steps:

[0121] According to the contour of the i-th layer, the contour circumscribed rectangle is preliminarily divided into M total areas, and the total number of areas M corresponds to the number of printing robots in multi-level collaborative printing;

[0122] Divide each of the M total areas into sub-areas and plan the paths within the sub-areas;

[0123] Calculate the path length of each sub-area according to the filled path;

[0124] For each total area, the sum of the path lengths of the sub-areas it contains is calculated as the total area path length;

[0125] A deep learning algorithm model based on machine learning is used to optimize the sub-area division and the paths within the sub-areas, so that the difference between the lengths of any two total area paths is less than or equal to a preset first threshold;

[0126] After the optimization is completed, the sub-areas in each total area are transitionally connected using empty trips;

[0127] Output the optimized path data of the i-th layer contour.

[0128] In this example, machine learning is used to optimize the subdivision.

[0129] As an optional embodiment, when each total area is divided into sub-areas, the path length and style of each divided sub-area are the same or different, and the number of divided sub-areas is greater than or equal to 2.

[0130] {Example 2}

[0131] Combination Figure 1 , Figure 3 As shown, according to an embodiment disclosed in the present invention, an additive manufacturing printing system is also proposed, including:

[0132] Multiple printing robots set up for collaborative printing;

[0133] The printing control system is configured to control the collaborative printing operation of the printing robot according to the global multi-machine collaborative printing path determined by the multi-machine collaborative printing path planning method for additive manufacturing in the aforementioned embodiment.

[0134] {Example 3}

[0135] Combination Figures 4 to 10 As shown, we will combine the multi-machine collaborative printing path planning method and additive manufacturing printing system of the above embodiments to explain its implementation process in more detail.

[0136] like Figure 4 The example shown is an additive manufacturing printing system consisting of four printing robots, that is, n=4. Figure 4 The area enclosed by the red line is taken as an example as the outline of the i-th layer.

[0137] The circumscribed rectangle of the contour of the i-th layer is shown in the figure.

[0138] First, sort the red outlined areas counterclockwise to generate four total regions.

[0139] like Figure 4 As shown, both area A and area C are divided into two sub-areas, dark and light. The dark sub-area is the area adjacent to other total areas, and the light sub-area is the area not adjacent to other total areas.

[0140] Area B and area D are divided into three sub-areas, which are also configured with dark and light sub-areas. The dark sub-area is the area adjacent to other total areas, and the light sub-area is the area not adjacent to other total areas.

[0141] Combination Figure 4 As shown, both area B and area D are respectively configured with an additional third area, namely, a green isolation area, as a fixed isolation area that divides the circumscribed rectangle in half. As an optional embodiment, for the circumscribed rectangle of the outline of each layer, the isolation area is set at the center of the circumscribed rectangle, and when the initial total area is divided, the areas of the four partitions A, B, C, and D are equal and evenly divide the rectangular area except the isolation area.

[0142] In this example, the isolation area is configured so that when one of the sub-areas is adjusted, too many other sub-areas will not be affected.

[0143] Then, paths are generated for the divided total areas A, B, C, and D.

[0144] Combined with the path planning method of the above embodiment and Figure 4The sub-area division shown in the figure generates the path by intersecting the bounding box with the initial path within the contour. On this basis, the sub-area size of each area is adjusted iteratively to optimize the path, with the goal of making the execution time of the four printing robots the same or similar, so as to achieve the effect of collaboratively completing a task.

[0145] Since the printing execution time is related to the length of the path, in the embodiment of the present invention, a judgment is made based on the length of the path, and the sum of the path lengths of the sub-areas included in each total area is taken as the total area path length.

[0146] Each iterative optimization process follows the aforementioned adjustment method (i.e., the arithmetic mean of the total path lengths of all sub-areas of the four total areas is used as the standard, the path lengths of all sub-areas are compared and optimized, and the optimization principle is: if it is longer than the arithmetic mean, the sub-area is reduced, and if it is shorter than the arithmetic mean, the sub-area is expanded). As shown in the figure, the adjusted partition boundary is the red line segment marked with a black double arrow.

[0147] Finally, after multiple iterations of optimization, the paths reach the same or similar standard in the four general areas, for example, the difference between them is less than a preset standard. As shown in the figure, the black solid line is the boundary between the general areas, and the black dotted line is the sub-area boundary within the general area.

[0148] In an embodiment of the present invention, the rules followed in the sub-area printing order are: when there are two sub-areas in the total area, the dark risk area (i.e., the area adjacent to the sub-areas) is completed first, and then the light safety area (i.e., the area not adjacent to other partitions) is completed; when there are more than two sub-areas, the order of dark and light colors remains unchanged, and finally the printing of the green isolation area is completed.

[0149] The scanning direction of the internal path of each sub-area follows the rule of from left to right and from bottom to top.

[0150] As an optional embodiment, in the above process, for the path generation in each sub-area, since two types of paths are prepared after the slicing process, taking the straight line strategy as an example, if the short side path is more effective for manufacturing parts, the path filling direction and angle will be determined according to the aspect ratio of the sub-area. Figure 5 As shown, the longest side of the sub-area boundary represented by the dotted line is used as the path filling direction. After selecting the preliminary path style, the path in the intersection area of ​​the bounding box of the sub-area and the path layer of the style is retained.

[0151] After completing the printing task of its partition, each printing robot returns to the safe starting point of the current layer or rises to the starting point of the next layer to wait.

[0152] When all printing robots complete their tasks, they will start printing the next layer.

[0153] According to the layer-by-layer stacking process characteristics of additive manufacturing, two or more printing robots work together to form parts according to the collaborative path planned by the above method.

[0154] {Example 4}

[0155] In this example, we take a multi-machine collaborative printing robot system consisting of four robots as an example for explanation, and its path planning is implemented using the method of the aforementioned embodiment.

[0156] like Figure 6 As shown, first we take a robot as an example. After slicing, it plans the printing path of the entire printed part according to the straight line filling. The time to complete the part printing is 2755.4 seconds.

[0157] Combination Figure 7 As shown, we use a multi-machine collaborative printing robot system consisting of four robots to perform multi-machine collaborative printing.

[0158] According to the speed of real-time demonstration, such as Figure 7 , 8 As shown, the four robots all plan the corresponding sub-areas and paths of the total area according to the method of the above embodiment. The red line segments represent the paths, and the total printing paths of the total areas A, B, C, and D are shown respectively.

[0159] like Fig. 9 As shown, at 374 seconds, the first printing robot completed the task.

[0160] like Fig.10 As shown, at 434 seconds, all four printing robots completed the task.

[0161] Since the division of sub-areas and the filling of paths are almost the same, there are still small differences, so the total path length of each total area varies within a certain range. Therefore, after the first printing robot completes the job, it needs to wait for the other three printing robots to finish the task, causing the entire printing task to take more time than the completion time of the first printing robot. Combined with the printing process of the additive manufacturing system in the above example, multiple printing robots can achieve a work sequence with mostly identical overlapping intervals after partitioning. In the same time, the workload completed by multiple printing robots is much greater than that of a single printing robot, thereby greatly shortening the printing time and improving the molding efficiency of parts.

[0162] Combination Figure 6 as well as Fig.10 It can be seen from the comparison of printing time that when the multi-machine collaborative path planning method proposed in the present invention is used for part printing and forming, the total time consumption can be shortened by 84% compared with single-machine printing and forming, which significantly improves the efficiency of printing parts.

[0163] Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. A person with ordinary knowledge in the technical field to which the present invention belongs may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the definition of the claims.

Claims

1. A multi-machine collaborative printing path planning method for additive manufacturing, characterized in that: include: Obtaining input three-dimensional model data of printed parts; Slice the printed parts according to the 3D model data to obtain n layers of equal thickness and the outline of each layer; Generate horizontal and vertical filling paths for each layer's outline; Starting from the 1st layer to the nth layer, a multi-machine collaborative printing path is planned for each layer of contour; Output the global multi-machine collaborative printing path.

2. The multi-machine collaborative printing path planning method for additive manufacturing according to claim 1, characterized in that: Slice the printed part according to its 3D model data to obtain n layers of equal thickness and the outline of each layer, including: According to the 3D model data of the printed part, a set of equal-thickness planes are used to intersect the model to obtain multiple layers and the contours of each layer to complete the slicing process.

3. The multi-machine collaborative printing path planning method for additive manufacturing according to claim 1, characterized in that: The two horizontal and vertical filling paths include one of a straight line filling path, a parallel scanning filling path, a contour parallel scanning filling path, and a space filling curve scanning filling path.

4. The multi-machine collaborative printing path planning method for additive manufacturing according to claim 1, characterized in that: The multi-machine collaborative printing path planning for each layer of contour includes: For any i-th contour from the 1st layer to the nth layer, as an independent contour generation path process, it includes the following steps: According to the contour of the i-th layer, the contour circumscribed rectangle is preliminarily divided into M total areas, and the total number of areas M corresponds to the number of printing robots in multi-level collaborative printing; Divide each of the M total areas into sub-areas and plan the paths within the sub-areas; Calculate the path length of each sub-area according to the filled path; Calculate the arithmetic mean of the path lengths of all sub-areas contained in the M total areas; According to the arithmetic mean, the sub-area division and the paths within the sub-areas are optimized according to the first strategy; After the optimization is completed, the sub-areas in each total area are transitionally connected using empty trips; Output the optimized path data of the i-th layer contour.

5. The multi-machine collaborative printing path planning method for additive manufacturing according to claim 4, characterized in that: The first strategy is set to: For each of the M total areas, the sum of the path lengths of the sub-areas contained in it is calculated as the total area path length; Based on the M total area path lengths corresponding to the M total areas, when the difference between any two total area path lengths is less than or equal to a preset first threshold, the optimization is terminated.

6. The multi-machine collaborative printing path planning method for additive manufacturing according to claim 4, characterized in that: The first strategy is set to: When the number of iterative optimizations reaches a preset second threshold, the optimization is terminated.

7. The multi-machine collaborative printing path planning method for additive manufacturing according to any one of claims 4 to 6, characterized in that: The sub-area division and the paths within the sub-areas are optimized according to the arithmetic mean and the first strategy, and the optimization method includes: Based on the arithmetic mean and taking it as a benchmark, the size of the path length of the sub-area and the arithmetic mean is determined: When the path length of a sub-area exceeds the arithmetic mean, the sub-area is reduced; When the path length of a sub-area does not reach the arithmetic mean, the sub-area will be expanded.

8. The multi-machine collaborative printing path planning method for additive manufacturing according to claim 1, characterized in that: The multi-machine collaborative printing path planning for each layer of contour includes: For any i-th contour from the 1st layer to the nth layer, as an independent contour generation path process, it includes the following steps: According to the contour of the i-th layer, the contour circumscribed rectangle is preliminarily divided into M total areas, and the total number of areas M corresponds to the number of printing robots in multi-level collaborative printing; Divide each of the M total areas into sub-areas and plan the paths within the sub-areas; Calculate the path length of each sub-area according to the filled path; For each total area, the sum of the path lengths of the sub-areas it contains is calculated as the total area path length; The sub-area division and the paths within the sub-areas are optimized based on a preset iterative optimization algorithm until the number of iterations reaches a preset value or the difference between the lengths of any two total area paths is less than or equal to a preset first threshold, then the iterative process ends; After the optimization is completed, the sub-areas in each total area are transitionally connected using empty trips; Output the optimized path data of the i-th layer contour.

9. The multi-machine collaborative printing path planning method for additive manufacturing according to claim 1, characterized in that: The multi-machine collaborative printing path planning for each layer of contour includes: For any i-th contour from the 1st layer to the nth layer, as an independent contour generation path process, it includes the following steps: According to the contour of the i-th layer, the contour circumscribed rectangle is preliminarily divided into M total areas, and the total number of areas M corresponds to the number of printing robots in multi-level collaborative printing; Divide each of the M total areas into sub-areas and plan the paths within the sub-areas; Calculate the path length of each sub-area according to the filled path; For each total area, the sum of the path lengths of the sub-areas it contains is calculated as the total area path length; A deep learning algorithm model based on machine learning is used to optimize the sub-area division and the paths within the sub-areas, so that the difference between the lengths of any two total area paths is less than or equal to a preset first threshold; After the optimization is completed, the sub-areas in each total area are transitionally connected using empty trips; Output the optimized path data of the i-th layer contour.

10. An additive manufacturing printing system, characterized in that: include: Multiple printing robots set up for collaborative printing; A printing control system is configured to control the collaborative printing job of the printing robot according to a global multi-machine collaborative printing path determined by a multi-machine collaborative printing path planning method for additive manufacturing according to any one of claims 1 to 9.

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