Distributed workshop scheduling method for additive and subtractive composite manufacturing

Abstract

The invention relates to the field of intelligent manufacturing, in particular to a distributed workshop scheduling method for additive and subtractive composite manufacturing. The invention provides a two-stage algorithm comprising a batching stage and a scheduling stage. In the batching stage, an equilibrium height distribution greedy algorithm with two-dimensional interval constraint and a space block left lower placement strategy is designed, all workpieces to be produced are batched, workpiece batches are determined, and the obtained batches serve as coding information of the scheduling stage algorithm. In the scheduling stage, a non-dominated sorting genetic algorithm-II with excellence strategy driving variable neighborhood search is designed to carry out additive and subtractive composite manufacturing scheduling, and the optimization target of minimizing the maximum completion time and the total production cost is achieved. According to the method, additive and subtractive composite manufacturing scheduling can be realized, resources are better utilized, the production efficiency is maximized, and the production cost is reduced through cooperative work of multiple workshops and load dispersion.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing, and in particular to a distributed workshop scheduling method for additive and subtractive composite manufacturing. Background Art

[0002] Additive manufacturing, or 3D printing, is a manufacturing technology that builds three-dimensional entities by stacking materials layer by layer. Additive manufacturing has the advantages of high processing flexibility, high material utilization, and the ability to manufacture complex structures. However, additive manufacturing also has some limitations, and the manufacturing accuracy and strength may not be as good as traditional manufacturing methods.

[0003] Subtractive manufacturing is the process of removing excess material to obtain the desired part shape. Common processes include turning and milling. Subtractive manufacturing technology is mature and widely used. However, subtractive manufacturing has certain limitations in processing complex geometric shapes and internal structures, and the material utilization rate is relatively low.

[0004] Based on the limitations of additive manufacturing and subtractive manufacturing, additive and subtractive composite manufacturing has gradually been developed and applied. Additive and subtractive composite manufacturing combines the advantages of both additive and subtractive processes. While meeting the needs of complex structures, it can also improve processing accuracy through subtractive processes. Therefore, additive and subtractive composite manufacturing has shown broad application prospects in the manufacturing of high-precision parts and parts with complex functions.

[0005] Although additive and subtractive composite manufacturing has solved the problem of complex structure manufacturing and precision machining, it still faces the challenge of production scheduling technology in practical applications. Additive and subtractive composite manufacturing involves the combination of additive and subtractive processes, and different processes require different equipment. The existing production scheduling method is difficult to meet the complex scheduling requirements of multiple processes and multiple resources. In order to improve the efficiency of additive and subtractive composite manufacturing, an intelligent scheduling algorithm is urgently needed to solve the production scheduling problem of additive and subtractive composite manufacturing. Summary of the invention

[0006] In order to further improve the manufacturing efficiency and precision of intelligent manufacturing, the present invention provides a distributed workshop scheduling method for additive and subtractive composite manufacturing in view of the problem that the existing production scheduling methods are difficult to meet the complex scheduling requirements of multiple processes and multiple resources. The method is a two-stage algorithm, including two stages. The first stage is the batch stage, which is used to obtain the batch results of the workpieces to be produced as the coding information of the second stage algorithm; the second stage is the scheduling stage, which is used to schedule the production of additive and subtractive composite manufacturing in distributed workshops.

[0007] (1) In the batching stage, a balanced height allocation greedy algorithm with a two-dimensional spacing constraint and a space block bottom-left placement strategy is designed to batch all the workpieces to be produced and processed, determine the workpiece batches, and use the obtained batches as the encoding information of the algorithm in step (2);

[0008] (2) In the scheduling stage, a non-dominated sorting genetic algorithm-II with an excellence strategy-driven variable neighborhood search is designed to perform overall additive and subtractive composite manufacturing production scheduling to achieve the optimization goals of minimizing the maximum completion time and total production cost:

[0009] min{C max , TPC}

[0010]

[0011] TPC=∑∑∑∑PC ijmf

[0012] In the formula, C max , TPC are the maximum completion time and total production cost, is the maximum completion time of factory f, PC ijmf It is the cost of producing workpiece i in process j on machine m in factory f.

[0013] A storage device stores instructions and data for implementing a distributed workshop scheduling method for additive and subtractive composite manufacturing.

[0014] A distributed workshop scheduling device for additive and subtractive composite manufacturing includes: a processor and a storage device; the processor loads and executes instructions and data in the storage device to implement a distributed workshop scheduling method for additive and subtractive composite manufacturing.

[0015] The beneficial effects of the technical solution provided by the present invention are as follows: the present invention combines the advantages of additive manufacturing and subtractive manufacturing, can quickly manufacture objects of complex shapes during the manufacturing process, and can perform fine control during the manufacturing process, which can not only save material costs, but also achieve fast and easy manufacturing, and high-precision processing. Distributed workshop production distributes production tasks to multiple interrelated workshops or factories for processing. Each workshop has different machine and process processing capabilities, and the production task consists of multiple processes and needs to be completed on a specific machine. The advantage of the distributed workshop production problem is that it can better utilize resources, maximize production efficiency, and work together through multiple workshops to disperse the load and reduce bottlenecks. In addition, through reasonable workpiece allocation and machine scheduling, production costs can be reduced and the task burden of each workshop can be balanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0017] Figure 1 Schematic diagram of two-stage processing of additive manufacturing and subtractive processing in multiple workshops in an embodiment of the present invention.

[0018] Figure 2 It is a schematic diagram showing that a batch of workpieces is placed in an embodiment of the present invention.

[0019] Figure 3 It is a schematic diagram of the five-layer coding designed in the embodiment of the present invention.

[0020] Figure 4 Schematic diagram of a batch sequence string in an embodiment of the present invention.

[0021] Figure 5 Schematic diagram of a batch machine selection string in an embodiment of the present invention.

[0022] Figure 6 It is a schematic diagram of the process sequence string in the embodiment of the present invention.

[0023] Figure 7 Schematic diagram of process machine selection string in an embodiment of the present invention.

[0024] Figure 8 Schematic diagram of factory selection string in an embodiment of the present invention.

[0025] Fig. 9 Schematic diagram of the working principle of the crossover operator in an embodiment of the present invention.

[0026] Fig.10 Schematic diagram of the working principle of the mutation operator in the embodiment of the present invention.

[0027] Fig.11 It is a Pareto front display diagram in an embodiment of the present invention.

[0028] Fig.12 It is a diagram of parts batching in an embodiment of the present invention.

[0029] Fig.13 It is a Gantt chart produced in an embodiment of the present invention.

[0030] Fig.14 It is a schematic diagram of the operation of the hardware device in the embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0032] Example 1

[0033] Aiming at the distributed workshop scheduling problem of additive and subtractive composite manufacturing, the whole production process of the present invention includes two workshops, and the production process of each workshop includes two parts: additive manufacturing and subtractive processing. Figure 1 As shown, the first part is additive manufacturing, which is used to build parts with complex geometric shapes to form a body or a blank, and the second part is subtractive processing, which is used for subsequent fine processing.

[0034] The present invention is based on a two-stage hybrid algorithm with EHAG-2DS and NSGA-II-QSDVN (Two-stage Hybrid Algorithm with EHAG-2DS and NSGA-II-QSDVN for Additive-Subtractive Hybrid Manufacturing Distributed Scheduling) to divide the entire production optimization problem into two stages to solve: the first stage is the batching stage, and the second stage is the scheduling stage;

[0035] (1) In the batching stage, a balanced height allocation greedy algorithm with a two-dimensional spacing constraint and a spatial block bottom-left placement strategy is designed to batch all workpieces and determine the workpiece batches. The obtained batches are used as the encoding information of the algorithm in stage (2);

[0036] (2) In the scheduling stage, a non-dominated sorting genetic algorithm-II with an excellence strategy-driven variable neighborhood search is designed to perform overall production scheduling and achieve the optimization goal of minimizing the maximum completion time and total production cost:

[0037] min{C max , TPC}

[0038]

[0039] TPC=∑∑∑∑PC ijmf

[0040] In the formula, C max , TPC are the maximum completion time and total production cost, is the maximum completion time of factory f, PC ijmf It is the cost of producing workpiece i in process j on machine m in factory f.

[0041] Phase 1:

[0042] The additive manufacturing process of the present invention is selective laser melting (SLM). In SLM, the designated part is formed from material powder, which is melted layer by layer by a laser beam. The blade sprinkles a layer of material powder on the machine's building platform, and the moving laser scans the designated area to melt the powder, and then forms a layer of the part. The cycle repeats, and the part can eventually be fully formed.

[0043] The placement of workpieces needs to consider three constraints 1)-3):

[0044] 1) The height of the workpiece must not exceed the maximum height that the machine can accommodate.

[0045] h k ≤H mf (1)

[0046] Where h k and H mf are the maximum height of workpieces in batch k and the maximum height that can be accommodated by machine m in factory f, respectively.

[0047] If the height of the workpiece exceeds the maximum height that the machine can accommodate, the workpiece will not be successfully constructed.

[0048] 2) When placing workpieces, the specific shape of the build platform space must be considered, not simply judged by area. Therefore, the placement of workpieces must not exceed the build platform:

[0049]

[0050] In the formula, (x, y) represents the coordinates of the lower left corner of the workpiece on the platform, x, y are the coordinates of the lower left corner of the workpiece on the platform, l, w are the length and width of the workpiece, d is the placement interval of the workpiece, L mf , W mf It is the length and width of the casting platform of machine m in factory f.

[0051] 3) After the metal powder is melted and cooled, irregular surface features and adhesion may occur. Therefore, there must be a gap between each workpiece. The gap between each workpiece is limited to:

[0052]

[0053] In the formula, (x, y) represents the lower left corner coordinate of workpiece i on the platform, and (x′, y′) represents the lower left corner coordinate of workpiece i′ on the platform. l, w represent the length and width of workpiece i, l′, w′ represent the length and width of workpiece i′. Formula (3) is the constraint condition that must be satisfied when workpiece i is on the left or above workpiece i′.

[0054] 4) Batch production time

[0055] t kmf =SetTmf +ScanT mf ·∑(υ+sυ)+RT mf ·h k (4)

[0056] Among them, SetT mf , ScanT mf , RT mf are the batch setting time, unit volume scanning time and unit height powder laying time of the 3D printing machine, respectively. υ and sυ are the volume of the workpieces in the batch and the volume of the support material used in the process of manufacturing the workpieces, respectively.

[0057] Therefore, in stage (1), an Even Height Allocation greedy algorithm with 2Dspacingconstraints and a bottom-left placement strategy for space blocks (EHAG-2DS) is designed to batch all workpieces, determine workpiece batches, improve the utilization rate of the building platform, reduce the number of batches, and thus reduce production time and cost. The specific steps are as follows:

[0058] Step 1.1: Sort the workpieces in descending order according to their height;

[0059] Step 1.2: If the workpiece list is not empty, initialize a processing batch and initialize the placement space S; otherwise, go to step 1.6;

[0060] Step 1.3: If the workpiece list is not empty, traverse the placement space list S and find the lower leftmost space s; otherwise, go to step 1.6;

[0061] Step 1.4: Traverse the workpiece list P. If part i can be placed in s, add i to the batch, remove part i from the list P, and end the traversal. If not, rotate part i 90 degrees to determine whether it can be placed in s. If so, add i to the batch, remove part i from the list P, and end the traversal.

[0062] Step 1.5: Update the space list S. If S is not empty, go to step 1.3, otherwise go to step 1.2;

[0063] Step 1.6: Output all batches.

[0064] The space block bottom left placement strategy is:

[0065] Define the placement space using a data structure [X, Y, L, W], which represents a rectangular space. (X, Y) are the coordinates of the lower left corner of the space, L is the length of the space, and W is the width of the space.

[0066] Determine whether the workpiece can be placed in the placement space. Since there needs to be an interval between workpieces, an interval d needs to be added. Find the leftmost and lowest space s in the space list S. If l + 2d < L and w + 2d < W, it can be placed in s. The placement requirement is that the lower left corners are aligned, that is, (x, y) coincides with (X, Y).

[0067] Update the space list S: After placement, divide the space s. Divide it by the right boundary and the upper boundary. Find the leftmost and lowest space s. After placing the workpiece, use the upper boundary of the workpiece as the lower boundary of the new space, and the right boundary of the workpiece as the right boundary of the new space. The remaining space of the original s space is a new space.

[0068] If l + 2d < L and w + 2d < W are not satisfied, rotate the workpiece 90 degrees, and then determine whether the workpiece can be placed again. If it can, perform the placement and update the space list S; if not, skip this workpiece.

[0069] Continue to place workpieces and perform the above operations until the space is full.

[0070] During the placement process, some overly small spaces may be generated and cannot be used, that is, all workpieces cannot be placed in them. These spaces need to be discarded. The specific method is to make the lower boundary of this space flush with the lowest lower boundary of the adjacent spaces on both sides and merge them into a new space, and discard the remaining space and no longer use it. In addition, during the placement process, spaces with flush lower boundaries are merged into one space.

[0071] As Figure 2 shown, a schematic diagram of the placement completion of a batch of workpieces. Continue to place workpieces and perform the above operations until the space is full (including the overly small spaces that are no longer used).

[0072] The second stage:

[0073] The present invention designs an improved NSGA-II algorithm: Non-dominated Sorting Genetic Algorithm-II with Quality Strategy-Driven Variable Neighborhood Search (NSGA-II-QSDVN). The NSGA-II algorithm is a very effective method for solving multi-objective optimization problems. It is based on genetic algorithms and can find a set of Pareto optimal solutions through mechanisms such as non-dominated sorting and crowding distance. The specific steps of the improved NSGA-II algorithm are as follows:

[0074] Step 2.1: Use the non-dominated sorting genetic algorithm-II with excellence strategy driven variable neighborhood search to find the Pareto solution:

[0075] Step 2.1.1: Initialize the algorithm parameters, encode the solution individuals and initialize the population according to the results obtained in step (1);

[0076] Step 2.1.2: If the current number of iterations is less than the maximum number of iterations, go to step 2.1.3; otherwise, end the iteration and output the Pareto solution;

[0077] Step 2.1.3: Perform fast non-dominated sorting and crowding calculation on the population;

[0078] Step 2.1.4: Divide the sorted population into three parts: a front part, a middle part, and a back part; in this embodiment, the front part is the front 15%, the middle part is the middle 25%, and the back part is the back 60%;

[0079] Step 2.1.5: The first 15% is retained, the middle 25% is subjected to variable neighborhood search, and the last 60% is subjected to crossover mutation to obtain offspring;

[0080] Step 2.1.6: Perform fast non-dominated sorting and crowding calculation on the combined population of the parent and offspring generations, and select the first popsize individuals to form a new population, where popsize represents the size of the population;

[0081] Step 2.1.7: Increase the number of iterations by one, and go to step 2.1.2 to iterate until the maximum number of iterations is reached.

[0082] The problem characteristics in this embodiment are described as follows:

[0083] like Figure 1 As shown, when the workpiece processing is in the additive manufacturing part, according to the characteristics of the additive manufacturing process, the workpieces in the same batch can be processed simultaneously on one machine, that is, batch processing; while in the subtractive manufacturing part, one machine cannot process multiple workpieces at the same time, and one machine can only process one workpiece at the same time.

[0084] It is divided into five sub-problems: 1) Factory selection, selecting a factory for each batch / workpiece; 2) Batch machine selection, selecting a machine to process a batch of workpieces; 3) Batch sequencing, sorting the processing order of batches when additive manufacturing batch processing is part of the process; 4) Process machine selection, selecting a machine to process the process; 5) Process sequencing, sorting the processes when the subtractive processing part is processed separately.

[0085] Additive and subtractive manufacturing constraints:

[0086]

[0087] In the formula, C ij is the end time of process j of workpiece i; B ij is the start time of process j of workpiece i; AT ij is the processing time of the additive manufacturing part of workpiece i in process j, that is, the processing time of this process in the batch; ST ij It is the processing time of the subtractive processing part of workpiece i in process j.

[0088] Algorithm implementation:

[0089] 1. Coding method: A five-layer coding method is designed, such as Figure 3 As shown, there are five sub-problems:

[0090] Batch sequence (BS)

[0091] Batch machine selection string (BMS)

[0092] Operation sequence (OS)

[0093] Operation machine selection (OMS)

[0094] Factory selection string (FS)

[0095] The algorithm encoding reflects the batch information, such as Figure 3 In the figure, there are 4 processes that can be batch processed. Batch1 and batch2 represent the batch results obtained according to the algorithm in stage (1). That is, there are two batches to be produced and processed. Then there are only "1" and "2" in the batch sequence string (BS). The BS string has a total of 2*4 gene bits and a length of 8. Part1, part2, and part3 represent workpiece 1, workpiece 2, and workpiece 3 respectively. In the figure, batch1: part1 and part3 represent workpiece 1 and workpiece 3 are divided into batch 1; batch2: part2 represents workpiece 2 is divided into batch 2.

[0096] For the process stages that can be batch processed, the batch is used as the scheduling unit, and for the process stages that can be processed individually, the process of the individual workpiece is used as the scheduling unit.

[0097] like Figure 4 As shown, the batch sequence string represents the process in the order in which the batch numbers appear in the chromosome. For example, from left to right, the first "2" represents the first process of batch 2, and the second "2" represents the second process of batch 2.

[0098] like Figure 5 As shown, the batch machine selection string uses the index number in the optional machine set of each batch to represent the machine where each batch is produced. For example, the optional machine set of the second process of batch1 is {M4, M5, M6}, then the gene "3" corresponding to the second process of batch 1, that is, batch1O2, represents M6, where M represents the machine.

[0099] like Figure 6 As shown, the process sequence string represents the processes (plus those processed in batches) in the order in which the workpiece numbers and batch numbers appear in the chromosome. From left to right, the first "3" represents the fifth process of workpiece 3 (the first four processes are processed in batches), and the first "1" represents the fifth process of workpiece 1.

[0100] like Figure 7 As shown, the process machine selection string uses the index number in each process optional machine set to represent the machine where each workpiece process is produced. The optional machine set for the fifth process of workpiece 1 is {M10, M15}, so the gene "2" corresponding to O15 represents M15.

[0101] like Figure 8 As shown, the factory selection string means that each batch / workpiece can only be processed in one factory, and the production process does not cross factories during the production process. The factory where each batch is produced is indicated in the order of factory number appearance. The factory where the batch is located already contains the factory information where the workpiece is located.

[0102] 2. Step 2.2: Decode the solution individual obtained in step 2.1:

[0103] The previous decoding method may cause some unnecessary idle time of the machine, which not only wastes resources and increases costs, but also increases the completion time. In order to solve these problems, the present invention proposes an active decoding method that reduces idle time, as follows:

[0104] Step 2.2.1: Decode the batch string, traverse the batch string, and find the machine m of factory f selected by process j of batch b;

[0105] Step 2.2.2: Traverse the idle time period idle_time of machine m;

[0106] Step 2.2.3: Let the start processing time of batch b process j be equal to the maximum of the completion time of batch b process j-1 and the start time of the idle time period, and determine whether the start processing time of batch b process j plus the processing time is less than the end time of the idle time period. If so, go to step 2.2.4, otherwise go to step 2.2.2;

[0107] Step 2.2.4: Update the idle time period of machine m, the completion time and production cost of process j in batch b, and set the completion time of process j of all workpieces in batch b to be equal to the completion time of process j in batch b; after the batch string is decoded, go to step 2.2.5, otherwise go to step 2.2.1;

[0108] Step 2.2.5: Continue decoding the process string, traverse the process string, and find the machine m of factory f selected by process i and process j;

[0109] Step 2.2.6: Traverse the idle time period idle_time of machine m;

[0110] Step 2.2.7: Let the start processing time of process j of workpiece i be equal to the maximum value of the completion time of process j-1 of workpiece i and the start time of the idle time period, and determine whether the start processing time of process j of workpiece i plus the processing time is less than the end time of the idle time period. If so, go to step 2.2.8, otherwise go to step 2.2.6;

[0111] Step 2.2.8: Update the idle time period of machine m and the completion time and production cost of process j of job i. After decoding the process string, go to step 2.2.9, otherwise go to step 2.2.5;

[0112] Step 2.2.9: Decoding is completed, and the maximum completion time and total production cost are output.

[0113] 3. Evolutionary Strategy

[0114] 1) Elite individual retention: For the population after non-dominated sorting and crowding calculation sorting, the top 15% of individuals are retained as elites and directly enter the next generation.

[0115] 2) Quality Strategy-Driven Variable Neighborhood Search (QSDVNS): The middle 25% of the individuals in the sorted population are selected for neighborhood search. The excellent individuals maintain excellent characteristics and improve the local search ability of the population. Five neighborhood search operators are designed, and the strategy excellence drives the selection of variable neighborhood search operators.

[0116] Step 1: Let the population size be m, n=0.

[0117] Step 2: Randomly select a neighborhood search operator using the roulette mechanism.

[0118] Step 3: Perform neighborhood search on the population using the selected operator. If the new individual dominates the original individual, then n+1.

[0119] Step 4: After the evolution of the population individuals, calculate n / m, update the population and roulette wheel, and add n / m to the value of the search operator corresponding to the roulette wheel.

[0120] The five neighborhood search operators are as follows:

[0121] ① Random process minimum processing time machine transfer

[0122] Randomly select several processes and ask them to choose the machine with the minimum processing time

[0123] ② Random process minimum production cost machine transfer

[0124] Randomly select several processes and ask them to choose the machine with the minimum production cost

[0125] ③Randomly exchange two batches

[0126] Randomly select two batches from the batch sequence of the factory with the longest completion time and swap their positions

[0127] ④ Randomly exchange two processes

[0128] Randomly select two processes from the process sequence of the factory with the longest completion time and exchange their positions

[0129] ⑤ Batch transfer of key factories

[0130] Select the factory with the longest completion time and transfer a random batch from it to other factories

[0131] 3) Crossover and mutation: The remaining 60% of individuals undergo crossover and mutation to improve the global search capability of the population.

[0132] ①Crossover operator:

[0133] The invention proposes a crossover operator suitable for encoding of flexible job shop scheduling problems, namely a fixed gene fragment self-exchange crossover operator.

[0134] Randomly select two sites and exchange the gene fragments between the two sites of the two individuals. In the flexible workshop scheduling problem, the coding needs to ensure the correct number of processes. Simply exchanging gene fragments may lead to infeasible solutions, so a new way of exchanging is adopted. As shown in the figure below. The gene fragments that need to be exchanged in the figure are between sites 3 and 6. After the exchange, the gene fragments corresponding to p1 are "1, 2, 3, 1", and the gene fragments corresponding to p2 are "2, 1, 3, 2". When the first position is exchanged, the "2" of p1 needs to be exchanged with the "1" of p2, but in order to maintain the validity of the chromosome, it is not exchanged with the gene of p2, but with its own gene. Find the first gene that appears from left to right in its own gene and exchange it. The "2" at site 3 of p1 is exchanged with the "1" at site 2, and so on. p2 is also exchanged in the same way.

[0135] ② Mutation operator:

[0136] The present invention proposes a mutation operator suitable for encoding the flexible job shop scheduling problem, the logistic curve dynamic boundary mapping swap mutation operator.

[0137] Mutation probability Mr:

[0138] In the formula, t is the current number of iterations, and T is the maximum number of iterations. As the iterations change, the mutation rate will also change. Mr0 represents the initial mutation rate, and Mr t represents the mutation rate at generation t.

[0139] The probability of mutation will gradually decrease with the number of iterations. The characteristic of the Logistic curve is that it decreases slowly before the inflection point, decreases quickly after the inflection point, and finally tends to be stable. Parameter a can control the speed of the decrease of the probability of mutation, parameter b controls which iteration is the inflection point, and parameter c controls the degree to which the curve decreases when it is at the inflection point. Selecting appropriate parameters can maintain a high probability of mutation in the early iteration process, facilitate the generation of new solutions, and improve the exploration ability of the algorithm; while in the later iteration process, the algorithm gradually converges, which makes the probability of mutation decrease rapidly and prevents the destruction of the excellence of the solution.

[0140] The mutation strategy uses a random exchange method: randomly select several genes and randomly exchange their positions. The corresponding genes after the exchange are calculated by formula (6) and put into the gene position to ensure the feasibility of the solution. p The number of machines that can be selected for the operation.

[0141]

[0142] In the formula, gene p Represents the gene value of the solution p position, gene` p Represents the gene value at position p of the solution after exchange.

[0143] Experimental parameters in the embodiments of the present invention

[0144] The 3D printing machine parameter settings in the experiment are shown in Table 1:

[0145] Table 1 3D printing machine related parameters

[0146]

[0147] The experimental parameter settings are shown in Table 2:

[0148] Table 2 Algorithm parameter settings in the experiment

[0149]

[0150]

[0151] By using the above parameters, the technical solution of the embodiment of the present invention is implemented to obtain the following Fig.11 The Pareto frontier is shown in the figure. The horizontal axis is the maximum completion time of the largest production task, and the vertical axis is the total production cost. Curves of different colors represent different dominance relationships among individuals in the population. The solution on the curve closest to the lower left corner is the non-dominated solution set in the population, that is, the Pareto optimal solution set. The obtained workpiece batching diagram is shown in Fig.12 As shown in the figure, there are 9 batches in total, and each batch has at least 2 workpieces. The resulting production Gantt chart is as follows Fig.13 As shown in the figure, different colors represent different batches or workpieces, and different colors in a factory represent different processes (jobs) of the same batch or workpiece.

[0152] Example 2

[0153] A distributed workshop scheduling device 401 for additive and subtractive composite manufacturing, such as Fig.14 As shown, it includes: a processor 402 and a storage device 403; the processor 402 loads and executes instructions and data in the storage device 403 to implement the distributed workshop scheduling method for additive and subtractive composite manufacturing.

[0154] Example 3

[0155] A storage device stores instructions and data for implementing the distributed workshop scheduling method for additive and subtractive composite manufacturing.

[0156] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A distributed workshop scheduling method for additive and subtractive composite manufacturing, characterized by: The method includes two stages, the first stage is the batching stage, and the second stage is the scheduling stage; (1) In the batching stage, a balanced height allocation greedy algorithm with two-dimensional spacing constraints and a space block bottom-left placement strategy is designed to batch all the workpieces to be produced and processed, determine the workpiece batches, and use the obtained batches as the encoding information of the algorithm in stage (2); (2) In the scheduling stage, a non-dominated sorting genetic algorithm-II with an excellence strategy-driven variable neighborhood search is designed to carry out production scheduling for distributed workshop additive and subtractive composite manufacturing, achieving the optimization goal of minimizing the maximum completion time and total production cost: min{C max ,TPC} TPC=∑∑∑∑PC ijmf In the formula, C max , TPC are the maximum completion time and total production cost, is the maximum completion time of factory f, PC ijmf It is the cost of producing workpiece i in process j on machine m in factory f.

2. A distributed workshop scheduling method for additive and subtractive composite manufacturing according to claim 1, characterized in that: In step (1), all workpieces to be produced and processed are divided into batches. The specific implementation steps for determining the workpiece batches are as follows: Step 1.1: Sort the workpieces in descending order according to their height; Step 1.2: If the workpiece list is not empty, initialize a processing batch and initialize the placement space S; Otherwise go to step 1.6; Step 1.3: If the workpiece list is not empty, traverse the placement space list S and find the lower leftmost space s; Otherwise go to step 1.6; Step 1.4: Traverse the artifact list P. If part i can be placed in s, add i to the batch, remove part i from the list P, and end the traversal. If not, rotate part i 90 degrees to determine whether it can be placed in s. If so, add i to the batch, remove parti from the list P, and end the traversal. Where parti represents the i-th artifact. Step 1.5: Update the space list S. If S is not empty, go to step 1.3, otherwise go to step 1.2; Step 1.6: Output all batches.

3. A distributed workshop scheduling method for additive and subtractive composite manufacturing as claimed in claim 2, characterized in that: In step 1.4, the placement of the workpiece needs to meet three constraints: 1) The height of the workpiece must not exceed the maximum height that the machine can accommodate. The restrictions that need to be met are: h k ≤H mf (1) In the formula, h k and H mf are the maximum height of workpieces in batch k and the maximum height that can be accommodated by machine m in factory f, respectively; 2) The placement of the workpiece must not exceed the build platform. The restrictions that need to be met are: In the formula, (x, y) represents the coordinates of the lower left corner of the workpiece on the platform, l, w are the length and width of the workpiece, d is the placement interval of the workpiece, L mf , W mf is the length and width of the casting platform of machine m in factory f; 3) The intervals between the workpieces must meet the following constraints: Wherein, (x, y) and (x′, y′) represent the lower left corner coordinates of workpiece i and workpiece i′ on the platform respectively, l, w represent the length and width of workpiece i, and l′, w′ represent the length and width of workpiece i′.

4. A distributed workshop scheduling method for additive and subtractive composite manufacturing as claimed in claim 3, characterized in that: The production time of a workpiece batch is: t kmf =SetT mf +ScanT mf ·∑(v+sv)+RT mf ·h k (4) In the formula, SetT mf , ScanT mf , RT mf They are the batch setting time, unit volume scanning time and unit height powder laying time of the 3D printer, v, sv They are the volume of workpieces in a batch and the volume of support materials used in the process of manufacturing workpieces.

5. A distributed workshop scheduling method for additive and subtractive composite manufacturing according to claim 1, characterized in that: In step (1), the strategy for placing the space block in the lower left corner is: Use a data structure [X, Y, L, W] to define the placement space, (X, Y) is the coordinate of the lower left corner of the space, L is the length of the space, and W is the width of the space; Determine whether the workpiece can be placed in the placement space, find the bottom left placement space s in the space list S, if l+2d<L and w+2d<W, where l and w represent the length and width of the workpiece, then the workpiece can be placed in s. The placement requires the lower left corner to be aligned, that is, (x, y) and (X, Y) coincide; Update space list S: After placement, divide the placement space s into right and upper boundaries; find the lower left space s, and after placing the workpiece, use the upper boundary of the workpiece as the lower boundary of the new space, and the right boundary of the workpiece as the right boundary of the new space. The remaining space of the original s space is a new space; If l+2d<L and w+2d<W are not satisfied, the workpiece is rotated 90 degrees and it is determined again whether the workpiece can be placed. If so, it is placed and the space list S is updated; if not, the workpiece is skipped; Continue placing workpieces and performing the above operations until the space is full.

6. A distributed workshop scheduling method for additive and subtractive composite manufacturing according to claim 1, characterized in that: The additive manufacturing process is selective laser melting, where the specified component is formed from material powder, which is melted layer by layer by a laser beam.

7. A distributed workshop scheduling method for additive and subtractive composite manufacturing according to claim 1, characterized in that: The specific implementation process of step (2) is as follows: Step 2.1: Use the non-dominated sorting genetic algorithm-II with excellence strategy driven variable neighborhood search to find the Pareto solution: Step 2.1.1: Initialize the algorithm parameters, encode the solution individuals and initialize the population according to the results obtained in step (1); Step 2.1.2: If the current number of iterations is less than the maximum number of iterations, go to step 2.1.3; otherwise, end the iteration and output the Pareto solution; Step 2.1.3: Perform fast non-dominated sorting and crowding calculation on the population; Step 2.1.4: Divide the sorted population into three parts; Step 2.1.5: Keep the first part, perform variable neighborhood search on the middle part, and perform crossover mutation on the last part to obtain offspring; Step 2.1.6: Perform fast non-dominated sorting and crowding calculation on the combined population of the parent and offspring generations, and select the first popsize individuals to form a new population; Step 2.1.7: Increase the number of iterations by one, and go to step 2.1.2 to iterate until the maximum number of iterations is reached; Step 2.2: Decode the solution individual: Step 2.2.1: Decode the batch string, traverse the batch string, and find the machine m of factory f selected by process j of batch b; Step 2.2.2: Traverse the idle time period idle_time of machine m; Step 2.2.3: Let the start processing time of batch b process j be equal to the maximum of the completion time of batch b process j-1 and the start time of the idle time period, and determine whether the start processing time of batch b process j plus the processing time is less than the end time of the idle time period. If so, go to step 2.2.4, otherwise go to step 2.2.2; Step 2.2.4: Update the idle time of machine m, the completion time and production cost of process j in batch b, and set the completion time of process j of all workpieces in batch b to be equal to the completion time of process j in batch b; go to step 2.2.5 after the batch string is decoded, otherwise go to step 2.2.1; Step 2.2.5: Continue decoding the process string, traverse the process string, and find the machine m of factory f selected by process i and process j; Step 2.2.6: Traverse the idle time period idl of machine m e Tim e ; Step 2.2.7: Let the start processing time of process j of workpiece i be equal to the maximum value of the completion time of process j-1 of workpiece i and the start time of the idle time period, and determine whether the start processing time of process j of workpiece i plus the processing time is less than the end time of the idle time period. If so, go to step 2.2.8, otherwise go to step 2.2.6; Step 2.2.8: Update the idle time period of machine m, the completion time and production cost of process j of job i. If the process string is decoded, go to step 2.2.9, otherwise go to step 2.2.5; Step 2.2.9: Decoding is completed, and the maximum completion time and total production cost are output.

8. The distributed workshop scheduling method for additive and subtractive composite manufacturing according to claim 1, characterized in that: Additive and subtractive manufacturing constraints: In the formula, C ij is the end time of process j of workpiece i; B ij is the start time of process j of workpiece i; AT ij is the processing time of the additive manufacturing part of workpiece i in process j, that is, the processing time of this process in the batch; ST ij It is the processing time of the subtractive processing part of workpiece i in process j.

9. A storage device, characterized in that: The storage device stores instructions and data for implementing the distributed workshop scheduling method for additive and subtractive composite manufacturing as described in any one of claims 1 to 8.

10. A distributed workshop scheduling device for additive and subtractive composite manufacturing, characterized by: include: A processor and a storage device; the processor loads and executes instructions and data in the storage device to implement the distributed workshop scheduling method for additive and subtractive composite manufacturing as described in any one of claims 1 to 8.

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