Electrode milling batch processing batch decision method based on community division algorithm

Abstract

The invention relates to an electrode milling batch processing batch method based on a community division algorithm. The method comprises the following specific steps: acquiring work order information of electrode processing from an electrode milling programming section; electrode attributes and tool parameters in the work order information are obtained and analyzed; performing grouping operation according to the electrode attributes and the cutter parameter analysis result; and outputting an electrode grouping result. According to the method, the weighted network diagram is constructed by taking the electrode objects as nodes and taking the similarity degree between the electrodes as a connecting edge. In the grouping process, the similarity degree between the electrodes is converted into a network modularity index, that is, the larger the modularity index value of the network is, the higher the similarity of the electrodes in the group is after grouping. By implementing the method, a scientific basis and an intelligent means can be provided for batch decision-making of electrode milling batch machining, the machining efficiency can be effectively improved, and the machining cost can be effectively reduced.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing technology, and in particular to a batch decision method for electrode milling batch processing based on a community partitioning algorithm. Background Art

[0002] In the mold production process, the milling process of the electrode is often time-consuming. Figure 1 As shown in the figure, in order to improve the processing efficiency of this link, enterprises usually adopt batch processing mode. However, if the electrode batch is unreasonable, the tool specifications and quantity used by different electrodes in the same batch may be quite different, and such differences will lead to frequent tool changes during the processing. This frequent tool switching will not only affect the processing performance of the machine tool, but also cause production interruptions and reduce production efficiency. Therefore, a reasonable electrode batch has a significant impact on improving the electrode milling efficiency.

[0003] From the on-site investigation of electrode milling, although enterprises have been using batch processing mode for electrode milling, there is a lack of strategy for electrode grouping, which is somewhat blind. To this end, after carefully analyzing the characteristics of electrode milling batch processing flow, process constraints and tool usage characteristics of different electrodes, this paper proposes a batch processing method for electrode milling based on community partitioning algorithm, aiming to provide scientific basis and intelligent means for batch decision-making of electrode milling batch processing, ensure that the tool specifications and processing sequence used by electrodes in the same batch are as similar as possible, thereby effectively reducing the number of tool switching during the processing process.

[0004] The defects of the prior art are:

[0005] Although the company has been using batch processing mode for electrode milling, there is a lack of strategy for electrode grouping and a certain degree of blindness, which leads to frequent tool changes during the processing process, causing production interruptions and reduced production efficiency. Summary of the invention

[0006] In view of the above problems, the present invention provides a batch decision method for electrode milling batch processing based on a community partitioning algorithm, which aims to provide a scientific basis and intelligent means for batch decision-making in electrode milling batch processing, effectively improve processing efficiency and reduce processing costs.

[0007] To solve the above problems, the technical solution provided by the present invention is:

[0008] The batch decision method for electrode milling batch processing based on community partitioning algorithm includes the following steps:

[0009] S100. Obtaining work order information of electrode processing from the electrode milling programming section;

[0010] S200. Acquire and analyze electrode properties and tool parameters in the work order information;

[0011] S300. Implementing a grouping operation according to the electrode properties and the tool parameter analysis results;

[0012] S400. Output electrode grouping results.

[0013] Preferably, the step S200 specifically includes the following contents:

[0014] S210. Complete and correct the data in the work order information;

[0015] S220. Converting the processed data into a network model under a graph theory framework; the network model uses each electrode as an independent node in the network; the edges between the independent nodes are constructed based on the similarity between the electrodes, and the corresponding edge weights are assigned to reflect the strength of the similarity;

[0016] S230. Define an algorithm for network modularity according to the network model of step S220.

[0017] Preferably, the weight of the edge is expressed as follows:

[0018] W i,j =δ 1 ·A i,j +δ 2 ·B i,j +δ 3 ·(θ 1 ·C i,j +θ 2 ·D i,j )

[0019] Where: W i,j A is used to represent the weight of the edge between node i and node j; i,j It is used to characterize the similarity between the machining accuracy requirements of the i-th electrode and the j-th electrode; B i,j It is used to indicate whether the i-th electrode and the j-th electrode belong to the same workpiece; C i,j It is used to characterize the similarity between the tool types of the i-th electrode and the j-th electrode; D i,j It is used to characterize the similarity between the tool processing sequence of the i-th electrode and the j-th electrode; δ 1 ,δ 2 ,δ 3 and δ 4 are the proportions of the above four cases in the total weight, and δ 1 +δ 2 +δ 3 +δ 4 =1.

[0020] The network modularity is expressed as follows:

[0021]

[0022] Wherein, Q is used to characterize the modularity of the network; W i,j k is used to represent the weight of the edge between node i and node j; i and k j The table is used to represent the degree of node i and node j in the network; m is used to represent the number of edges in the entire network; δ(E i ,E j ) is used to determine whether nodes i and j belong to the same sub-network. If nodes i and j belong to the same sub-network, then δ(E i ,E j )=1, otherwise, δ(E i ,E j )=0.

[0023] Preferably, the step S300 specifically includes the following contents:

[0024] S310. Initialize parameters; set the number of electrodes to be processed; set the maximum number of nodes in a single group; set the number of nodes in the network structure to the number of electrodes to be processed; the node set is expressed as follows:

[0025] E={E 1 ,E 2 .....E n}

[0026] Wherein: E is used to represent the node set; n is used to represent the number of nodes in the network structure;

[0027] S320. Perform initial sub-network division; divide a sub-network into a node to construct an initial sub-network set; calculate the network modularity of the initial sub-network set; the initial sub-network set is expressed as follows:

[0028] S={S 1 ,S 2 .....S n}

[0029] Wherein: S is used to characterize the initial sub-network set;

[0030] S330. Initialize the loop parameter and set the value of the loop parameter to 0;

[0031] S340. Determine whether the value of the cycle parameter is less than the number of nodes in the network structure, and then perform the following operations according to the determination result:

[0032] If it is determined that the value of the cycle parameter is less than the number of nodes in the network structure, executing step S350;

[0033] If it is determined that the value of the cycle parameter is not less than the number of nodes in the network structure, executing step S360;

[0034] S350. Determine node E i The sub-network to which it belongs, and changes the node E according to the judgment result i The sub-network to which the network belongs, and the number of the sub-network is recorded; the value of the cycle parameter is increased by 1, and then step S340 is executed;

[0035] S360. Determine for all nodes whether the sub-network they belong to is the same as the previous round, and then perform the following operations according to the determination result:

[0036] If the numbers of the sub-networks to which all nodes belong are the same as in the previous round, step S370 is executed;

[0037] If the numbers of the sub-networks to which all nodes belong are different from those in the previous round, step S330 is executed;

[0038] S370. Generate a new network structure, merge all nodes with the same sub-network number to form a new node number; set the number of nodes in the network structure to the number of updated network nodes;

[0039] S380. Calculate the network modularity of the merged network, then compare the calculated result with the original value of the network modularity, and perform the following operations according to the comparison result:

[0040] If the network modularity of the merged network is greater than the original value of the network modularity, executing step S330;

[0041] If the network modularity of the merged network is not greater than the original value of the network modularity, executing step S390;

[0042] S390. Output the new network structure and obtain the grouping result of the child nodes.

[0043] Preferably, step S350 specifically includes the following sub-processes:

[0044] S351. The node E i Removed from the original subnetwork to which it belongs and associated with the node E i The connected sub-networks are merged; and the increments of the network modularity of the merged networks are calculated respectively;

[0045] S352. Find the node E with the largest increment in the network modularity. i the connected sub-networks;

[0046] S353. The node E i The sub-network to which it belongs is changed to the sub-network found in step S352, and the number of the updated sub-network is recorded;

[0047] S354. Add 1 to the value of the loop parameter, and then execute step S340.

[0048] Compared with the prior art, the present invention has the following advantages:

[0049] The implementation of the present invention can provide a scientific basis and intelligent means for batch decision-making in electrode milling batch processing, which can effectively improve processing efficiency and reduce processing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of CNC milling batch processing and tool library configuration of a specific embodiment of the present invention;

[0051] Figure 2 A weighted network diagram of a specific embodiment of the present invention;

[0052] Figure 3 It is a schematic diagram of the implementation flow of the electrode grouping operation of a specific embodiment of the present invention;

[0053] Figure 4 It is a flowchart of an electrode grouping method based on a community partitioning algorithm according to a specific embodiment of the present invention. DETAILED DESCRIPTION

[0054] The present invention is further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0055] The batch decision method of electrode milling batch processing based on community partition algorithm, such as Figure 3 As shown, the following steps are included:

[0056] S100. Obtaining work order information of electrode processing from the electrode milling programming section.

[0057] S200. Obtain and analyze electrode properties and tool parameters in the work order information.

[0058] S300. Implement grouping operation according to the analysis results of electrode properties and tool parameters.

[0059] S400. Output electrode grouping results.

[0060] It should be noted that step S200 specifically includes the following contents:

[0061] S210. Complete and correct the data in the work order information.

[0062] S220. Convert the processed data into a network model under the framework of graph theory; the network model uses each electrode as an independent node in the network; the edges between independent nodes are constructed based on the similarity between the electrodes, and the corresponding edge weights are assigned to reflect the strength of the similarity.

[0063] S230. Define an algorithm for network modularity based on the network model of step S220.

[0064] It should be noted that the data collected in step S100 includes information such as electrode machining tool characteristics, machining accuracy requirements, equipment performance, etc. These data can be collected from the electrode milling programming section using automated means.

[0065] It should be further explained that the data collected in step S100 may be missing or abnormal. In order to ensure the data quality and the accuracy of the analysis, the data needs to be supplemented and corrected.

[0066] It should be noted that each electrode is used as an independent node in the network, and the edges between nodes are constructed based on the similarity between the electrodes, and the corresponding weights are given to reflect the strength of this similarity, thus forming a weighted network graph. By constructing a weighted network, not only can the relationship between electrodes be intuitively displayed, but also a model basis can be provided for the subsequent algorithm implementation.

[0067] It should be further explained that the weight of the edge is expressed as formula 1:

[0068] W i,j =δ 1 ·A i,j +δ 2 ·B i,j +δ 3 ·(θ 1 ·C i,j +θ 2 ·D i,j ) (1)

[0069] Where: W i,j A is used to represent the weight of the edge between node i and node j; i,j It is used to characterize the similarity between the machining accuracy requirements of the i-th electrode and the j-th electrode; B i,j It is used to indicate whether the i-th electrode and the j-th electrode belong to the same workpiece; C i,jIt is used to characterize the similarity between the tool types of the i-th electrode and the j-th electrode; D i,j It is used to characterize the similarity between the tool processing sequence of the i-th electrode and the j-th electrode; δ 1 ,δ 2 ,δ 3 and δ 4 are the proportions of the above four cases in the total weight, and δ 1 +δ 2 +δ 3 +δ 4 =1.

[0070] The network modularity is expressed as follows:

[0071]

[0072] Among them, Q is used to characterize the network modularity; W i,j Used to represent the weight of the edge between node i and node j; k i and k j The table is used to represent the degree of node i and node j in the network; m is used to represent the number of edges in the entire network; δ(E i ,E j ) is used to determine whether nodes i and j belong to the same sub-network. If nodes i and j belong to the same sub-network, then δ(E i ,E j )=1, otherwise, δ(E i ,E j )=0.

[0073] like Figure 2 As shown, it needs to be further explained that, taking a simple network composed of four electrodes as an example: there are 4 nodes and 6 edges. When two electrodes are divided into one sub-network, it means that there is a high similarity between them.

[0074] According to the above definition, the adjacency matrix is ​​generated as follows: i,j =W j,i , the adjacency matrix is ​​the basis for subsequent network analysis.

[0075]

[0076] It should be noted that if Figure 4 As shown, step S300 specifically includes the following contents:

[0077] S310. Initialize parameters; set the number of electrodes to be processed; set the maximum number of nodes in a single group; set the number of nodes in the network structure to the number of electrodes to be processed; the node set is expressed as formula 4:

[0078] E={E1 ,E 2 .....E n} (4)

[0079] Among them: E is used to represent the node set; n is used to represent the number of nodes in the network structure.

[0080] S320. Perform initial sub-network division; divide a sub-network into a node to construct an initial sub-network set; calculate the network modularity of the initial sub-network set; the initial sub-network set is expressed as Formula 5:

[0081] S={S 1 ,S 2 .....S n} (5)

[0082] Where: S is used to characterize the initial sub-network set.

[0083] S330. Initialize loop parameters and set the values ​​of the loop parameters to 0.

[0084] S340. Determine whether the value of the loop parameter is less than the number of nodes in the network structure, and then perform the following operations according to the determination result:

[0085] If it is determined that the value of the loop parameter is less than the number of nodes in the network structure, step S350 is executed.

[0086] If it is determined that the value of the loop parameter is not less than the number of nodes in the network structure, step S360 is executed.

[0087] S350. Determine node E i The sub-network to which it belongs, and changes node E according to the judgment result i The subnetwork to which the network belongs is found, and the subnetwork number is recorded; the value of the loop parameter is increased by 1, and then step S340 is executed.

[0088] S360. Determine for all nodes whether the subnetwork they belong to is the same as the previous round, and then perform the following operations based on the determination result:

[0089] If the numbers of the sub-networks to which all nodes belong are the same as in the previous round, step S370 is executed.

[0090] If the numbers of the sub-networks to which all nodes belong are different from those in the previous round, step S330 is executed.

[0091] S370. Generate a new network structure, merge all nodes with the same sub-network number to form a new node number; set the number of nodes in the network structure to the updated number of network nodes.

[0092] S380. Calculate the network modularity of the merged network, then compare the calculated result with the original value of the network modularity, and perform the following operations according to the comparison result:

[0093] If the network modularity of the merged network is greater than the original value of the network modularity, step S330 is executed.

[0094] If the network modularity of the merged network is not greater than the original value of the network modularity, step S390 is executed.

[0095] S390. Output the new network structure and obtain the grouping result of the child nodes.

[0096] It should be further explained that step S350 specifically includes the following sub-processes:

[0097] S351. Node E i Removed from the original subnetwork to which it belongs and connected with node E i The connected sub-networks are merged; the increment of the network modularity of the merged network is calculated respectively.

[0098] S352. Find the node E with the largest increment in network modularity i Connected subnetworks.

[0099] S353. Node E i The sub-network to which it belongs is changed to the sub-network found in step S352, and the number of the updated sub-network is recorded.

[0100] S354. Add 1 to the value of the loop parameter, and then execute step S340.

[0101] It should be further explained that in step S351, i The connected subnetwork is denoted as {S r ,S s ,S t ,...}; the increment of the network modularity of the merged network is recorded as {ΔQ r ,ΔQ s ,ΔQ t ,...}.

[0102] The node with the largest network modularity increment in step S352 is node E. i The connected subnetwork is denoted as S max , where S max ∈{S r ,S s ,S t ,...}.

[0103] In step S353, node E i The subnetwork to which it belongs becomes Smax , and record the number, i.e. E i ∈S max ,S' i ∈S max .

[0104] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly stated in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0105] The disclosed embodiments are described above to enable any person skilled in the art to implement or use the present invention. Various modifications of these embodiments are obvious to those skilled in the art, and the general principles defined herein may also be applied to other embodiments without departing from the spirit and scope of the present disclosure. Therefore, the present disclosure is not limited to the embodiments given herein, but is consistent with the broadest scope of the principles and novel features disclosed in this application.

[0106] The above description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including", just as "including," is explained as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to mean "non-exclusive or".

[0107] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A batch decision method for electrode milling batch processing based on community partitioning algorithm, characterized by: The following steps are involved: S100. Obtaining work order information of electrode processing from the electrode milling programming section; S200. Acquire and analyze electrode properties and tool parameters in the work order information; S300. Implementing a grouping operation according to the electrode properties and the tool parameter analysis results; S400. Output electrode grouping results.

2. The electrode milling batch processing batch decision method based on community partition algorithm according to claim 1 is characterized by: The step S200 specifically includes the following contents: S210. Complete and correct the data in the work order information; S220. Converting the processed data into a network model under a graph theory framework; the network model uses each electrode as an independent node in the network; The edges between the independent nodes are constructed based on the similarity between the electrodes, and the weights of the corresponding edges are assigned to reflect the strength of the similarity; S230. Define an algorithm for network modularity according to the network model of step S220.

3. The electrode milling batch processing batch decision method based on community partition algorithm according to claim 2 is characterized by: The weight of the edge is expressed as follows: W i,j =δ1·A i,j +δ2·B i,j +δ3·(θ1·C i,j +θ2·D i,j ) Where: W i,j A is used to represent the weight of the edge between node i and node j; i,j It is used to characterize the similarity between the machining accuracy requirements of the i-th electrode and the j-th electrode; B i,j It is used to indicate whether the i-th electrode and the j-th electrode belong to the same workpiece; C i,j It is used to characterize the similarity between the tool types of the i-th electrode and the j-th electrode; D i,j It is used to characterize the similarity between the tool processing sequence of the i-th electrode and the j-th electrode; δ1, δ2, δ3 and δ4 are the proportions of the above four cases in the total weight, and δ1+δ2+δ3+δ4=1; The network modularity is expressed as follows: Wherein, Q is used to characterize the modularity of the network; W i,j k is used to represent the weight of the edge between node i and node j; i and k j The tables are used to represent the degree of nodes i and j in the network; m is used to represent the number of edges in the entire network; δ(E i ,E j ) is used to determine whether nodes i and j belong to the same sub-network. If nodes i and j belong to the same sub-network, then δ(E i ,E j )=1, otherwise, δ(E i ,E j )=0.

4. The electrode milling batch processing batch decision method based on community partition algorithm according to claim 3 is characterized by: The step S300 specifically includes the following contents: S310. Initialize parameters; set the number of electrodes to be processed; set the maximum number of nodes in a single group; set the number of nodes in the network structure to the number of electrodes to be processed; the node set is expressed as follows: E={E1,E2.....E n } Wherein: E is used to represent the node set; n is used to represent the number of nodes in the network structure; S320. Perform initial sub-network division; divide a sub-network into a node to construct an initial sub-network set; calculate the network modularity of the initial sub-network set; the initial sub-network set is expressed as follows: S={S1,S2.....S n } Wherein: S is used to characterize the initial sub-network set; S330. Initialize the loop parameter and set the value of the loop parameter to 0; S340. Determine whether the value of the cycle parameter is less than the number of nodes in the network structure, and then perform the following operations according to the determination result: If it is determined that the value of the cycle parameter is less than the number of nodes in the network structure, executing step S350; If it is determined that the value of the cycle parameter is not less than the number of nodes in the network structure, executing step S360; S350. Determine node E i The sub-network to which it belongs, and changes the node E according to the judgment result i The sub-network to which the network belongs, and the number of the sub-network is recorded; the value of the cycle parameter is increased by 1, and then step S340 is executed; S360. Determine for all nodes whether the sub-network they belong to is the same as the previous round, and then perform the following operations according to the determination result: If the numbers of the sub-networks to which all nodes belong are the same as in the previous round, step S370 is executed; If the numbers of the sub-networks to which all nodes belong are different from those in the previous round, step S330 is executed; S370. Generate a new network structure, merge all nodes with the same sub-network number to form a new node number; set the number of nodes in the network structure to the number of updated network nodes; S380. Calculate the network modularity of the merged network, then compare the calculated result with the original value of the network modularity, and perform the following operations according to the comparison result: If the network modularity of the merged network is greater than the original value of the network modularity, executing step S330; If the network modularity of the merged network is not greater than the original value of the network modularity, executing step S390; S390. Output the new network structure and obtain the grouping result of the child nodes.

5. The electrode milling batch processing batch decision method based on community partition algorithm according to claim 4 is characterized by: Step S350 specifically includes the following sub-processes: S351. The node E i Removed from the original subnetwork to which it belongs and associated with the node E i The connected sub-networks are merged; and the increments of the network modularity of the merged network are calculated respectively; S352. Find the node E with the largest increment in the network modularity. i the connected sub-networks; S353. The node E i The sub-network to which it belongs is changed to the sub-network found in step S352, and the number of the updated sub-network is recorded; S354. Add 1 to the value of the loop parameter, and then execute step S340.

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