CN113487101A - Additive manufacturing path planning algorithm based on double laser heads - Google Patents

Additive manufacturing path planning algorithm based on double laser heads Download PDF

Info

Publication number
CN113487101A
CN113487101A CN202110838963.9A CN202110838963A CN113487101A CN 113487101 A CN113487101 A CN 113487101A CN 202110838963 A CN202110838963 A CN 202110838963A CN 113487101 A CN113487101 A CN 113487101A
Authority
CN
China
Prior art keywords
point
printing
sub
partition
path planning
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110838963.9A
Other languages
Chinese (zh)
Inventor
崔紫微
王祎
范永刚
王胜法
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Dalian University of Technology
Original Assignee
Dalian University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Dalian University of Technology filed Critical Dalian University of Technology
Priority to CN202110838963.9A priority Critical patent/CN113487101A/en
Publication of CN113487101A publication Critical patent/CN113487101A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/10—Additive manufacturing, e.g. 3D printing

Abstract

The invention belongs to the field of artificial intelligence and 3D printing, and relates to an additive manufacturing path planning algorithm based on double laser heads. The method comprises the steps of firstly partitioning a two-dimensional contour obtained after a three-dimensional model is sliced by adopting a polygon convex decomposition algorithm based on global subdivision, then selecting a parallel line scanning method to fill in each sub-partition, and finally connecting sub-partition paths by regarding an original traveling salesman problem as a multi-traveling salesman problem to realize a partition connection algorithm based on double laser heads, obtaining a simulation result of path planning, and finally completing a printing process. The method can effectively and uniformly distribute the printing paths to the two printing heads, and meanwhile, the partitioning algorithm based on polygonal convex decomposition can effectively reduce the rising and falling times of the printing heads and the idle running stroke of the printing heads, so that the aims of effectively reducing the printing time and improving the printing efficiency and the printing quality can be fulfilled.

Description

Additive manufacturing path planning algorithm based on double laser heads
Technical Field
The invention belongs to the field of artificial intelligence and 3D printing, and designs an additive manufacturing path planning algorithm based on double laser heads.
Background
The main process of additive manufacturing is to slice a part model according to a certain thickness, then to conduct path planning on each sliced layer and to introduce the sliced layer into a printer for printing, and finally to form the required part. The most important link in the printing process is the planning design of the printing path, and the quality of the printing path determines the quality of printing and simultaneously influences the printing speed and the amount of printing consumables. The common path planning methods at present include a parallel scanning method, a contour bias method, a spiral scanning method, and the like. The common path planning algorithm for the additive manufacturing of the large metal part is mainly a parallel scanning method, the method is mature, simple and easy to use, the printing speed is high, the printing time can be saved, and the method is favorable for path connection in the next step.
For a more complex model contour, the problems of large printing head idle distance, multiple rise and fall times of the printing head, printing and accumulation of sharp corners and the like can occur by utilizing a traditional path filling algorithm.
In recent years, the wide application of additive manufacturing technology has diversified the manufacture of printers, and it has appeared that various kinds of printers are suitable for different industries and research and manufacture. In order to improve printing efficiency and printing quality, a multi-laser-head printer and a corresponding technology are available, and since research on the aspect is still not much, the technology for planning the multi-laser-head additive manufacturing path is not mature. A printer for strong mixing conforming materials is designed in a paper 'design of a strong mixing composite material double-screw double-nozzle 3D printer' of the manufacturing technology laboratory of Qingdao science and technology university, and the printer fixes two nozzles and prints in a mode of moving a printing platform along a three-dimensional coordinate axis. The printer head of the main printer adopts a double-screw mechanism for feeding, so that the uniformity of the material is improved, and the printing head of the auxiliary machine is used for printing and supporting the material in the process of printing a complex structure. The working mode of the printer is that two printing nozzles do not operate simultaneously, so that the stability of the whole printer can be improved to a certain extent, the printing precision is improved, and the printing speed cannot be effectively improved.
Disclosure of Invention
In order to solve the problems, the invention provides an additive manufacturing path planning algorithm based on double laser heads.
The technical scheme of the invention is as follows:
a material increase manufacturing path planning algorithm based on double laser heads is characterized in that a two-dimensional contour obtained after a three-dimensional model is sliced is partitioned by adopting a polygon convex decomposition algorithm based on global subdivision, then a parallel line scanning method is selected in each sub-partition for filling, and finally an original traveler problem is regarded as a multi-traveler problem to be connected with a sub-partition path to realize a partition connection algorithm based on the double laser heads, so that a simulation result of path planning is obtained, and a printing process is finally completed.
The method comprises the following specific steps:
first step, preprocessing and model contour partitioning
And carrying out preliminary processing on the model to be printed by utilizing three-dimensional software to form an STL file recognized by the printer, and carrying out slicing operation. And storing the sliced two-dimensional contour sequence point set as an excel file. The vertexes of the polygon outline are arranged in the anticlockwise direction, a complex concave polygon is divided into simple convex polygons in the outline point set based on the global division idea, and concave points are eliminated by adding division lines.
Second, path planning calculation based on double laser heads
(1) Sub-partition path planning algorithm
And selecting a parallel scanning path planning algorithm to carry out path filling on each small partition by comparing different path filling algorithms. The parallel scanning filling is carried out along a certain initial direction in a self-adaptive selection mode aiming at the path filling of each small subarea, and compared with the parallel scanning along a certain side of a polygon, the problems of material accumulation, incomplete filling and the like of a sharp corner part can be avoided.
(2) Sub-partition path connection algorithm
After partitioning, the whole printing path planning problem can be converted into a traversal problem among sub-partitions, and the traversal problem among the sub-partitions can be converted into a business trip problem. For the printing path partition connection of the double laser heads, the original traveler problem can be regarded as a multi-traveler problem solution.
The original traveler problem is that one traveler searches for the shortest path by traversing all cities, while the multi-traveler problem is that a plurality of travelers simultaneously traverse all the current cities to obtain the shortest traversal path, which is more complicated than the traveler problem. The invention adopts an optimized genetic algorithm RLGA to solve the problem of multiple travelers. And the other part of the path planning still operates according to the partition algorithm and the sub-partition filling algorithm in the path planning algorithm based on the single laser head in the previous chapter.
The invention has the beneficial effects that:
the method can solve the problems that the additive manufacturing path planning based on the double laser heads can effectively distribute the printing path to the two printing heads more uniformly, meanwhile, the zoning algorithm based on the polygonal convex decomposition can effectively reduce the rising and falling times of the printing heads and the idle running stroke of the printing heads, and the parallel scanning method is selected to be used for filling in the sub-zoning filling process, so that the accumulation of printing sharp corners can be effectively reduced. The path planning of the double-laser-head printing is carried out through the three steps, so that the printing time can be effectively reduced, and the aims of improving the printing efficiency and the printing quality are fulfilled.
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 shows a pit piA subdivision schematic diagram;
FIG. 3 is a sub-partition parallel line scanning rule map;
FIG. 4 is a flow chart for solving a multi-business trip problem using an improved genetic algorithm;
FIG. 5 is a graph of the result of a square plan profile double-laser head printing plan;
fig. 6(a) and 6(b) are diagrams of the planning result of the plane profile of the five-pointed star, wherein fig. 6(a) is the planning result of the double laser heads, and fig. 6(b) is the planning result of the single laser head;
FIG. 7 is a diagram of the path planning result of a circular planar profile dual laser head with a cavity;
FIG. 8 is a graph of the result of path planning for a square planar profile dual laser head with multiple cavities;
fig. 9(a) and 9(b) are graphs of path planning results of complex contours, where fig. 9(a) is a double-laser head planning result and fig. 9(b) is a single-laser head planning result.
Detailed Description
The following further describes a specific embodiment of the present invention with reference to the drawings and technical solutions.
The flow chart of the path planning of the invention is shown in fig. 1, and the specific steps are as follows:
step 1: computation of pre-processing jobs and model partitioning
And establishing a 3D simulation model according to the model needing to be printed in the additive manufacturing, and slicing the 3D simulation model. For each two-dimensional slice surface, outputting the contour of each two-dimensional slice surface as a sequential point set, and storing the sequential point set in an excel file, wherein the model partitioning process takes one layer as an example as follows:
firstly, traversing polygon vertexes of the two-dimensional slice surface outline, storing the polygon vertexes into a point set, and establishing a concave point string. And sequentially taking out vertexes from the polygonal point set, judging whether the vertexes are pits or not, if the vertexes are pits, storing the vertexes into a pit string, and simultaneously recording the predecessors and successors of the pits and marking the predecessors and successors as unaccessed points. If no concave point exists after the traversal is finished, the polygon is a convex polygon, and the path planning process of Step2 is directly carried out; otherwise, continuing to partition the polygon.
In a second step, two vertices in a concave polygon are said to be visible to each other if the lines between the two vertices can all fall inside the polygon. Sequentially traversing the unaccessed pits in the pit strings, guiding the polygon partition process by adopting a weighting method, and establishing a weight function w for each group of visible point pairsiAnd connecting the two visual point pairs with the maximum weight value each time according to the formula (1), so that the number of the obtained convex polygons is minimum. Wherein p is shown in FIG. 2iIs a concave point of a polygon, pi-1、pi+1Are neighbors of the point. Let p bejIs from piThe other end of the starting subdivision line, alpha and beta are vectors p respectivelyipjAnd vector pipi-1，pipi+1The included angle of (a).
And thirdly, traversing the pit strings, finding an optimal subdivision point, weight and subdivision type from each pit by using a weight function of a formula (1), and storing the optimal subdivision point, weight and subdivision type, wherein the subdivision type value is 1 when the subdivision point is a pit, 2 when the subdivision point is a bump, and 3 when the subdivision point is an auxiliary point.
wi＝f(α,β)＝|cosα-cosβ| (1)
And fourthly, traversing the concave point string to find out the concave point with the minimum weight. The method is found according to the following rules: the subdivision type value is 1, namely the optimal subdivision is realized; if not, finding the salient point with the subdivision type value of 2; and finally finding out the auxiliary point subdivision.
And fifthly, decomposing the original polygon into two new polygons according to the concave points and the subdivision points, adding the two new polygons into the polygon storage linked list, deleting the original polygon from the polygon linked list, and performing concave polygon convex decomposition on the newly generated polygons in sequence recursively until no concave point exists.
Step 2: path planning based on double laser heads
(1) Sub-partition path planning
And selecting a parallel scanning path planning method to plan the path of each sub-partition by comparing different additive manufacturing path planning algorithms. And each sub-partition is adaptively selected to be scanned in parallel along a certain initial direction, so that the problems of material accumulation, incomplete filling and the like of a sharp corner part can be avoided compared with the problem of scanning in parallel along a certain side of a polygon. The sub-partition path plan fill results are shown in fig. 3.
(2) Connection of sub-partition paths
After the slice surface is subjected to region segmentation through Step1, for path planning of the sub-partitions, printing each closed sub-partition one by one, and regarding a connection sequence solving problem of the sub-partitions as a classic TSP multi-traveler problem, namely regarding a laser head as a traveler of the multi-traveler problem, and regarding each sub-partition as a city node in the multi-traveler problem to solve. Meanwhile, to equally distribute the task to each traveler (laser head), the multi-traveler problem with M paths for N points can be divided into the following two cases: firstly, evenly distributing the number of city nodes visited by m traveling merchants; and secondly, the lengths of the access paths of the m traveling merchants are distributed equally. Both cases have separate solutions and objective functions. For the problem of using multiple travelers in path planning, the second method for averagely distributing the access path length is selected to solve the problem of printing path planning, so that two printing laser heads can be fully utilized to print simultaneously, the printing time is effectively shortened, and the printing efficiency is improved.
The specific process is as follows:
at point a0Denotes the starting point of the laser head, A ═ a0,a1,…,an-1,anDenotes m travelers b1,b2,…,bmA child partition that needs access. Two variables are defined as shown in equations (2) and (3):
Figure BDA0003178240490000061
Figure BDA0003178240490000062
cijrepresents a sub-partition aiAnd sub-partition ajThe distance between them. The objective function of the mathematical model of the multi-traveler problem of the path planning for equally dividing the workload is shown as formula (4):
Z＝min(max(z1,z2,…,zm)) (4)
wherein:
Figure BDA0003178240490000063
the constraints are as follows:
Figure BDA0003178240490000064
Figure BDA0003178240490000065
Figure BDA0003178240490000071
Figure BDA0003178240490000072
wherein S is the branch elimination constraint, that is, the elimination of some solution sets forming incomplete routes. Equation (4) represents the minimization of the longest path among the m laser heads; the path length traveled by each laser head is represented in equation (5); the formula (6) represents the departure point a0All the sub-partitions are accessed by a certain laser head and only accessed once; formula (7) shows that the terminal sub-partition of any one path has only one starting point city connected with it; formula (8) indicates that the starting point sub-partition of any one path has only one end point city connected with it; equation (9) represents the elimination of the solution set that constitutes an incomplete way.
For the problem of multiple travelers with equalized workload, the method adopts an improved genetic algorithm (RLGA) to solve the problem, namely solving the solutions of a formula (4) and a formula (5), so that a group of path sequences traversing all sub-partitions are obtained, the sub-partitions in Step1 are sequentially connected according to the sequence, and the final path plan is obtained. A flow chart of this process is shown in fig. 4. Improved genetic algorithms are proposed based on the shortcomings of genetic algorithms and the advantages of invasive weed optimization algorithms. The propagation mechanism with fitness as the benchmark is added into the invasive weed optimization algorithm to generate offspring and carry out genetic operation, so that the adaptability of the population is improved, and the algorithm efficiency is improved. Meanwhile, a mixed local optimization strategy is provided as a mutation operator, exchange among groups is carried out on all traveling salesmen, and a two-element optimization (2-opt) exchange strategy is adopted on a single traveling salesmen, so that the local search capability of the algorithm is improved, and the solving precision of the algorithm is improved.
Step 3: output planning G-code
And outputting the final path planning data as a G-code. The G-code can be recognized by the 3D printer and is generally stored as a gcode format file in which relevant information such as a printing speed, an idle stroke speed, a printing size, coordinates of each dot printed, and the like is stored. The result of the planned path is convenient to view and use by outputting the G-code.
In order to verify the feasibility of the path planning algorithm based on the double laser heads, a plurality of groups of part slice two-dimensional contour graphs are selected for experiments. First, a simplest square plane contour is selected for path planning, and the result is shown in fig. 5. The whole path plan is divided into two parts, wherein areas marked by the first and second parts respectively represent the areas printed by the printing laser heads 1 and 2. For simple graphs which do not need to be partitioned, a printing path equipartition mode is selected, and the length of the whole printing path is evenly divided. Where S represents a printing start point and E represents a printing end point. As can be seen in the figure, the printing starting positions of the two printing heads are different, the respective printing paths do not have the phenomenon of cross collision, and finally the whole path is printed.
A five-pointed star section figure outline is selected for path planning, as shown in fig. 6(a), for the five-pointed star figure, the condition that the printing head still runs idle in the middle printing process, wherein a dotted line e is the idle running stroke of the printing head 1, and a dotted line f is the idle running stroke of the printing head 2. The distance of the empty stroke (as shown by the dashed path in the path of fig. 6 (b)) compared to the print path plan of the single laser head shows that the empty stroke is shorter in the path plan based on the double laser head. Both use the same partitioning algorithm and therefore the partition size is the same. The partition connection planning is regarded as a multi-traveler problem of path equipartition, and the path length of the path connection distance (idle travel) is relatively uniformly distributed.
Besides the two regular patterns, the path planning of the double laser heads can be carried out, and the path planning of the double laser heads can also be carried out on the complex pattern contour with the holes. A section view of the copper-wire-shaped part is selected, a small square is arranged in the middle of the circle, and the path planning result is shown in figure 7. The result of selecting a graph with three holes in a square for path planning is shown in fig. 8. As can be seen from comparing fig. 7 and 8, the path planning based on the two laser heads for the two graphs has no idle distance (idle stroke) of the printing head and no lifting times of the printing head, so that the drawing of the printing material and the running time of the printer caused by the lifting and falling of the printing head can be reduced. The printing efficiency and the printing quality can be improved by fully utilizing the two printing heads.
In order to further verify the feasibility of the algorithm, a more complex fan-shaped sawtooth part is selected for double-laser-head path planning, the experimental result is shown in fig. 9(a), different sub-partitions are distributed to two printing heads for printing according to the path sharing rule, the printing head 1 is distributed with four partitions, the printing head 2 is distributed with six partitions, and the lengths of the printing paths of the two laser heads are almost equal, so that the printing time can be effectively reduced. The number of print head landings is reduced compared to the path planning result for a single laser head (as shown in fig. 9 (b)). In the double-laser-head path planning, the two printing heads need to be lifted seven times totally, the printing head 1 is lifted three times, the printing head is lifted two times, the two laser heads print simultaneously, and accordingly the time for lifting the printing heads four times is consumed, while in the single-laser-head path planning, the printing heads need to be lifted 8 times, and the number of times for lifting and falling the printing heads 8 times is consumed. Therefore, the printing time can be effectively reduced by using the double-laser-head path planning, and the printing efficiency is improved. The experimental data relating to fig. 9 are shown in table 1.
TABLE 1 results for scalloped parts
Figure BDA0003178240490000091
Through the experiment, the invention can effectively solve the problem of path planning based on a double-laser-head 3D printer, the printing paths are uniformly distributed to two printing heads, meanwhile, the rising and falling times of the printing heads and the idle stroke of the printing heads can be effectively reduced based on the partitioning algorithm of polygonal convex decomposition, and the problem of printing sharp corner accumulation can be effectively reduced by selectively filling in the sub-partitioning filling process by using a parallel scanning method. The path planning of double-laser-head additive manufacturing is carried out through the three steps, so that the printing time can be effectively reduced, and the aims of improving the printing efficiency and the printing quality are fulfilled.

Claims (1)

1. An additive manufacturing path planning algorithm based on double laser heads is characterized by comprising the following specific steps:
step 1: computation of pre-processing jobs and model partitioning
Establishing a 3D simulation model according to a model needing to be printed in additive manufacturing, and slicing the 3D simulation model; for each two-dimensional slice surface, outputting the contour of each two-dimensional slice surface as a sequential point set, and storing the sequential point set in an excel file, wherein the model partitioning process takes one layer as an example as follows:
firstly, traversing polygon vertexes of a two-dimensional slice surface outline, storing the polygon vertexes into a point set, and establishing a concave point string; sequentially taking out vertexes from the polygonal point set, judging whether the vertexes are concave points or not, if the vertexes are concave points, storing the concave points into a concave point string, and simultaneously recording the predecessors and successors of the concave points and marking the predecessors as not-accessed; if no concave point exists after the traversal is finished, the polygon is a convex polygon, and the path planning process of Step2 is directly carried out; otherwise, continuing to partition the polygon;
second, two vertices in a concave polygon are said to be visible to each other if the lines between the two vertices can all fall inside the polygon; sequentially traversing the unaccessed pits in the pit strings, guiding the polygon partition process by adopting a weighting method, and establishing a weight function w for each group of visible point pairsiConnecting two visual point pairs with the maximum weight value every time according to the formula (1), so that the number of the obtained convex polygons is minimum; wherein p isiIs a concave point of a polygon, pi-1、pi+1Is a neighbor of the point; let p bejIs from piThe other end of the starting subdivision line, alpha and beta are vectors p respectivelyipjAnd vector pipi-1，pipi+1The included angle of (A);
thirdly, traversing the pit strings, finding an optimal subdivision point, weight and subdivision type from each pit by using a weight function of a formula (1), and storing the optimal subdivision point, weight and subdivision type, wherein the subdivision type value is 1 when the subdivision point is a pit, 2 when the subdivision point is a bump, and 3 when the subdivision point is an auxiliary point;
wi＝f(α,β)＝|cosα-cosβ| (1)
fourthly, traversing the concave point string to find out the concave point with the minimum weight; the method is found according to the following rules: the subdivision type value is 1, namely the optimal subdivision is realized; if not, finding the salient point with the subdivision type value of 2; finally finding out the auxiliary point for subdivision;
fifthly, decomposing the original polygon into two new polygons according to the concave points and the subdivision points, adding the two new polygons into the polygon storage linked list, deleting the original polygon from the polygon linked list, and performing concave polygon convex decomposition on the newly generated polygons in sequence recursively until no concave point exists;
step 2: path planning based on double laser heads
(1) Sub-partition path planning
Performing path planning on each sub-partition by using a parallel scanning path planning method, and performing parallel scanning on each sub-partition along a certain initial direction in a self-adaptive selection manner;
(2) connection of sub-partition paths
After the slice surface is subjected to region segmentation through Step1, for path planning of the sub-partitions, printing each closed sub-partition one by one, and regarding a connection sequence solving problem of the sub-partitions as a classic TSP multi-traveler problem, namely regarding a laser head as a traveler of the multi-traveler problem, and regarding each sub-partition as an urban node in the multi-traveler problem to solve; meanwhile, to equally distribute the task to each traveler, namely the laser head, for the problem of multiple travelers with M paths at N points, the lengths of access paths of M travelers need to be equally distributed to solve the problem of printing path planning;
the specific process is as follows:
at point a0Denotes the starting point of the laser head, A ═ a0,a1,…,an-1,anDenotes m travelers b1,b2,…,bmA child partition to be accessed; two variables are defined as shown in equations (2) and (3):
Figure FDA0003178240480000021
Figure FDA0003178240480000022
cijrepresents a sub-partition aiAnd sub-partition ajThe distance between them; the objective function of the mathematical model of the multi-traveler problem of the path planning for equally dividing the workload is shown as formula (4):
Z＝min(max(z1,z2,…,zm)) (4)
wherein:
Figure FDA0003178240480000031
the constraints are as follows:
Figure FDA0003178240480000032
Figure FDA0003178240480000033
Figure FDA0003178240480000034
Figure FDA0003178240480000035
wherein, S is the branch elimination constraint, namely, the elimination of some solution sets forming incomplete routes; equation (4) represents the minimization of the longest path among the m laser heads; the path length traveled by each laser head is represented in equation (5); the formula (6) represents the departure point a0All the sub-partitions are accessed by a certain laser head and only accessed once; formula (7) shows that the terminal sub-partition of any one path has only one starting point city connected with it; formula (8) indicates that the starting point sub-partition of any one path has only one end point city connected with it; equation (9) represents the elimination of the solution set that constitutes an incomplete way;
for the problem of multiple travelers with equally distributed workload, an improved genetic algorithm RLGA is adopted to solve the problem, namely solutions of a formula (4) and a formula (5) are solved, so that a group of path sequences traversing all sub-partitions are obtained, the sub-partitions in Step1 are sequentially connected according to the sequence, and a final path plan is obtained;
step 3: output planning G-code
And outputting the final path planning data as a G-code, and facilitating the result viewing of the path planning and the use of the planning result by outputting the G-code.
CN202110838963.9A 2021-07-23 2021-07-23 Additive manufacturing path planning algorithm based on double laser heads Pending CN113487101A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110838963.9A CN113487101A (en) 2021-07-23 2021-07-23 Additive manufacturing path planning algorithm based on double laser heads

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110838963.9A CN113487101A (en) 2021-07-23 2021-07-23 Additive manufacturing path planning algorithm based on double laser heads

Publications (1)

Publication Number Publication Date
CN113487101A true CN113487101A (en) 2021-10-08

Family

ID=77942386

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110838963.9A Pending CN113487101A (en) 2021-07-23 2021-07-23 Additive manufacturing path planning algorithm based on double laser heads

Country Status (1)

Country Link
CN (1) CN113487101A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113942230A (en) * 2021-12-20 2022-01-18 南京铖联激光科技有限公司 3D printing control system for double-laser segmentation and segmentation method thereof
CN113987828A (en) * 2021-11-10 2022-01-28 中国兵器装备集团自动化研究所 Time sequence-based multi-energy field additive manufacturing process planning method

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113987828A (en) * 2021-11-10 2022-01-28 中国兵器装备集团自动化研究所 Time sequence-based multi-energy field additive manufacturing process planning method
CN113987828B (en) * 2021-11-10 2023-06-09 中国兵器装备集团自动化研究所有限公司 Multi-energy-field additive manufacturing process planning method based on time sequence
CN113942230A (en) * 2021-12-20 2022-01-18 南京铖联激光科技有限公司 3D printing control system for double-laser segmentation and segmentation method thereof
WO2023116442A1 (en) * 2021-12-20 2023-06-29 南京铖联激光科技有限公司 3d printing control system for double-laser segmentation and segmentation method of 3d printing control system

Similar Documents

Publication Publication Date Title
CN113487101A (en) Additive manufacturing path planning algorithm based on double laser heads
Lee et al. An improved genetic algorithm for facility layout problems having inner structure walls and passages
CN110209363A (en) Intelligent 3D printing paths planning method based on genetic algorithm
CN111178582B (en) Logistics distribution optimization method based on improved genetic algorithm
CN109034468A (en) A kind of logistics distribution paths planning method with time window based on cuckoo algorithm
CN110941261B (en) Autonomous underwater vehicle multi-zone traversal path planning method
Fok et al. An ACO-based tool-path optimizer for 3-D printing applications
CN111882099A (en) Logistics distribution path planning method based on variable neighborhood parallel annealing algorithm
Li et al. Efficient path planning method based on genetic algorithm combining path network
CN107330214A (en) Spatial configuration optimal method based on discretization Yu heuristic evolution algorithm
CN106273496A (en) A kind of complicated break area automatic division method increased during material manufactures
Larki et al. Solving the multiple traveling salesman problem by a novel meta-heuristic algorithm
CN111290283B (en) Additive manufacturing single machine scheduling method for selective laser melting process
CN110619441A (en) Leader-based GA-PSO (genetic algorithm-particle swarm optimization) soft time window vehicle path optimization method
CN113570025A (en) E-commerce storage center goods space distribution method based on discrete particle swarm algorithm
CN110263970B (en) Hybrid discrete artificial bee colony algorithm for solving automatic rail trolley feeding scheduling problem
Mariescu-Istodor et al. Solving the large-scale tsp problem in 1 h: Santa claus challenge 2020
CN112836974A (en) DQN and MCTS based box-to-box inter-zone multi-field bridge dynamic scheduling method
Weller et al. A framework for tool-path airtime optimization in material extrusion additive manufacturing
Wu et al. Two-layered ant colony system to improve engraving robot’s efficiency based on a large-scale TSP model
CN101451305B (en) Control method of cutter idle stroke path during clothing piecing and cloth sample cutting process
CN105881897B (en) The optimal mobile route planing method of shower nozzle in 3 D-printing
Siswanto et al. Multi-heuristics based genetic algorithm for solving maritime inventory routing problem
Tang et al. A new ILS algorithm for cast planning problem in steel industry
Hu et al. Parallel BVH construction using locally density clustering

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination