PTFE thin-walled part cutting processing error analysis and process optimization method

Abstract

The application provides a PTFE thin-wall part cutting processing error analysis and process optimization method, first, by constructing a weighted directed complex network model, the discrete processing equipment, processing features and process relationship are integrated into a unified analysis framework, the generation and transmission path of the processing error are realized, and the isolated and one-sided limitations of the error analysis are overcome; the topological property analysis and Shannon information entropy theory are introduced, the network structure characteristics are quantified as the influence entropy value of the node, the target key node which has the greatest influence on the processing quality stability can be accurately identified, and the uncertainty of relying on experience judgment is avoided; based on the path support provided by the network model, the error transmission chain can be traced back from the identified target key node, and the error transmission strength of each node on the backtracking path is calculated, so that the error source node causing the quality problem is accurately positioned, and a clear target is provided for subsequent process optimization.

Description

Technical Field

[0001] This invention relates to the field of high-performance manufacturing technology for core components of integrated circuit manufacturing equipment, and in particular to a method for error analysis and process optimization of PTFE thin-walled parts. Background Technology

[0002] PTFE (Polytetrafluoroethylene) thin-walled components are core components of bellows pumps in integrated circuit manufacturing equipment, widely used in conveying systems for ultra-clean process media (such as strong acids and alkalis). Currently, they are mainly manufactured using turning technology. However, due to the low elastic modulus, poor thermal conductivity, and high elasticity of PTFE material, problems such as tool deformation and thermal deformation easily occur during cutting, seriously affecting the stability of machining quality. These machining errors not only directly lead to poor dimensional and positional accuracy of the thin-walled components themselves, but also, through the error transmission and accumulation in multi-process series, ultimately affect the overall performance, sealing performance, and service life of the thin-walled components.

[0003] The machining of PTFE thin-walled parts involves multiple processes, from rough turning of the outer diameter to finishing of the inner and outer contours, and is a typical multi-process, multi-step sequential process. In this process, machining errors are transmitted between processes through complex mechanisms and affect the machining results of subsequent processes, making it difficult to accurately identify the core processes or key equipment that have the most critical impact on the final machining quality.

[0004] Therefore, how to identify errors in the cutting process and how to optimize the machining process have become urgent problems to be solved. Summary of the Invention

[0005] This invention provides a method for error analysis and process optimization in the machining of PTFE thin-walled parts, in order to solve the technical problem in the prior art that it is difficult to quantitatively identify and locate the key sources of influence on the final machining quality due to the complex transmission of machining errors between processes.

[0006] To achieve the above objectives, the technical solution of this invention is as follows: In a first aspect, the present invention provides a method for error analysis and process optimization in the machining of PTFE thin-walled parts, the method comprising: To obtain the processing route of PTFE thin-walled parts, a weighted directed complex network model of the processing route is constructed by abstracting and numbering the processing equipment and processing features in the processing process. The nodes of the weighted directed complex network model represent processing features or processing equipment, the directed edges between adjacent nodes represent the process sequence, and the weight of the directed edges represents the error propagation intensity. A topological characteristic analysis is performed on the weighted directed complex network model to obtain the topological characteristic indices of each node; the topological characteristic indices include node degree and node strength. Based on the aforementioned topological characteristic indicators, the Shannon information entropy theory is used to calculate the entropy value of each node's influence on the stability of processing quality, and the target key nodes are identified based on the influence entropy value. Error source tracing is performed on the target key nodes, the error propagation intensity of each node on the backtracking path is calculated, and the error source nodes are determined; Based on the error source node, locate the corresponding target processing equipment or target processing feature, and optimize and adjust the operating parameters of the target processing equipment or the process parameters of the target processing feature.

[0007] In some embodiments, obtaining the processing route of PTFE thin-walled parts involves abstracting and defining the processing equipment and processing features sequentially, and constructing a weighted directed complex network model of the processing route, including: The processing route is identified and divided to obtain a process list; each process in the process list is deconstructed to obtain the processing equipment and the processing features; the processing equipment and the processing features are defined as nodes of the network, and the processing order and error propagation relationship between the nodes are defined as directed edges, constructing an initial network structure containing a set of nodes and a set of directed edges; according to a preset numbering rule, all nodes in the initial network structure are normalized and numbered to obtain the weighted directed complex network model.

[0008] In some embodiments, the processing equipment includes at least a machine tool, a fixture, and a cutting tool; the weight calculation process for the directed edge is as follows: Based on the actual and permissible spindle rotation error, positioning error, and repeatability error of the machine tool, the machine tool error propagation weight is calculated; based on the actual cutting time and permissible cutting time of the cutting tool, the cutting tool error propagation weight is calculated; based on the actual and designed machining dimensions and surface roughness of the fixture, the fixture error propagation weight is calculated; based on the actual and designed machining dimensions, form and position errors, and surface roughness of the machining features, the comprehensive error propagation weight of the machining features is calculated; for machining processes where wall thickness is the key evaluation criterion, based on the deviation between the actual and designed form and position errors of the PTFE thin-walled parts, the deviation between the actual and designed surface roughness, and the deviation between the actual and designed wall thickness dimensions, the wall thickness feature error propagation weight is calculated.

[0009] In some embodiments, the formula for calculating the machine tool error propagation weight is as follows: ; In the formula, Weights are passed to machine tool errors. This represents the actual spindle rotation error of the machine tool. These represent the actual positioning errors in the X, Y, and Z directions, respectively. These are the actual repeatability errors in the X, Y, and Z directions, respectively. This represents the ideal spindle rotation error of the machine tool. These represent the ideal positioning errors in the X, Y, and Z directions, respectively. These are the ideal repeatability errors in the X, Y, and Z directions, respectively. The formula for calculating the tool error propagation weight is as follows: ; In the formula, Weighting is passed on to tool error. The actual cutting time of the tool. This refers to the allowable cutting time for the cutting tool; The formula for calculating the fixture error propagation weight is as follows: ; In the formula, Weighting is used to transfer fixture errors. These are the actual machining dimensions and actual surface roughness of the fixture, respectively. The design machining dimensions and surface roughness of the fixture; The formula for calculating the weighting of the processing feature error propagation is as follows: ; In the formula, Weights are passed to the overall error of the processing features. These represent the actual machining dimensions, actual form and position errors, and actual surface roughness of the PTFE thin-walled part, respectively. These represent the design machining dimensions, design geometric tolerances, and design surface roughness of PTFE thin-walled parts, respectively. The formula for calculating the weighting of the wall thickness characteristic error is as follows: ; In the formula, This indicates the weighting of the wall thickness characteristic error propagation. and These represent the actual wall thickness and the designed wall thickness of the PTFE thin-walled component, respectively.

[0010] In some embodiments, the topological characteristic analysis of the weighted directed complex network model to obtain the topological characteristic indices of each node includes: Based on the weighted directed complex network model, the node degree of each node is calculated; the node degree is the sum of the out-degree and in-degree of the node, where the out-degree is the number of directed edges from the node to other nodes, and the in-degree is the number of directed edges from other nodes to the node; based on the weighted directed complex network model, the node strength of each node is calculated; the node strength is the sum of the out-strength and in-strength of the node, where the out-strength is the sum of the weights of all directed edges from the node, and the in-strength is the sum of the weights of all directed edges pointing to the node.

[0011] In some embodiments, the step of calculating the influence entropy value of each node on the processing quality stability based on the topological characteristic index using Shannon information entropy theory, and identifying target key nodes based on the influence entropy value, includes: For each initial key node in the initial key node set, a weighted clustering coefficient is calculated based on network adjacency and edge weights. The weighted clustering coefficient is then normalized to obtain an influence coefficient characterizing the degree of influence of the initial key node on processing quality stability. Based on Shannon's information entropy theory, the node entropy value of each initial key node is calculated using the influence coefficient as a probability distribution. The SND algorithm is then used to evaluate the importance of nodes based on their entropy values, and initial key nodes with entropy values ​​higher than a preset threshold are identified as the target key nodes.

[0012] In some embodiments, the weighted clustering coefficient is calculated using the following formula: ; In the formula, This is the weighted clustering coefficient; Indicates node strength; Indicates node degree; Represents a node To the node The weight of the connected edges, Represents a node To the node The weight of the connected edges; Represents a node To the node The process route, Represents a node To the node The process route, Represents a node To the node The process route, where n represents the total number of nodes in the weighted directed complex network model; The formula for calculating the influence coefficient is as follows: ; In the formula, This is the influence coefficient; The formula for calculating the entropy value is as follows: ; In the formula, This represents a node in a weighted directed complex network model. The influence of entropy value.

[0013] In some embodiments, the step of tracing the error source of the target key node, calculating the error propagation intensity of each node on the backtracking path, and determining the error source node includes: Starting from the target key node, backtracking is performed along the directed edge to a preset backtracking depth, forming a backtracking node set; the error propagation coefficient of each backtracking node in the backtracking node set is calculated; the error propagation coefficient is defined as the ratio of the in-strength to the out-strength of the backtracking node; where the out-strength represents the node's ability to propagate error, and the in-strength represents the node's ability to absorb error; for each propagation path from the backtracking node to the target key node, the error propagation strength of the backtracking node is calculated; the error propagation strength is defined as the product of the out-strength of the starting node of the propagation path and the error propagation coefficients of all nodes on the propagation path; the backtracking node with the largest strength value from the error propagation strength is selected as the error source node.

[0014] In some embodiments, the formula for calculating the error propagation coefficient is as follows: ; In the formula, Indicates the error propagation coefficient. Represents a node The output strength, Represents a node The intensity of penetration; This represents the sum of all directed edges; Represents a node To the node The weight of the connected edges, Represents a node To the node The weight of the connected edges, Represents a node To the node The process route, Represents a node To the node The process route, if the node and If they are connected and All equal to 1, if not connected and All equal to 0; The formula for calculating the error propagation strength is as follows: ; In the formula, Indicates the strength of error propagation. For nodes The output strength, It is a set of backtracking nodes The first in All nodes of the path, Representing nodes in the error tracing process To the target key node any node in the path, Represents any node in the error tracing process. The error propagation coefficient, This represents the cumulative effect of the error propagation coefficients at all nodes during the error sourcing process.

[0015] In some embodiments, the process list includes external turning, turning external contours, internal turning, turning internal contours, and bottom surface machining; the machining features include end faces, external circles, external contours, and internal contours.

[0016] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: The present invention provides a method for error analysis and process optimization in the machining of PTFE thin-walled parts. Firstly, by constructing a weighted directed complex network model, discrete machining equipment, machining features, and process relationships are integrated into a unified analysis framework. This achieves a systematic and visual description of the generation and propagation paths of machining errors, overcoming the limitations of isolated and one-sided error analysis in traditional methods. Simultaneously, topological characteristic analysis and Shannon's information entropy theory are introduced to quantify network structure characteristics into the influence entropy values ​​of nodes. This accurately identifies the target critical nodes that have the greatest impact on machining quality stability, avoiding the uncertainty of relying on empirical judgment. Furthermore, based on the path support provided by the network model, the error propagation chain can be traced backward from the identified target critical nodes. By calculating the error propagation intensity of each node on the backtracking path, the error source node causing quality problems can be accurately located, providing a clear target for subsequent process optimization. Finally, by mapping the abstract error source nodes back to specific machining equipment or machining features, operating parameters or process parameters are adjusted to target the root cause of the error, thereby significantly improving the pertinence and effectiveness of optimization measures. This provides a solid theoretical basis and technical support for achieving precise and efficient machining of PTFE thin-walled parts. Attached Figure Description

[0017] Figure 1This is a flowchart illustrating the method for error analysis and process optimization of PTFE thin-walled parts provided in this embodiment of the invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] In the following description, references to "some embodiments" refer to a subset of all possible embodiments; however, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. Unless otherwise defined, all technical and scientific terms used in the embodiments of the invention have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of the invention pertain. The terminology used in the embodiments of the invention is for the purpose of describing the embodiments of the invention only and is not intended to limit the invention.

[0020] It should be noted that PTFE thin-walled parts refer to parts or components made of polytetrafluoroethylene (PTFE) whose wall thickness to their maximum outline dimensions (such as diameter, length, and width) meets the thin-walled characteristic. Generally, the industry considers the wall thickness to feature dimension ratio to be ≤1:10, but this can be adjusted according to the actual functional requirements of the part. These parts are extremely prone to deformation during machining due to cutting forces, clamping forces, or internal stress, requiring extremely high machining accuracy and stability.

[0021] The following describes exemplary applications of the PTFE thin-walled part machining error analysis and process optimization equipment according to embodiments of the present invention. This equipment can be implemented as a terminal or a server. In one implementation, the PTFE thin-walled part machining error analysis and process optimization equipment can be implemented as various types of terminals such as laptops, tablets, desktop computers, and mobile devices. In another implementation, the equipment can also be implemented as a server. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiments of the present invention. The following will describe exemplary applications of the PTFE thin-walled part machining error analysis and process optimization equipment as a server.

[0022] This invention provides a method for error analysis and process optimization in the machining of thin-walled PTFE parts. (See also...) Figure 1 , Figure 1 This is a flowchart illustrating a method for error analysis and process optimization in the machining of PTFE thin-walled parts, provided in an embodiment of the present invention. Figure 1 The steps shown are explained.

[0023] Step S110: Obtain the processing route of the PTFE thin-walled part. By abstracting and defining and numbering the processing equipment and processing features in the processing process, a weighted directed complex network model of the processing route is constructed. The nodes of the weighted directed complex network model represent processing features or processing equipment, the directed edges between adjacent nodes represent the process sequence, and the weight of the directed edges represents the error propagation intensity.

[0024] In some implementations, the processing route refers to a set of ordered processing steps planned to process PTFE thin-walled parts from raw materials to finished products. In actual processing scenarios, the various processing steps required from the blank to the finished product, the equipment used, and the sequence of these steps are specified.

[0025] For the application scenarios of this invention, the turning process for PTFE thin-walled parts can sequentially include the following five steps: external turning, turning the external contour, turning the internal hole, turning the internal contour, and bottom surface machining. Steps 1 to 4 are performed on a CNC lathe, mainly machining the main structure of the PTFE thin-walled part; step 5 is performed on a machining center, mainly finishing the bottom end face of the PTFE thin-walled part.

[0026] In some implementations, processing equipment refers to mechanical, electrical, or thermal equipment used to perform specific processing actions during the processing of PTFE thin-walled parts. It is the core execution carrier of the processing process route, and its performance parameters directly affect the processing quality. These processing actions include, but are not limited to, cutting, forming, heat treatment, and surface treatment.

[0027] In the application scenario of this invention, the processing equipment can be a CNC lathe for turning outer circles, inner holes, and end faces; a CNC milling machine for milling planes and grooves; or a wire cutting machine for processing complex irregular holes.

[0028] In some implementations, processing features refer to structural units with specific geometric shapes or functional attributes that are gradually formed during the processing of PTFE thin-walled parts. They are the core objectives of the processing steps and the key basis for judging whether the parts meet the design requirements. For example, geometric features include: outer circles, such as the cylindrical surface of a PTFE thin-walled shaft; inner holes, such as the center hole of a PTFE thin-walled bushing; and surface quality features such as surface roughness and surface finish.

[0029] In some implementations, the weighted directed complex network model refers to a mathematical model constructed from the PTFE thin-walled part processing route to describe the relationship between processing equipment and processing features and the error propagation law. It includes nodes, directed edges, and weights, and can intuitively reflect the correlation and dynamism of the processing process.

[0030] Here, nodes are the basic units of the weighted directed complex network model, divided into machining equipment nodes and machining feature nodes. A directed edge is a directed line segment connecting adjacent nodes, representing the sequence of machining operations, with the direction from the preceding operation node to the following operation node. For example, the directed edge from CNC lathe A (machining equipment node) to rough machining of inner hole B (machining feature node) represents the sequence of operations using CNC lathe A to complete the rough machining of inner hole B. The weight represents the strength of error propagation from preceding node operations to subsequent node operations; the larger the value, the more significant the impact of errors generated by preceding operations on the machining quality of subsequent operations.

[0031] In some implementations, error propagation intensity refers to the degree to which machining errors (such as dimensional deviations, shape errors, positional deviations, etc.) generated in the preceding process are propagated to the subsequent process (corresponding to the subsequent node in the model) during the machining of PTFE thin-walled parts, and affect the machining quality of the subsequent process. It is usually obtained through experimental measurement or process simulation calculation.

[0032] Step S120: Perform topology characteristic analysis on the weighted directed complex network model to obtain the topology characteristic index of each node; the topology characteristic index includes node degree and node strength.

[0033] In some implementations, topology characteristic analysis refers to the process of quantitatively analyzing the structural characteristics of a weighted directed complex network model based on graph theory, and mining the importance of nodes in the model, such as whether a certain node plays a key role in the stability of the entire network.

[0034] In some implementations, topology characteristic indicators refer to specific parameters obtained through topology characteristic analysis that are used to quantitatively describe the importance of node structures. They are core quantitative indicators for evaluating the impact of nodes on the stability of the processing route. In this invention, topology characteristic indicators include node degree and node strength.

[0035] In some implementations, node degree refers to the number of edges connecting a node to other nodes in a weighted directed complex network model. It is used to intuitively reflect the connectivity activity of a node (the more edges connected, the more active the node is in the network). Since the model is a directed network, node degree is further divided into in-degree and out-degree.

[0036] In some implementations, node strength refers to the sum of the weights of all the edges connecting a node in a weighted directed complex network model. It is an advanced index that combines node degree with error propagation strength, used to quantify the actual influence of a node (the larger the sum of weights, the more significant the node's total contribution to error propagation). It is also divided into ingress strength and outgress strength.

[0037] Step S130: Based on the topological characteristic index, calculate the influence entropy value of each node on the stability of processing quality using Shannon information entropy theory, and identify the target key node based on the influence entropy value.

[0038] In some implementations, Shannon's information entropy theory is used to transform node topology characteristics into the degree of influence of nodes on processing quality stability. By calculating the influence entropy value of nodes, the uncertainty contribution of nodes to processing quality stability is quantified. Here, the higher the influence entropy value, the more significant the influence of nodes on processing quality stability.

[0039] In some implementations, the influence entropy value refers to a numerical value calculated based on Shannon's information entropy theory, using the topological indices of nodes as input, to quantify the degree of influence of nodes on the machining quality stability of PTFE thin-walled parts. In practical applications, the magnitude of the influence entropy value is positively correlated with the degree of influence of the node; that is, the higher the influence entropy value, the greater the impact of the node's topological characteristics on the uncertainty of machining quality stability, and thus the more critical the node. For example, if the influence entropy value of the machine tool node is 1.8 and the influence entropy value of the cutting tool is 0.5, it indicates that the influence of the machine tool node on machining quality stability is significantly greater than that of the cutting tool node.

[0040] In some implementations, the target critical node refers to the node in the weighted directed complex network model whose entropy value exceeds a preset threshold. This node is the core node that plays a decisive role in the stability of processing quality and is also the key target for subsequent error tracing. This preset threshold can be determined based on factors such as the quality requirements of PTFE thin-walled parts and the complexity of the processing technology; this invention does not impose specific limitations on it.

[0041] Step S140: Perform error source tracing on the target key node, calculate the error propagation intensity of each node on the backtracking path, and determine the error source node.

[0042] In some implementations, error tracing refers to the process of tracing the error propagation path in a weighted directed complex network model backward from the target key node. Its purpose is to locate the initial source node that generates the error, i.e., the error source node.

[0043] It should be noted that this tracing logic needs to follow the reverse path from the subsequent node to the preceding node, and check the error propagation strength of each node on the backtracking path in turn, prioritizing the tracing of paths with high error propagation strength, because paths with high error propagation strength are more likely to be the main channels for error propagation.

[0044] In some implementations, a backtracking path refers to an ordered sequence of nodes that extends backward from the target critical node to the potential error source node during the error tracing process. It is a historical path record of error propagation, and each backtracking path corresponds to a complete error propagation chain.

[0045] In some implementations, the error source node refers to the node that initially generates the error and whose error propagation intensity reaches a preset threshold, and is the fundamental source of error in the processing process.

[0046] Step S150: Locate the target processing equipment or target processing feature corresponding to the error source node, and optimize and adjust the operating parameters of the target processing equipment or the process parameters of the target processing feature.

[0047] In some implementations, the target processing equipment and target processing features refer to the processing equipment and processing features corresponding to the error source node. They are the direct targets of parameter optimization and adjustment. If the error source node is a device node, the corresponding target processing equipment is adjusted and optimized; if the error source node is a feature node, the corresponding target processing feature is adjusted and optimized.

[0048] In some implementations, operating parameters refer to the technical parameters that can be adjusted during the operation of the target processing equipment and directly affect the processing accuracy. The operating parameters of different types of equipment are different, and optimizing these parameters can eliminate or reduce the error output of error source nodes.

[0049] In some implementations, process parameters refer to the technical parameters set for the processing of the target feature that affect the feature forming quality. Optimizing these parameters can directly improve the accuracy of the target feature (such as dimensional accuracy and shape accuracy), thereby eliminating error propagation.

[0050] The present invention provides a method for error analysis and process optimization in the machining of PTFE thin-walled parts. Firstly, by constructing a weighted directed complex network model, discrete machining equipment, machining features, and process relationships are integrated into a unified analysis framework. This achieves a systematic and visual description of the generation and propagation paths of machining errors, overcoming the limitations of isolated and one-sided error analysis in traditional methods. Simultaneously, topological characteristic analysis and Shannon's information entropy theory are introduced to quantify network structure characteristics into the influence entropy values ​​of nodes. This accurately identifies the target critical nodes that have the greatest impact on machining quality stability, avoiding the uncertainty of relying on empirical judgment. Furthermore, based on the path support provided by the network model, the error propagation chain can be traced backward from the identified target critical nodes. By calculating the error propagation intensity of each node on the backtracking path, the error source node causing quality problems can be accurately located, providing a clear target for subsequent process optimization. Finally, by mapping the abstract error source nodes back to specific machining equipment or machining features, operating parameters or process parameters are adjusted to target the root cause of the error, thereby significantly improving the pertinence and effectiveness of optimization measures. This provides a solid theoretical basis and technical support for achieving precise and efficient machining of PTFE thin-walled parts.

[0051] In some embodiments, step S110 may further include steps 111 to 114: Step 111: Identify and divide the processing route to obtain a process list.

[0052] In some implementations, the process list refers to an ordered set of processes arranged in the order of processing after systematically identifying and sorting out the processing route of PTFE thin-walled parts. It is the basic data carrier for deconstructing processing equipment and processing characteristics and building a network model, and clearly marks the sequence number, name and core task of each process.

[0053] Step 112: Deconstruct each process in the process list to obtain the processing equipment and the processing features.

[0054] Step 113: Define the processing equipment and the processing features as nodes of the network, and define the processing order and error propagation relationship between the nodes as directed edges, and construct an initial network structure containing a set of nodes and a set of directed edges.

[0055] In some implementations, the initial network structure refers to a basic network framework based on a list of processes, formed by defining nodes containing processing equipment and processing characteristics, constructing process sequence and error propagation relationships as directed edges. It is an original network model that has not undergone numbering standardization, contains only the core set of nodes and the set of directed edges, and does not involve weight assignment. It is the network topology skeleton for building the processing technology route.

[0056] Step 114: According to the preset numbering rules, plan and number all nodes in the initial network structure to obtain the weighted directed complex network model.

[0057] In some implementations, the preset numbering rule refers to a unified numbering standard set in advance to achieve standardized management of nodes in the initial network structure. The numbering is unique, readable, and relevant, which facilitates subsequent topology analysis, node query, and error tracing of the network model.

[0058] Based on the foregoing embodiments, the processing equipment includes at least a machine tool, a fixture, and a cutting tool; the weight calculation process for the directed edge in step S110 is as follows: Based on the actual and permissible spindle rotation error, positioning error, and repeatability error of the machine tool, the machine tool error propagation weight is calculated; based on the actual cutting time and permissible cutting time of the cutting tool, the cutting tool error propagation weight is calculated; based on the actual and designed machining dimensions and surface roughness of the fixture, the fixture error propagation weight is calculated; based on the actual and designed machining dimensions, form and position errors, and surface roughness of the machining features, the comprehensive error propagation weight of the machining features is calculated; for machining processes where wall thickness is the key evaluation criterion, based on the deviation between the actual and designed form and position errors of the PTFE thin-walled parts, the deviation between the actual and designed surface roughness, and the deviation between the actual and designed wall thickness dimensions, the wall thickness feature error propagation weight is calculated.

[0059] It should be noted that the "tolerance spindle rotation error" in this invention is determined by the machine tool's own settings.

[0060] It should be noted that the "permissible cutting time" and "permissible cutting machining time of the tool" in this invention are based on the cutting conditions and can be determined by consulting the machining process manual or according to the GB / T 16461-2016 standard for single-edge turning tool life test.

[0061] It should be noted that the "designed machining dimensions and surface roughness" in "based on the actual and designed machining dimensions and surface roughness of the fixture", the "designed machining dimensions, form and position errors and surface roughness" in "based on the actual and designed machining dimensions, form and position errors and surface roughness of the machining features", the "deviation of designed wall thickness", the "designed machining dimensions, design form and position tolerances, and design surface roughness of thin-walled parts", the "designed machining dimensions and design surface roughness of the fixture", and the "designed wall thickness" are all given by specific part design drawings.

[0062] It should be noted that the "ideal spindle rotation error", "ideal positioning error" and "ideal repeatability error" of the machine tool in this invention are all determined by the settings of the machine tool itself.

[0063] In some implementations, the formula for calculating the machine tool error transfer weight is as follows: ; In the formula, Weights are passed to machine tool errors. This represents the actual spindle rotation error of the machine tool. These represent the actual positioning errors in the X, Y, and Z directions, respectively. These are the actual repeatability errors in the X, Y, and Z directions, respectively. This represents the ideal spindle rotation error of the machine tool. These represent the ideal positioning errors in the X, Y, and Z directions, respectively. These are the ideal repeatability errors in the X, Y, and Z directions, respectively.

[0064] In some implementations, the formula for calculating the tool error propagation weight is as follows: ; In the formula, Weighting is passed on to tool error. The actual cutting time of the tool. This represents the allowable cutting time for the cutting tool.

[0065] In some implementations, the formula for calculating the fixture error propagation weight is as follows: ; In the formula, Weighting is used to transfer fixture errors. These are the actual machining dimensions and actual surface roughness of the fixture, respectively. The design machining dimensions and surface roughness of the fixture are determined.

[0066] In some implementations, the formula for calculating the processing feature error propagation weight is as follows: ; In the formula, Weights are passed to the overall error of the processing features. These represent the actual machining dimensions, actual form and position errors, and actual surface roughness of the PTFE thin-walled part, respectively. These represent the design and machining dimensions, design geometric tolerances, and design surface roughness of PTFE thin-walled parts, respectively.

[0067] In some implementations, the formula for calculating the wall thickness characteristic error propagation weight is as follows: ; In the formula, This indicates the weighting of the wall thickness characteristic error propagation. and The actual wall thickness and the design wall thickness of PTFE thin-walled parts are represented separately.

[0068] In some embodiments, step S120 may further include steps 121 to 122: Step 121: Based on the weighted directed complex network model, calculate the node degree of each node; the node degree is the sum of the out-degree and in-degree of the node, where the out-degree is the number of directed edges from the node to other nodes, and the in-degree is the number of directed edges from other nodes to the node.

[0069] Step 122: Based on the weighted directed complex network model, calculate the node strength of each node; the node strength is the sum of the outgoing strength and incoming strength of the node, where the outgoing strength is the sum of the weights of all directed edges originating from the node, and the incoming strength is the sum of the weights of all directed edges pointing to the node.

[0070] In some embodiments, step S130 may further include steps 131 to 134: Step 131: For each initial key node in the initial key node set, calculate the weighted clustering coefficient of the initial key node based on the network adjacency relationship and the edge weight.

[0071] In some implementations, the initial critical node set refers to the set of nodes that may have a significant impact on the stability of processing quality, which are initially screened based on the aforementioned topological characteristic indicators (node ​​degree, node strength). This set is determined by setting initial thresholds for node degree and node strength (e.g., node degree ≥ 2, node strength ≥ 1.0), and includes potential critical equipment nodes and critical feature nodes.

[0072] In some implementations, network adjacency refers to the direct connection between a node and other nodes through directed edges in a weighted directed complex network model. This includes two directional relationships: the node pointing to other nodes and other nodes pointing to the node. It is used to determine whether there is a direct error propagation path between nodes.

[0073] In some implementations, the edge weight, or error propagation strength mentioned above, refers to the numerical value attached to the directed edge connecting adjacent nodes in a weighted directed complex network model, used to quantify the degree of error propagation from the preceding node to the succeeding node.

[0074] In some implementations, the weighted clustering coefficient refers to a topological index used in a weighted directed complex network model to quantify the degree of interconnection between adjacent nodes of a given node. It is a refined local structural feature parameter that combines edge weights.

[0075] Step 132: Normalize the weighted aggregation coefficients to obtain the influence coefficients characterizing the degree of influence of the initial key nodes on the stability of processing quality.

[0076] In some implementations, the influence coefficient refers to the normalized value obtained from the weighted aggregation coefficient, and is a quantitative indicator characterizing the degree of influence of the initial critical node on the processing quality stability of PTFE thin-walled parts. The coefficient ranges from 0 to 1. Specifically, as shown in the formula for calculating the influence coefficient, it is inversely proportional to the weighted aggregation coefficient. A larger influence coefficient indicates a more prominent relative influence of the node on processing quality stability after normalization.

[0077] Step 133: Based on Shannon's information entropy theory, using the influence coefficient as the probability distribution, calculate the node entropy value of each initial key node.

[0078] In some implementations, node entropy refers to a numerical value calculated based on Shannon's information entropy theory, using the probability distribution of influence coefficients as input, to quantify the degree of influence of initial critical nodes on the uncertainty of processing quality stability. A higher node entropy value indicates that the node's influence coefficient is more significant in the probability distribution, its critical influence weight on processing quality stability is greater, and the node's importance is higher.

[0079] Step 134: Using the SND algorithm, the importance of nodes is evaluated based on the node entropy value, and the initial key nodes with node entropy values ​​higher than a preset threshold are determined as the target key nodes.

[0080] In some implementations, the preset threshold refers to a critical value of node entropy set in advance for screening target critical nodes. This threshold is determined based on factors such as the quality requirements of PTFE thin-walled parts (e.g., dimensional tolerances, surface roughness), processing complexity, and industry standards, and is used to delineate the boundary between ordinary initial critical nodes and target critical nodes. Nodes with entropy values ​​higher than this threshold are identified as critical nodes that play a core role in the stability of processing quality.

[0081] In some implementations, the weighted clustering coefficient is calculated using the following formula: ; In the formula, This is the weighted clustering coefficient; Indicates node strength; Indicates node degree; Represents a node To the node The weight of the connected edges, Represents a node To the node The weight of the connected edges; Represents a node To the node The process route, Represents a node To the node The process route, Represents a node To the node The process route, where n represents the total number of nodes in the weighted directed complex network model.

[0082] In some implementations, the formula for calculating the influence coefficient is as follows: ; In the formula, This is the influence coefficient.

[0083] In some implementations, the formula for calculating the entropy value is as follows: ; In the formula, This represents a node in a weighted directed complex network model. The influence of entropy value.

[0084] In some embodiments, step S140 may further include steps 141 to 144: Step 141: Starting from the target key node, backtrack along the directed edge to the preset backtracking depth to form a backtracking node set.

[0085] In some implementations, the preset backtracking depth refers to the maximum number of process levels or the maximum number of node jumps that are pre-set in the error tracing process, which is used to limit the backtracking scope and avoid meaningless over-tracing, so as to ensure the efficiency and accuracy of tracing.

[0086] In some implementations, the backtracking node set refers to the set of all nodes covered when backtracking from the target critical node along the directed edges to a predetermined backtracking depth, including potential error source nodes that directly or indirectly transmit errors to the target critical node.

[0087] Step 142: Calculate the error propagation coefficient of each backtracking node in the backtracking node set; the error propagation coefficient is defined as the ratio of the input strength to the output strength of the backtracking node; where the output strength represents the node's ability to propagate errors, and the input strength represents the node's ability to absorb errors.

[0088] In some implementations, a backtracking node refers to any node in the backtracking node set other than the target critical node, i.e., the upstream node of the target critical node (the equipment node or feature node corresponding to the preceding process), which is a potential object for error tracing.

[0089] In some implementations, the error propagation coefficient is used as an indicator to quantify the error propagation efficiency of the backtracking node, and is defined as the ratio of the in-strength to the out-strength of the backtracking node.

[0090] Step 143: For each transmission path from the backtracking node to the target key node, calculate the error transmission strength of the backtracking node; the error transmission strength is defined as the product of the output strength of the starting node of the transmission path and the error transmission coefficient of all nodes on the transmission path.

[0091] In some implementations, the propagation path refers to a unique ordered sequence of nodes that starts from a backtracking node and extends forward along a directed edge to the target critical node. It is the specific channel through which errors are propagated from upstream nodes to the target critical node.

[0092] Step 144: Select the backtracking node with the largest intensity value from the error propagation intensity as the error source node.

[0093] In some implementations, the error propagation coefficient is calculated using the following formula: ; In the formula, Represents a node The output strength, Represents a node The intensity of penetration; Represents a node To the node The weight of the connected edges, This represents the sum of all directed edges. Represents a node To the node The weight of the connected edges, Represents a node To the node The process route, Represents a node To the node The process route, if the node and If they are connected and All equal to 1, if not connected and All are equal to 0.

[0094] In some implementations, the formula for calculating the error propagation strength is as follows: ; In the formula, Indicates the strength of error propagation. For nodes The output strength, It is a set of backtracking nodes The first in All nodes of the path, Representing nodes in the error tracing process To the target key node any node in the path, Represents any node in the error tracing process. The error propagation coefficient, This represents the cumulative effect of the error propagation coefficients at all nodes during the error sourcing process.

[0095] In some embodiments, the process list includes external turning, external contour turning, internal hole turning, internal contour turning, and bottom surface machining.

[0096] The following will describe an exemplary application of the embodiments of the present invention in a practical application scenario.

[0097] This invention provides a method for identifying and tracing machining errors in PTFE thin-walled parts, specifically including the following steps: Step 1: Obtain the processing technology for PTFE thin-walled parts, and assign numbers to the processing features and processing equipment.

[0098] Step 2: Establish a weighted directed complex network model of the PTFE thin-walled part processing route.

[0099] Step 3: Analyze the topological characteristics of the weighted directed complex network model of the PTFE thin-walled part processing route.

[0100] Step 4: Identify key nodes for PTFE thin-walled parts.

[0101] Step 5: Based on steps 3 and 4, trace the source of processing errors.

[0102] In this embodiment, step one above includes the following: The machining process for PTFE thin-walled parts includes turning and milling, with turning being the primary process. The turning process for PTFE thin-walled parts is divided into five steps: external turning, external contour turning, internal hole turning, internal contour turning, and bottom surface machining. Steps 1 to 4 are performed on a CNC lathe, mainly machining the main structure of the PTFE thin-walled part; step 5 is performed on a machining center, mainly finishing the bottom end face of the PTFE thin-walled part.

[0103] The turning process route for PTFE thin-walled parts mainly includes three major types of turning process information: machining equipment, machining features, and machining quality features. Machining equipment includes machine tools, fixtures, and cutting tools; machining features include end faces, outer diameters, outer contours, and inner contours; and machining quality features mainly include dimensional accuracy, geometrical accuracy, and surface accuracy. Simultaneously, the machining geometry of the part and the machining equipment such as machine tools, cutting tools, and fixtures are defined and extracted as nodes in a network, and directed edges are set based on the relationships between these nodes.

[0104] To facilitate description in subsequent networked modeling and analysis of the machining process, a unified numbering system is adopted for machining features and equipment in the PTFE thin-walled part machining process. The specific numbering rules are as follows: Machining features are designated by the abbreviation of the English name of the process, followed by the process number, and then the step numbers are added sequentially according to the step sequence. Each process contains multiple steps, and corresponding numbers are added according to the step sequence to represent the machining feature nodes of the PTFE thin-walled part. For example, TOS04-1 represents the first step of the fourth process, turning out the surface. The numbering rule for machining equipment is the abbreviation of the English name of the machining equipment followed by the machine tool, cutting tool, and fixture number. For example, CT01 represents cutting tool number 1 in the process route.

[0105] In this embodiment, step two includes the following: First, the network features of the PTFE thin-walled part turning process are extracted. The specific steps are as follows: The weighted directed complex network of the PTFE thin-walled part turning process can be represented by a triple G(V, A, W). The node sets of the machining equipment and machining features are represented by... express, The directed edges between the nodes are denoted by A. Directed edge Represents a node To the node The technological route. Adjacency matrix of weighted directed complex networks. , Represents a node To the node The weight of the directed edge between two nodes represents the strength of their interaction.

[0106] To describe the strength of error propagation between key nodes in the turning process of PTFE thin-walled parts, error information from the actual machining process is used to assign values ​​to the edge weights.

[0107] The specific error analysis and edge weight assignment methods for thin-walled parts are as follows: Turning of PTFE thin-walled parts is achieved through the rotation of the machine tool spindle and the feed motion of the machine tool. Machine tool spindle error, positioning error, and repeatability error are all important factors affecting the machining error of the parts. Therefore, the propagation weight of machine tool errors is defined. for: ; In the formula, Weights are passed to machine tool errors. This represents the actual spindle rotation error of the machine tool. These represent the actual positioning errors in the X, Y, and Z directions, respectively. These are the actual repeatability errors in the X, Y, and Z directions, respectively. This represents the ideal spindle rotation error of the machine tool. These represent the ideal positioning errors in the X, Y, and Z directions, respectively. These are the ideal repeatability errors in the X, Y, and Z directions, respectively.

[0108] The formula for calculating the tool error propagation weight is as follows: ; In the formula, Weighting is passed on to tool error. The actual cutting time of the tool. This represents the allowable cutting time for the cutting tool.

[0109] In the machining of thin-walled parts, two main types of fixtures are involved. One is a three-jaw chuck for clamping PTFE bars, and the other is a special fixture for machining thin-walled structures. Before turning, the three-jaw chuck is adjusted to eliminate errors according to different workpiece requirements. At the start of turning, rough turning is performed on the workpiece to eliminate errors such as runout caused by clamping force. For the special fixture, dimensional and surface quality errors have a significant impact on the machining quality of PTFE thin-walled parts; therefore, the error information of the special fixture is used to calculate the fixture error propagation weight. The formula for calculating the fixture error propagation weight is as follows: ; In the formula, Weighting is used to transfer fixture errors. These are the actual machining dimensions and actual surface roughness of the fixture, respectively. The design machining dimensions and surface roughness of the fixture are determined.

[0110] In the multi-stage machining process of thin-walled parts, the error propagation of machining features mainly originates from errors generated by dimensional features, geometrical features, and surface features. Therefore, the dimensional errors, geometrical errors, and surface roughness of each machining feature obtained from actual measurements are selected. The calculation formula for the error propagation weight of machining features is as follows: ; In the formula, Weights are passed to the overall error of the processing features. These represent the actual machining dimensions, actual form and position errors, and actual surface roughness of the PTFE thin-walled part, respectively. These represent the design and machining dimensions, design geometric tolerances, and design surface roughness of PTFE thin-walled parts, respectively.

[0111] Due to the low stiffness of PTFE material, thin-walled PTFE structures will undergo elastic deformation after machining. Measuring and comparing the error between the designed wall thickness and the machined wall thickness of the PTFE thin-walled part exhibiting elastic deformation is quite difficult. The wall thickness of the thin-walled part can be obtained from the design dimensions. It is a fixed value. Therefore, select... This serves as a standard for evaluating the dimensional errors in the machining of thin-walled structures. In all processes, it is necessary to use... The processing procedure is for evaluating the standard. Here, the formula for calculating the weighting of the wall thickness characteristic error is as follows: ; In the formula, This indicates the weighting of the wall thickness characteristic error propagation. and These represent the actual wall thickness and the designed wall thickness of the PTFE thin-walled component, respectively.

[0112] All processing features and equipment involved in the multi-stage processing of PTFE thin-walled parts are abstracted into different network nodes. Then, using the processing sequence and interrelationships between the nodes, network edges are extracted and connection relationships are determined. Finally, the weights of each network edge are calculated using the above formulas and values ​​are assigned to the network edges to establish a weighted directed error propagation network model.

[0113] In this embodiment, step three above includes the following: Based on the weighted directed error propagation network model established in step two, the larger the node in the network and the more edges connected to it, the more concentrated the processing operations or steps are at that node. Furthermore, the thickness of the edges can be used to preliminarily judge the strength of the processing error propagation; that is, the thicker the edge, the greater its weight, and the greater the impact of the processing error of the preceding processing feature on the processing quality of the subsequent processing feature connected to it. To improve the processing quality of PTFE thin-walled parts, it is necessary to comprehensively identify the key feature nodes in the network model. Therefore, key nodes can be preliminarily identified by analyzing the node degree distribution, node degree, node strength, and other characteristics of the network model.

[0114] Node size is directly proportional to node degree, which can be calculated using the following formula: ; In the formula, Indicates node degree. This represents the sum of all directed edges. Represents a node To the node The process route, Represents a node To the node The process route.

[0115] Based on the established weighted transmission network of processing errors in the PTFE thin-walled part process route, the strength of each node can be analyzed by calculating the outgoing and incoming strengths of each node through the edge weights of the network. The formula for calculating node strength is as follows: ; In the formula, For nodes The output strength, Represents a node The intensity of the ingress.

[0116] By comparing the node degree and node strength of each node, the initial critical nodes in the process route can be further determined.

[0117] In this embodiment, step four includes the following: During the identification of key target nodes, if the error propagation effect of a certain node in the machining process is too large, or if events such as tool replacement, fixture change, or cutting parameter adjustment occur, the impact on the network model will be propagated and amplified, ultimately leading to a decline in process network stability and product quality. Therefore, based on Shannon's information entropy theory, a node identification method is designed according to the degree of influence of nodes on machining quality stability in a directed weighted complex network of the process route.

[0118] The weighted clustering coefficient C of a node reflects the error propagation effect of the node in the network. The value after normalizing the weighted clustering coefficient is... The influence coefficient of a node on the stability of processing quality is defined as . After normalization, the influence coefficient of each node on the stability of processing quality is greater than or equal to 0 and less than or equal to 1, and the sum of the influence coefficients of all nodes in the network is 1. Therefore, based on Shannon's information entropy theory, the influence entropy value of each node in the network can be defined and obtained, and its calculation formula is as follows: ; In the formula, This is the weighted clustering coefficient; Indicates node strength; Indicates node degree; Represents a node To the node The weight of the connected edges, Represents a node To the node The weight of the connected edges; Represents a node To the node The process route, Represents a node To the node The process route, Represents a node To the node The process route, where n represents the total number of nodes in the weighted directed complex network model.

[0119] The formula for calculating the influence coefficient is as follows: In the formula, This is the influence coefficient.

[0120] The formula for calculating entropy is as follows: In the formula, This represents a node in a weighted directed complex network model. The influence of entropy value.

[0121] As shown in the formula above, the node information related to entropy mainly includes node degree, node strength, and edge weights between nodes. When the error propagation effect of a node changes, the entropy value of that node also changes. Therefore, key nodes can be identified based on their entropy values, i.e., ranked according to the degree of influence of that node on the stability of processing quality.

[0122] The SND algorithm combines the clustering coefficient and network information entropy of a weighted complex network to comprehensively analyze the impact of network node error propagation on machining quality stability. Therefore, the SND algorithm is selected to evaluate and analyze the importance of nodes in the error propagation network of thin-walled part machining process routes. Nodes with high influence entropy values ​​selected by the SND algorithm are identified as key target nodes that may lead to a decrease in part machining quality stability.

[0123] Step five above includes the following: Error tracing is performed on the target critical nodes by backtracking along the directed edges of the processing network. To ensure backtracking accuracy, a maximum of three backtracking steps are performed. The set of nodes included in the backtracking route is defined. For sets The nodes in the graph represent the error propagation coefficients of individual nodes. Defined as the ratio of the incoming intensity to the outgoing intensity at that node, as shown below: ; In the formula, Represents a node The output strength, Represents a node The intensity of penetration; Represents a node To the node The weight of the connected edges, Represents a node To the node The weight of the connected edges, Represents a node To the node The process route, Represents a node To the node The process route, if the node and If they are connected and All equal to 1, if not connected and All are equal to 0.

[0124] gather any node Error propagation strength The calculation method is as follows: based on nodes Starting from the node, calculate the node. Strength and nodes Reaching the target key node The product of the error propagation coefficients of all nodes along the path is shown below: ; In the formula, Indicates the strength of error propagation. For nodes The output strength, It is a set of backtracking nodes The first in All nodes of the path, Representing nodes in the error tracing process To the target key node any node in the path, Represents any node in the error tracing process. The error propagation coefficient, This represents the cumulative effect of the error propagation coefficients at all nodes during the error sourcing process.

[0125] This invention proposes a method for error identification, source tracing, and process optimization in the cutting process based on weighted directed complex networks. It aims to conduct source tracing analysis on key nodes derived from the law of machining error propagation, and optimize the corresponding process based on the source tracing results, thereby providing a solid theoretical basis and technical support for the precision and efficient machining of PTFE thin-walled parts.

[0126] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and scope of the present invention are included within the scope of protection of the present invention.

[0127] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of the invention, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the invention. The sequence numbers of the above-described embodiments of the invention are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0128] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. In the several embodiments provided by this invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components may be combined, or integrated into another system, or some features may be ignored or not performed.

[0129] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for error analysis and process optimization in the machining of thin-walled PTFE parts, characterized in that, The method includes: To obtain the processing route of PTFE thin-walled parts, a weighted directed complex network model of the processing route is constructed by abstracting and numbering the processing equipment and features in the processing process. Specifically, the processing route is identified and divided to obtain a list of processes; each process in the list is deconstructed to obtain the processing equipment and the processing features; the processing equipment and the processing features are defined as nodes of the network, and the processing order and error propagation relationship between nodes are defined as directed edges, constructing an initial network structure containing a set of nodes and a set of directed edges; according to a preset numbering rule, all nodes in the initial network structure are normalized and numbered to obtain the weighted directed complex network model; the nodes in the weighted directed complex network model represent processing features or processing equipment, the directed edges between adjacent nodes represent the process order, and the weights of the directed edges represent the error propagation intensity. A topological characteristic analysis is performed on the weighted directed complex network model to obtain the topological characteristic indices of each node; the topological characteristic indices include node degree and node strength. Based on the aforementioned topological characteristic indicators, the Shannon information entropy theory is used to calculate the entropy value of each node's influence on the stability of processing quality, and the target key nodes are identified based on the influence entropy value. Error source tracing is performed on the target key nodes, the error propagation intensity of each node on the backtracking path is calculated, and the error source nodes are determined; Based on the error source node, locate the corresponding target processing equipment or target processing feature, and optimize and adjust the operating parameters of the target processing equipment or the process parameters of the target processing feature; The topological characteristic analysis of the weighted directed complex network model to obtain the topological characteristic index of each node includes: calculating the node degree of each node based on the weighted directed complex network model; the node degree is the sum of the out-degree and in-degree of the node, where the out-degree is the number of directed edges from the node to other nodes, and the in-degree is the number of directed edges from other nodes to the node; and calculating the node strength of each node based on the weighted directed complex network model; the node strength is the sum of the out-strength and in-strength of the node, where the out-strength is the sum of the weights of all directed edges from the node, and the in-strength is the sum of the weights of all directed edges pointing to the node. The process of calculating the influence entropy value of each node on the stability of processing quality based on the topological characteristic index and using Shannon's information entropy theory, and identifying target key nodes based on the influence entropy value, includes: for each initial key node in the initial key node set, calculating the weighted clustering coefficient of the initial key node based on network adjacency and edge weights; normalizing the weighted clustering coefficient to obtain an influence coefficient characterizing the degree of influence of the initial key node on the stability of processing quality; calculating the node entropy value of each initial key node based on Shannon's information entropy theory and using the influence coefficient as a probability distribution; and using the SND algorithm to evaluate the importance of nodes based on the node entropy value, and determining the initial key nodes with node entropy values ​​higher than a preset threshold as the target key nodes. The formula for calculating the weighted clustering coefficient is as follows: In the formula, This is the weighted clustering coefficient; Indicates node strength; Indicates node degree; Represents a node To the node The weight of the connected edges, Represents a node To the node The weight of the connected edges; Represents a node To the node The process route, Represents a node To the node The process route, Represents a node To the node The process route, where n represents the total number of nodes in the weighted directed complex network model; the formula for calculating the influence coefficient is as follows: In the formula, The influence coefficient is used; the formula for calculating the influence entropy value is as follows: In the formula, This represents a node in a weighted directed complex network model. The influence of entropy value.

2. The method according to claim 1, characterized in that, The processing equipment includes at least a machine tool, a fixture, and a cutting tool; the weight calculation process for the directed edge is as follows: Based on the actual and allowable spindle rotation error, positioning error, and repeatability error of the machine tool, the error transfer weight of the computer tool is calculated. Based on the actual cutting time and allowable cutting time of the tool, calculate the tool error propagation weight; Based on the actual and designed machining dimensions and surface roughness of the fixture, calculate the fixture error propagation weight; Based on the actual and designed machining dimensions, form and position errors, and surface roughness of the machining features, calculate the comprehensive error propagation weight of the machining features; For the processing of materials with wall thickness as the key evaluation criterion, the characteristic error propagation weight of wall thickness is calculated based on the deviation between the actual form and position error and the design form and position error of the PTFE thin-walled part, the deviation between the actual surface roughness and the design surface roughness, and the deviation between the actual wall thickness and the design wall thickness.

3. The method according to claim 2, characterized in that, The formula for calculating the machine tool error propagation weight is as follows: ； In the formula, Weights are passed to machine tool errors. This represents the actual spindle rotation error of the machine tool. These represent the actual positioning errors in the X, Y, and Z directions, respectively. These represent the actual repeatability errors in the XYZ directions, respectively. The ideal spindle rotation error of the machine tool, These represent the ideal positioning errors in the X, Y, and Z directions, respectively. These are the ideal repeatability errors in the X, Y, and Z directions, respectively. The formula for calculating the tool error propagation weight is as follows: ； In the formula, Weighting is passed on to tool error. The actual cutting time of the tool. This refers to the allowable cutting time for the cutting tool; The formula for calculating the fixture error propagation weight is as follows: ； In the formula, Weighting is used to transfer fixture errors. These are the actual machining dimensions and actual surface roughness of the fixture, respectively. The design machining dimensions and surface roughness of the fixture; The formula for calculating the weighting of the comprehensive error propagation of the processing features is as follows: ； In the formula, Weights are passed to the overall error of the processing features. These represent the actual machining dimensions, actual form and position errors, and actual surface roughness of the PTFE thin-walled part, respectively. These represent the design machining dimensions, design geometric tolerances, and design surface roughness of PTFE thin-walled parts, respectively. The formula for calculating the weighting of the wall thickness characteristic error is as follows: ； In the formula, This indicates the weighting of the wall thickness characteristic error propagation. These represent the actual wall thickness and the designed wall thickness of the PTFE thin-walled component, respectively.

4. The method according to claim 1, characterized in that, The step of tracing the error source of the target key node, calculating the error propagation intensity of each node on the backtracking path, and determining the error source node includes: Starting from the target key node, backtrack along the directed edge to the preset backtracking depth to form a backtracking node set; Calculate the error propagation coefficient for each backtracking node in the backtracking node set; the error propagation coefficient is defined as the ratio of the in-strength to the out-strength of the backtracking node; where the out-strength represents the node's ability to propagate errors, and the in-strength represents the node's ability to absorb errors; For each transmission path from the backtracking node to the target critical node, the error transmission strength of the backtracking node is calculated; the error transmission strength is defined as the product of the output strength of the starting node of the transmission path and the error transmission coefficient of all nodes on the transmission path. The backtracking node with the largest intensity value among the error propagation intensities is selected as the error source node.

5. The method according to claim 4, characterized in that, The formula for calculating the error propagation coefficient is as follows: ； In the formula, Indicates the error propagation coefficient. Represents a node The output strength, Represents a node The intensity of penetration; This represents the sum of all directed edges; Represents a node To the node The weight of the connected edges, Represents a node To the node The weight of the connected edges, Represents a node To the node The process route, Represents a node To the node The process route, if the node and If they are connected and All equal to 1, if not connected and All equal to 0; The formula for calculating the error propagation strength is as follows: ； In the formula, Indicates the strength of error propagation. For nodes The output strength, It is a set of backtracking nodes The first in All nodes of the path, Representing nodes in the error tracing process To the target key node any node in the path, Represents any node in the error tracing process The error propagation coefficient, This represents the cumulative effect of the error propagation coefficients at all nodes during the error sourcing process.

6. The method according to claim 1, characterized in that, The process list includes external turning, external contour turning, internal hole turning, internal contour turning, and bottom surface machining; The processing features include end face, outer circle, outer contour and inner contour.

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