Method for planning virtual assembly position sequence based on dijkstra algorithm

Abstract

The application discloses a virtual assembly position sequence planning method based on Dijkstra algorithm and belongs to the technical field of virtual assembly. According to the principle of the shortest path obtained by the Dijkstra algorithm, the difference of the tool types required by assembling different position part groups is obtained, the assembly order path with the least tool type replacement is calculated, the optimal assembly order of the different position part groups is given, the walking frequency of the assembly personnel is reduced, the operation efficiency is improved, the optimization of the equipment assembly process quality is realized, the sequence planning result can provide the basis for the improvement of the equipment assembly process, the assembly cost of the product in the actual assembly process is reduced, and the assembly quality and efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of virtual assembly technology, and more specifically to a virtual assembly position sequence planning method based on Dijkstra's algorithm. Background Technology

[0002] Traditional product assembly process planning and product design are often carried out sequentially. Typically, after designers complete the product design, process engineers manually plan the assembly process and sequence based on the assembly drawings. Because sequence planning is largely dependent on the individual knowledge and experience of the process engineers, and the judgment of the quality of the assembly sequence is heavily influenced by personal subjectivity, there is no unified evaluation standard. Furthermore, due to limitations in individual skills and knowledge, it is impossible to fully consider all factors affecting assembly costs, efficiency, and quality during the actual assembly process. This leads to wasted assembly resources and costs during the actual assembly process, and ultimately affects the product's assembly quality. Assembly sequence planning is one of the key technologies in virtual assembly.

[0003] In the product design and development process involving virtual assembly, a reasonable product assembly model should be able to completely describe all information related to assembly, providing complete and accurate basic information support for subsequent assembly sequence planning and maintenance. Assembly sequence planning is a combinatorial optimization problem with strong constraints in a discrete sorting space. Its goal is to obtain the optimal assembly sequence between parts under various assembly constraints, so as to optimize the assembly cost and assembly quality of the entire assembly production process. The optimal assembly sequence obtained through reasoning can be used to guide the actual assembly planning of the product, so as to reduce the assembly cost in the actual assembly process and improve the assembly quality and efficiency. Therefore, research in the field of assembly sequence planning has significant social value and practical significance. To this end, a virtual assembly position sequence planning method based on Dijkstra's algorithm is proposed. Summary of the Invention

[0004] The technical problem to be solved by this invention is: how to overcome the shortcomings of traditional sequence planning methods such as the repetitiveness of the design process and low assembly efficiency. It provides a virtual assembly position sequence planning method based on Dijkstra's algorithm, which can obtain the assembly order of parts in different positions given the assembly parts group and all assembly tools. This assembly order can minimize the number of tool changes and movement of the assembly personnel, thereby increasing the effective working time and improving the assembly quality and efficiency.

[0005] The present invention solves the above-mentioned technical problems through the following technical solution, and the present invention includes the following steps:

[0006] S1: Construct a virtual reality environment, draw 3D part group models and assembly tool models according to the actual simulation task requirements, and place them into the virtual reality environment, recording the types and quantities of part group models and assembly tool models;

[0007] S2: Number the part groups and each assembly tool to obtain a list of assembly tools required to assemble each part group. Based on the assembly tool list, obtain the number of tool differences between each pair of part groups, i.e., the tool difference degree.

[0008] S3: Construct an undirected graph based on part group number and tool difference, where part group number is used as a node in the undirected graph and tool difference is used as the weight of the connection between two nodes.

[0009] S4: Calculate the average neighbor dissimilarity of all nodes in an undirected graph;

[0010] S5: Based on tool difference degree, undirected graph, and average neighbor difference degree, the assembly order is finally obtained according to the shortest path in the undirected graph.

[0011] Furthermore, in step S2, the parts group is numbered as follows:

[0012]

[0013] in These are the part numbers for each part group.

[0014] Furthermore, in step S2, all assembly tools required during the assembly process are numbered as follows:

[0015]

[0016] in, These are the numbers for each assembly tool.

[0017] Furthermore, in step S2, a list of assembly tools required for assembling each group of parts is provided:

[0018]

[0019]

[0020]

[0021]

[0022] .

[0023] Furthermore, in step S2, the tool difference is... as follows:

[0024] .

[0025] Furthermore, in step S4, the formula for calculating the average neighbor dissimilarity of each node is as follows:

[0026] ;

[0027] ;

[0028] ;

[0029] ;

[0030] .

[0031] Furthermore, in step S5, the shortest path between the start and end points in the undirected graph is obtained using Dijkstra's algorithm. If there are multiple shortest paths with the same sum of weights, the path with the most nodes is selected, with the node having the smallest average neighbor dissimilarity as the start point and the node having the largest average neighbor dissimilarity as the end point.

[0032] Furthermore, in step S5, all nodes of the shortest path are stored in the assembly sequence planning sequence in chronological order. Nodes traversed by the shortest path in the undirected graph are removed, and the starting point and ending point are searched again until all nodes are written into the planning path, thus obtaining the assembly sequence planning sequence for all part groups.

[0033] Compared with existing technologies, this invention has the following advantages: The virtual assembly position sequence planning method based on Dijkstra's algorithm, according to the principle of finding the shortest path using Dijkstra's algorithm, calculates the assembly sequence path with the fewest tool changes by obtaining the differences in the types of tools required to assemble parts groups at different positions. This provides the optimal assembly sequence for parts groups at different positions, reducing the number of times assembly personnel need to move around, improving operational efficiency, and optimizing the quality of equipment assembly processes. The sequence planning results can provide a basis for improving equipment assembly processes, reducing assembly costs in the actual assembly process, and improving assembly quality and efficiency. Attached Figure Description

[0034] Figure 1 This is a flowchart illustrating the virtual assembly position sequence planning method based on Dijkstra's algorithm in an embodiment of the present invention.

[0035] Figure 2 This is an undirected graph constructed based on part group number and tool difference degree in this embodiment of the invention;

[0036] Figure 3 This is an undirected graph after removing the shortest path node in this embodiment of the invention. Detailed Implementation

[0037] The embodiments of the present invention are described in detail below. These embodiments are implemented based on the technical solution of the present invention, and provide detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the following embodiments.

[0038] like Figure 1 As shown, this embodiment provides a technical solution: a virtual assembly position sequence planning method based on Dijkstra's algorithm. According to the definitions of tool variability, undirected graph, and average neighbor variability, the method utilizes Dijkstra's algorithm to propose corresponding theoretical and computational standards, providing the optimal assembly sequence for parts groups at different positions. This reduces the number of times assembly personnel need to move around, improves operational efficiency, and optimizes the assembly process quality. The specific steps are as follows:

[0039] Step 1: Build a virtual reality environment based on the open-source engine UE4, draw 3D part group models and assembly tool models according to the actual simulation task requirements, and place them into the virtual reality environment, recording the types and quantities of part group models and assembly tool models.

[0040] Step 2: Number the parts groups. The parts groups are numbered as follows: Then, each tool was numbered as follows: Next, list the tools needed to assemble each part group, as follows:

[0041]

[0042]

[0043]

[0044]

[0045]

[0046] Then, based on the tool list, the number of tool differences between each pair of part groups is obtained, i.e., the tool difference degree. :

[0047] .

[0048] Step 3: Figure 2 This is an undirected graph constructed based on part group number and tool difference, where part group number is the node of the undirected graph and tool difference is the weight of the line connecting two nodes.

[0049] Step 4: According to Step 3 Figure 2 The average neighbor dissimilarity of all nodes is calculated. :

[0050] ;

[0051] ;

[0052] ;

[0053] ;

[0054] .

[0055] Step 5: Using the average neighbor dissimilarity obtained in Step 4, find the shortest path in the undirected graph. Select the node with the smallest average neighbor dissimilarity as the starting point and the node with the largest average neighbor dissimilarity as the ending point. The distance between the starting and ending points is then calculated using... The algorithm finds the shortest path with the sum of its weights. The specific calculation method is as follows: In Figure 2, the starting point is... The destination is In Table 1, the first row represents the initial state. The distance to each point is infinite; then from The point takes its first step, and the distances to A, C, D, and E are 3, 1, 3, and 3 respectively, as shown in the second row of Table 1. Since the distance to C is the shortest, the next step begins from C. The distance from C to A is 1 + 4 = 5, which is greater than the direct distance from B to A in the previous step. Therefore, the distance to A remains 3, and no update is made; the value is still 3, as shown in the fourth row of Table 1. The distance from C to D is 1 + 2 = 3, which is equal to the direct distance from B to D in the previous step. No update is made; the value is still 3, as shown in the fourth row of Table 1. The distance from D to E is 3 + 4 = 7, which is greater than the direct distance from B to E in the previous step. No update is made; the value is still 3, as shown in the fourth row of Table 1. Now, to get from A to E, we need to move forward again after reaching 3 steps. The total number of steps must be greater than the direct distance of 3 from B to E. Similarly, the same applies to the path from D to E. Therefore, the shortest path is... The path does not pass through any intermediate points. Except for this embodiment, if multiple shortest paths with equal sums of weights exist, the path with the most nodes is selected. The shortest path nodes from this embodiment are then written into the assembly sequence planning sequence: .

[0056] Table 1 Algorithm Step Table

[0057]

[0058] Step Six: Remove the shortest path nodes obtained in Step Five from the undirected graph. The undirected graph at this point is shown in Figure 3. Repeat Step Five. The shortest path is obtained as follows: Write the nodes on this path into the assembly sequence planning sequence: Finally, the optimal order of the parts groups in this case was obtained.

[0059] Given a set of assembly parts and all assembly tools, this invention uses this method to obtain the assembly sequence of parts in different positions. This assembly sequence can minimize the number of tool changes and movement required by the assembly personnel, thereby increasing effective working time and improving assembly quality and efficiency.

[0060] In summary, the virtual assembly position sequence planning method based on Dijkstra's algorithm described in the above embodiments, based on the principle of finding the shortest path using Dijkstra's algorithm, calculates the assembly sequence path with the fewest tool changes by obtaining the differences in the types of tools required to assemble parts groups at different positions. This provides the optimal assembly sequence for parts groups at different positions, reducing the number of times assembly personnel need to move around, improving operational efficiency, and optimizing the quality of equipment assembly processes. The sequence planning results can provide a basis for improving equipment assembly processes, reducing assembly costs in the actual assembly process, and improving assembly quality and efficiency.

[0061] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A virtual assembly position sequence planning method based on Dijkstra's algorithm, characterized in that, Includes the following steps: S1: Construct a virtual reality environment, draw 3D part group models and assembly tool models according to the actual simulation task requirements, and place them into the virtual reality environment, recording the types and quantities of part group models and assembly tool models; S2: Number the part groups and each assembly tool to obtain a list of assembly tools required to assemble each part group. Based on the assembly tool list, obtain the number of tool differences between each pair of part groups, i.e., the tool difference degree. S3: Construct an undirected graph based on part group number and tool difference, where part group number is used as a node in the undirected graph and tool difference is used as the weight of the connection between two nodes. S4: Calculate the average neighbor dissimilarity of all nodes in an undirected graph; S5: Based on tool difference degree, undirected graph, and average neighbor difference degree, the assembly order is finally obtained according to the shortest path in the undirected graph. In step S5, the shortest path between the start and end points in the undirected graph is obtained using Dijkstra's algorithm. If there are multiple shortest paths with the same sum of weights, the path with the most nodes is selected. The node with the smallest average neighbor dissimilarity is chosen as the start point, and the node with the largest average neighbor dissimilarity is chosen as the end point.

2. The virtual assembly position sequence planning method based on Dijkstra's algorithm according to claim 1, characterized in that: In step S2, the parts group is numbered as follows: ； in These are the part numbers for each part group.

3. The virtual assembly position sequence planning method based on Dijkstra's algorithm according to claim 2, characterized in that: In step S2, all assembly tools required during the assembly process are numbered as follows: ； in, These are the numbers for each assembly tool.

4. The virtual assembly position sequence planning method based on Dijkstra's algorithm according to claim 3, characterized in that: In step S2, a list of assembly tools required for assembling each group of parts is provided: ； ； ； ； 。 5. The virtual assembly position sequence planning method based on Dijkstra's algorithm according to claim 4, characterized in that: In step S2, the tool difference is as follows: 。 6. The virtual assembly position sequence planning method based on Dijkstra's algorithm according to claim 5, characterized in that: In step S4, the formula for calculating the average neighbor dissimilarity of each node is as follows: ； ； ； ； 。 7. The virtual assembly position sequence planning method based on Dijkstra's algorithm according to claim 1, characterized in that: In step S5, all nodes of the shortest path are stored in the assembly sequence planning sequence in chronological order. Nodes traversed by the shortest path in the undirected graph are removed. The starting point and ending point are searched again until all nodes are written into the planning path, thus obtaining the assembly sequence planning sequence for all part groups.

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