A network modeling and key control node identification method for production line yield

Abstract

The present invention is applicable to the technical field of production lines, and in particular relates to a network modeling and key control node identification method for production line yield. Compared with the prior art, the present invention obtains multiple workpiece information and establishes coupling relationship factors between production line yield and reliability based on the workpiece information; establishes a wave transmission network model based on multiple workpiece information and coupling relationship factors, and the wave transmission network model is used to identify weak links that affect the system accuracy and reliability of the production line; establishes a key control node identification algorithm based on network controllability, and solves the wave transmission network model through the key control node identification algorithm to obtain an identification result. The present invention quantifies the control ability of the fault source node on the key quality attribute node, identifies the weak links that affect the production line yield, and thus guides the yield assurance and growth process.

Description

Technical Field

[0001] The present invention is applicable to the technical field of production lines, and in particular relates to a network modeling and key control node identification method for production line yield. Background Art

[0002] As the scale of the processing system expands, the performance degradation of a functional component may not be sufficient to cause a precision failure in a single device. However, after its impact is transmitted, expanded and accumulated in multiple manufacturing stages of the production line, it will cause the production line to be unable to process qualified workpieces, thereby triggering unnecessary shutdown inspection and maintenance activities of the production line, greatly reducing the reliability of the long-process production line.

[0003] The yield of discrete manufacturing production lines is crucial. Production lines are long and the yield mechanism is complex. Identifying key controllable nodes has become a necessary measure to ensure yield. Currently, there are the following problems in yield factor analysis for long-process production lines: (1) ignoring the cascading effects of multiple manufacturing stages and the coupling effects of quality and reliability, and focusing too much on traditional methods such as statistical process control; (2) the coupling relationships between machine / component degradation, process uncertainty fluctuations, and product yield attributes are complex, making it difficult to trace back to key control nodes; and (3) the weights of directed edges in the coupling network vary. Conventional qualitative methods such as complex network degree analysis are difficult to quantify and obtain importance rankings, and lack interpretability.

[0004] At the same time, the local precision fluctuations caused by the performance degradation of processing resource elements present a complex large-scale network structure after being transmitted through multiple stages of the production line. In a network model with a small number of nodes, an appropriate model can be used to establish a precise coupling relationship between nodes, conduct reliability assessment and identify weak links (such as BN modeling). At present, the yield of long-process production lines and the identification of key control nodes generally have the following problems: (1) Time dependence and performance degradation. Long-term operation causes the performance of equipment components to decline, and traditional reliability models cannot accurately describe the impact on production line accuracy; (2) Complex network structure. As the scale of the production line expands and the network structure becomes complex, traditional modeling methods are difficult to cope with, which may lead to unsatisfactory evaluation results; (3) Node relationships are difficult to determine. It is difficult to obtain deterministic relationships between nodes, which affects the establishment of an accurate reliability model; (4) The complexity of fluctuation transmission. Local precision fluctuations are transmitted in multiple stages. Traditional methods cannot effectively analyze the fluctuation transmission process and find weak links.

[0005] Therefore, a new network modeling and key control node identification method for production line yield is urgently needed to solve the above problems. Summary of the Invention

[0006] The present invention proposes a network modeling and key control node identification method for production line yield, aiming to solve the problems in the existing technology that traditional modeling methods are difficult to cope with for complex network structures, expanded production line scale, and complex network structures. The present invention establishes a processing process fluctuation transmission network model for production line yield reliability analysis, identifies the weak links that affect the system yield reliability, and further improves the yield of the production line.

[0007] The following steps are involved:

[0008] S1. Acquire multiple workpiece information and establish a coupling relationship factor between production line yield and reliability based on the workpiece information; wherein the workpiece information includes processing characteristics and fluctuation sources;

[0009] S2. Establishing a wave transmission network model based on the plurality of workpiece information and the coupling relationship factors, wherein the wave transmission network model is used to identify weak links that affect the system accuracy and reliability of the production line;

[0010] S3. Establish a key control node identification algorithm based on network controllability, and solve the wave transmission network model through the key control node identification algorithm to obtain an identification result.

[0011] Preferably, step S2 includes the following sub-steps:

[0012] Establish a processing feature network and define the workpiece set of the production line as P ={ P 1,… P i ,… P NP}, artifact P i The processing feature set is F i ={ F i1 , F i2 ,… },in n i Indicates workpiece P i The number of processing features, the processing feature network is , then the workpiece P i Processing feature network for:

[0013] ;

[0014] in, Represents workpiece Pi The edge set of the processing features;

[0015] Based on the processing characteristics, a precision reliability factor network is established, and the given processing characteristics are defined as Fij ,in , , the node set of precision reliability factor is Rij ={ rij 1, rij 2,…, },in s Indicates machining features Fij The number of nodes of the precision reliability factor, the precision reliability factor network is , then the accuracy reliability network for:

[0016] ;

[0017] in, Indicates the accuracy reliability factor and processing characteristics Fij The corresponding edge set;

[0018] A quality attribute node network is established based on the processing features, and the quality attribute node set is defined as Dij ={ dij1 , dij2 ,… },in h Indicates machining features Fij The number of nodes of the quality attribute, the quality attribute node network is , then the quality attribute node network for:

[0019] ;

[0020] in, An edge set representing the fluctuation source and the accuracy reliability factor;

[0021] Merge the processing feature network, the precision reliability factor network and the quality attribute node network to obtain the wave transmission network model, which is defined as , then the wave transfer network model satisfies the following relationship:

[0022] .

[0023] Preferably, step S3 includes the following sub-steps:

[0024] Determining a set of key quality attribute nodes according to the wave transmission network model;

[0025] Randomly sampling the set of key quality attribute nodes using a bipartite graph matching algorithm to obtain a maximum matching solution;

[0026] Calculating the frequency of each of the fluctuation sources appearing in the fully controllable subgraph corresponding to the maximum matching solution during the sampling process;

[0027] When the frequency of the fluctuation source is less than or equal to a preset value or all nodes in the maximum matching solution have been traversed, calculating the importance of the fluctuation source;

[0028] sorting the importance of the plurality of fluctuation sources to obtain a sorting result;

[0029] The weak links that affect the accuracy and reliability of the production line are determined according to the sorting results to obtain the identification results.

[0030] Preferably, the preset value is 0.001.

[0031] Preferably, the coupling relationship factors include reliability yield influencing factors, and the reliability yield influencing factors are used to reflect the impact of soft failures and hard failures of components in the production line causing problems in the production yield during the manufacturing stage.

[0032] Preferably, the coupling relationship factors include yield reliability influencing factors, and the yield reliability influencing factors are used to reflect the impact of product yield issues on the reliability of parts in the next manufacturing stage.

[0033] Preferably, the coupling relationship factors include reference constraint influencing factors, and the reference constraint influencing factors are used to reflect the influence of component error transmission caused by positioning references and process references in the manufacturing stage.

[0034] Preferably, the coupling relationship factors include error transmission influencing factors, and the error transmission influencing factors are used to reflect the influence of component error transmission caused by the sequence relationship between the processing steps.

[0035] Compared to existing technologies, the present invention obtains information about multiple workpieces and establishes coupling factors between production line yield and reliability based on this information. A wave propagation network model is established based on the information and coupling factors, and the wave propagation network model is used to identify weak links that affect the system accuracy and reliability of the production line. A key control node identification algorithm based on network controllability is established, and the wave propagation network model is solved using this key control node identification algorithm to obtain identification results. This invention has made significant progress in comprehensive information analysis and evaluation accuracy, providing a more effective approach to improving production line yield. By quantifying the controllability of fault source nodes over key quality attribute nodes, weak links that affect production line yield are identified, thereby guiding the yield assurance and growth process. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be described in detail below with reference to the accompanying drawings. The above and other aspects of the present invention will become clearer and easier to understand through the detailed description made with reference to the following drawings. In the accompanying drawings:

[0037] Figure 1 This is a flow chart of a network modeling and key control node identification method for production line yield provided by an embodiment of the present invention;

[0038] Figure 2 Schematic diagram of the production line yield-reliability coupling relationship of a multi-stage manufacturing process provided by an embodiment of the present invention;

[0039] Figure 3 Schematic diagram of the structure of a wave transmission network model provided by an embodiment of the present invention;

[0040] Figure 4 Schematic diagram of a wave transmission network model of a piston production line provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] Please refer to Figure 1-Figure 4 The present invention provides a network modeling and key control node identification method for production line yield, comprising the following steps:

[0043] S1. Acquire multiple workpiece information and establish a coupling relationship factor between production line yield and reliability based on the workpiece information; wherein the workpiece information includes processing characteristics and fluctuation sources;

[0044] In an embodiment of the present invention, the coupling relationship factors include reliability yield influencing factors, which are used to reflect the impact of soft failures and hard failures of components in the production line causing problems in production yield during the manufacturing stage.

[0045] The coupling relationship factors include yield reliability influencing factors, and the yield reliability influencing factors are used to reflect the impact of product yield issues on the reliability of parts in the next manufacturing stage.

[0046] The coupling relationship factors include reference constraint influencing factors, and the reference constraint influencing factors are used to reflect the influence of component error transmission caused by positioning references and process references in the manufacturing stage.

[0047] The coupling relationship factors include error transmission influencing factors, and the error transmission influencing factors are used to reflect the influence of component error transmission caused by the sequence relationship between the processing procedures.

[0048] Specifically, in long-process production lines, workpieces usually need to be processed in multiple manufacturing stages, and each manufacturing stage will complete multiple processing tasks. It is necessary to deeply analyze and understand the tasks, operations, and related parameters of each production stage of the workpiece, conduct in-depth analysis of the processing resource elements of each manufacturing stage, and clarify the possible factors of hard and soft failures, including equipment failures, material problems, etc. At the same time, the time domain and spatial attributes of the workpiece yield must be considered. Using historical data or simulation analysis, the reliability of the MSC (component) at each manufacturing stage is comprehensively evaluated, including the probability of hard and soft failures. Therefore, the coupling relationship between the yield and reliability of long-process production lines is defined as follows:

[0049] The impact of reliability factors on production line yield factors (Reliability-Yield Effects, R-YEffects): The soft and hard failures of MSC lead to problems with the product yield of this manufacturing stage. For example, the accuracy decline of machine tools, tool wear and chipping, loose fixtures, etc. lead to deviations in product dimensions or changes in measurement datums. The impact of production line yield factors on reliability factors (Yield-Reliability Effects, YR Effects): The impact of product yield problems on the reliability of MSC in the next manufacturing stage. Excessive deviations in key product dimensions will lead to poor cutting conditions, accelerate tool wear, increase the probability of chipping, and accelerate the decline of machining accuracy. (3) The process of transmission of production line yield fluctuations due to datum constraints (Datum Effects, DE): refers to the process of transmitting part errors when the processing of features in a certain manufacturing stage is based on the features processed in the previous stage as the datum (positioning datum and process datum).

[0050] (4) Evolution Effects (ER) refers to the process of part error transmission caused by the existence of a sequence of evolution between geometric processing features.

[0051] Based on the above four influencing factors, the coupling relationship between production line yield and reliability and the process relationship of transmission along multiple manufacturing stages are established as follows: Figure 2 As shown, Figure 2 Schematic diagram of the production line yield-reliability coupling relationship of the multi-stage manufacturing process provided by an embodiment of the present invention.

[0052] It is necessary to explain the following terms:

[0053] Processing resource elements: These refer to the key factors and elements that influence product processing during the production process. These factors directly impact processing efficiency, yield, and overall production line operation. Key processing resource elements include equipment, process parameters, raw materials, human resources, maintenance, production planning and scheduling, and data monitoring and analysis. They are key factors influencing production process efficiency and product quality.

[0054] Soft failure: refers to an abnormal condition or state in which a component does not completely lose its function, but has a certain degree of functional degradation.

[0055] Hard failure: An abnormal condition or state in which a component completely loses its function.

[0056] S2. Establishing a wave transmission network model based on the plurality of workpiece information and the coupling relationship factors, wherein the wave transmission network model is used to identify weak links that affect the system accuracy and reliability of the production line;

[0057] In an embodiment of the present invention, in a long manufacturing line, the coupling relationship becomes increasingly complex as the number of manufacturing stages and types of workpieces processed increase. The sources of fluctuations are diverse and propagate in a mesh along multiple transmission paths. It is necessary to identify the weak links of the system through reliability analysis to improve the yield or maintenance measures of the long manufacturing line. The elements of the complex system are abstracted into nodes, and the connection relationship between the nodes is abstracted into directed edges. The MSCs of the various processes in the process flow are linked by the processing characteristics of the workpiece. The nodes are connected through the processing benchmark and the geometric processing sequence relationship to construct the corresponding fluctuation transmission network model. Please refer to Figure 3 , Figure 3 The wave transmission network model structure diagram provided by the embodiment of the present invention is divided into four layers: processing steps, yield attributes, processing characteristics, and component performance degradation factors.

[0058] In this embodiment of the present invention, based on the workpiece's processing information and the workpiece's processing characteristics, the workpiece processing characteristics and the factors affecting the equipment's accuracy and reliability are abstracted into nodes, and the relationships between them are abstracted into edges. This allows the construction of a wave propagation network for the production line's processing. The specific steps for constructing a wave propagation network model for a long-process production line are as follows:

[0059] Establish a processing feature network and define the workpiece set of the production line as P ={ P 1,… P i ,… P NP}, artifact P i The processing feature set is F i ={ F i1 , F i2 ,… },in n i Represents workpiece P i The number of machining features of the workpiece is defined as the network node, and the relationship between features (base, evolution relationship, etc.) is defined as the edge of the network. The machining feature network is , then the workpiece P i Processing feature network for:

[0060] ;

[0061] in, Represents workpiece Pi The edge set of the processing features;

[0062] Based on the processing characteristics, a precision reliability factor network is established, and the given processing characteristics are defined as Fij ,in , , the node set of precision reliability factor is Rij ={ rij 1, rij 2,…, },in s Indicates machining features Fij The number of nodes of the precision reliability factor is defined as the network node, and the relationship between the precision reliability factor and the processing feature is defined as the edge of the network. The precision reliability factor network is , then the accuracy reliability network for:

[0063] ;

[0064] in, Indicates the accuracy reliability factor and processing characteristics Fij The corresponding edge set;

[0065] A quality attribute node network is established based on the processing features, and the quality attribute node set is defined as Dij ={ dij1 , dij2 ,… },in h Indicates machining features Fij The number of nodes of the quality attribute is defined as the network node, and the corresponding relationship between the quality attribute and the processing feature is defined as the edge of the network. The quality attribute node network is , then the quality attribute node network for:

[0066] ;

[0067] in, An edge set representing the fluctuation source and the accuracy reliability factor;

[0068] Merge the processing feature network, the precision reliability factor network and the quality attribute node network to obtain the wave transmission network model, which is defined as , then the wave transfer network model satisfies the following relationship:

[0069] .

[0070] S3. Establish a key control node identification algorithm based on network controllability, and solve the wave transmission network model through the key control node identification algorithm to obtain an identification result.

[0071] In the embodiment of the present invention, the fluctuation source xk The control capability quantification algorithm for key quality attribute nodes (target nodes) can be described as follows:

[0072] According to the wave transmission network model, a set of key quality attribute nodes is determined; specifically, given a wave transmission network model G’ , determine the key quality attribute node set Q { q 1, q 2,…, q t},initialization =0;

[0073] The maximum matching solution is obtained by randomly sampling the set of key quality attribute nodes through the bipartite graph matching algorithm; specifically, the fully controllable subgraph of each matching solution is determined, the root of the cactus structure of the key quality attribute node (target node) is found, and the root set is determined. D ,like x k ,but Accumulate 1, otherwise do not accumulate;

[0074] The frequency of each of the fluctuation sources appearing in the fully controllable subgraph corresponding to the maximum matching solution during the sampling process is calculated; the preset value is 0.001.

[0075] When the frequency of the fluctuation source is less than or equal to a preset value or all nodes in the maximum matching scheme have been traversed, the importance of the fluctuation source is calculated; specifically, the fluctuation source node x k The importance of can be calculated as follows:

[0076] ;

[0077] Among them, [1- ] is the source of fluctuation x k The probability of an accuracy failure, The source of fluctuation x k The frequency of occurrence in the fully controllable subgraph of the key quality attribute node.

[0078] sorting the importance of the plurality of fluctuation sources to obtain a sorting result;

[0079] The weak links that affect the accuracy and reliability of the production line are determined according to the sorting results to obtain the identification results.

[0080] For example, please refer to Figure 4 , Figure 4 This is a schematic diagram of a wave transmission network model of a piston production line provided by an embodiment of the present invention, wherein the network has 6 processing feature nodes, 2 key quality attribute nodes (compression height and maximum outer diameter of special-shaped outer circle), and 32 precision reliability factor nodes.

[0081] The accuracy reliability factor node representing the performance degradation of the machine tool component ( Figure 4The in-degree of the fluctuation source node is 0, which means it must be the driving point of the network. As time goes by, these nodes will experience different degrees of performance degradation during their life cycle. This performance degradation can be understood as the network being controlled by the "external input signal". The accuracy level of the production line can be fully controlled by driving the state of the network. The combination of different fluctuation source nodes corresponds to different production line accuracy states. Each state has multiple network drive control methods. Based on the maximum matching algorithm, the control method can be searched to identify the weak links of the system. Figure 4 As shown in the figure, compression height and maximum outer diameter are two key quality attributes of the piston. Different drive control methods can be obtained through the maximum matching algorithm to control these two target nodes.

[0082] according to Figure 4 The algorithm determines the ability of the device's precision reliability factor nodes to control compression height and maximum outer diameter. A maximum matching scheme within the wave propagation network model is used to identify control methods for these nodes. Among the various control methods, the driving points reachable from the compression height and maximum outer diameter nodes are identified. By traversing all maximum matching schemes, the ability of the fluctuation source to control these two key quality attributes is quantified. The results are shown in Table 1.

[0083] Table 1: Identification of weak links for accuracy and reliability

[0084]

[0085] As can be seen from Table 1, the node with the greatest control ability over the two key quality attributes is the fluctuation source of the precision boring pin hole boring machine. These fluctuation source nodes have the same The value is 871, which shows that among all control methods, the frequency of these nodes serving as the roots of the fully controllable subgraph containing the target node is relatively high.

[0086] Due to the wear of tools and fixtures on the production line, their failure probability is relatively high, so their importance ranking is generally higher than that of the sources of fluctuation of machine tool functional components. The sources of fluctuation with higher ranking should be given priority attention in precision reliability growth and maintenance measures.

[0087] Among all the functional components of the machine tool, the three-tool servo mechanism of the special-shaped external cylindrical lathe has the greatest impact on the accuracy and reliability of the system. After on-site investigation, it was found that the three-tool servo mechanism used for special-shaped elliptical external cylindrical cutting is a purchased part and is not very compatible with the main body of the machine tool. Therefore, it is necessary to focus on implementing reliability improvement measures.

[0088] The combustion chamber lathe has no control over two key quality attributes: the combustion chamber machining does not affect the final compression height dimension and the maximum outer diameter.

[0089] Compared to existing technologies, the present invention obtains information about multiple workpieces and establishes coupling factors between production line yield and reliability based on this information. A wave propagation network model is established based on the information and coupling factors, and the wave propagation network model is used to identify weak links that affect the system accuracy and reliability of the production line. A key control node identification algorithm based on network controllability is established, and the wave propagation network model is solved using this key control node identification algorithm to obtain identification results. This invention has made significant progress in comprehensive information analysis and evaluation accuracy, providing a more effective approach to improving production line yield. By quantifying the controllability of fault source nodes over key quality attribute nodes, weak links that affect production line yield are identified, thereby guiding the yield assurance and growth process.

[0090] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0091] The embodiments of the present invention are described above in conjunction with the accompanying drawings. What is disclosed is only a preferred embodiment of the present invention. However, the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms and equivalent changes without departing from the scope of protection of the purpose of the present invention and the claims, which are all within the protection of the present invention.

Claims

1. A network modeling and key control node identification method for production line yield, characterized by: The following steps are involved: S1. Acquire multiple workpiece information and establish a coupling relationship factor between production line yield and reliability based on the workpiece information; wherein the workpiece information includes processing characteristics and fluctuation sources; S2. Establishing a wave transmission network model based on the plurality of workpiece information and the coupling relationship factors, wherein the wave transmission network model is used to identify weak links that affect the system accuracy and reliability of the production line; S3. Establishing a key control node identification algorithm based on network controllability, solving the wave transmission network model by using the key control node identification algorithm to obtain an identification result; Step S2 includes the following sub-steps: Establish a processing feature network and define the workpiece set of the production line as P ={ P 1,… P i ,… P NP }, artifact P i The processing feature set is F i ={ F i1 , F i2 ,… },in n i Represents workpiece P i The number of processing features, the processing feature network is , then the workpiece P i Processing feature network for: ； in, Represents workpiece Pi The edge set of the processing features; Based on the processing characteristics, a precision reliability factor network is established, and the given processing characteristics are defined as Fij ,in , , the node set of precision reliability factor is Rij ={ rij 1, rij 2,…, },in s Indicates machining features Fij The number of nodes of the precision reliability factor, the precision reliability factor network is , then the accuracy reliability factor network for: ； in, Indicates the accuracy reliability factor and processing characteristics Fij The corresponding edge set; A quality attribute node network is established based on the processing features, and the quality attribute node set is defined as Dij ={ dij1 , dij2 ,… },in h Indicates machining features Fij The number of nodes of the quality attribute, the quality attribute node network is , then the quality attribute node network for: ； in, An edge set representing the fluctuation source and the accuracy reliability factor; Merge the processing feature network, the precision reliability factor network and the quality attribute node network to obtain the wave transmission network model, which is defined as , then the wave transfer network model satisfies the following relationship: ； Step S3 includes the following sub-steps: Determining a set of key quality attribute nodes according to the wave transmission network model; Randomly sampling the set of key quality attribute nodes using a bipartite graph matching algorithm to obtain a maximum matching solution; Calculating the frequency of each of the fluctuation sources appearing in the fully controllable subgraph corresponding to the maximum matching solution during the sampling process; When the frequency of the fluctuation source is less than or equal to a preset value or all nodes in the maximum matching solution have been traversed, calculating the importance of the fluctuation source; sorting the importance of the plurality of fluctuation sources to obtain a sorting result; The weak links that affect the accuracy and reliability of the production line are determined according to the sorting results to obtain the identification results.

2. The network modeling and key control node identification method for production line yield according to claim 1, characterized in that: The preset value is 0.

001.

3. The network modeling and key control node identification method for production line yield according to claim 1, characterized in that: The coupling relationship factors include reliability yield influencing factors, which are used to reflect the impact of soft failures and hard failures of components in the production line causing problems in the production yield during the manufacturing stage.

4. The network modeling and key control node identification method for production line yield according to claim 1, characterized in that: The coupling relationship factors include yield reliability influencing factors, and the yield reliability influencing factors are used to reflect the impact of product yield issues on the reliability of parts in the next manufacturing stage.

5. The network modeling and key control node identification method for production line yield according to claim 1, characterized in that: The coupling relationship factors include reference constraint influencing factors, and the reference constraint influencing factors are used to reflect the influence of component error transmission caused by positioning references and process references in the manufacturing stage.

6. The network modeling and key control node identification method for production line yield according to claim 1, characterized in that: The coupling relationship factors include error transmission influencing factors, and the error transmission influencing factors are used to reflect the influence of component error transmission caused by the sequence relationship between processing procedures.

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