Workshop part declaration management system

Abstract

The invention discloses a workshop part declaration management system, and relates to the technical field of production workshop part inventory management. The workshop spare and accessory declaration management system comprises an acquisition and division unit used for analyzing data of a comprehensive production evaluation index of each part in each part type after a preset workshop production cycle is finished; the defect judgment unit is used for judging and screening production defect parts based on the comprehensive production evaluation index and a preset production evaluation interval; the maintenance analysis unit is used for analyzing the maintenance evaluation index; and the maintenance judgment unit is used for dividing maintainable defective parts and scrapped defective parts based on the maintenance evaluation indexes and a preset maintenance evaluation interval and generating a part declaration report. Each part is comprehensively analyzed through the comprehensive production evaluation indexes, so that the production quality of the parts can be accurately evaluated, and the production efficiency of the parts is improved. A comprehensive evaluation result is provided for each part type, and potential problems of the parts can be identified more comprehensively and meticulously.

Description

Technical Field

[0001] The present invention relates to the technical field of production workshop spare parts inventory management, in particular to a workshop spare parts declaration management system. Background Art

[0002] With the continuous development of manufacturing and industrial production, the quality control and management of parts, as fundamental components of the production process, has become increasingly important. Particularly during workshop production, the quality, integrity, and repairability of parts directly impact production efficiency, product quality, and overall economic benefits. Therefore, effectively managing and evaluating part production quality, repair requirements, and scrapping decisions has become a critical issue for many manufacturing companies.

[0003] In the existing spare parts management system of production workshops, many companies rely on traditional management methods, focusing mainly on inventory management, warehousing declaration and simple quality inspection of parts, and lack scientific assessment of defects and repair potential in the parts production process.

[0004] Existing technology, such as the invention patent application with announcement number CN117114588A, discloses a production workshop spare parts declaration management system and method. The management system includes: a data storage module, a data processing module, a background management layer, and a user application layer; the user application layer is respectively connected to the data processing module in communication, the data processing module is respectively connected to the data storage module in communication, and the background management layer is respectively connected to the data storage module, the data processing module, and the user application layer in communication. Through this system, spare parts information can be effectively managed and controlled in real time, and spare parts inventory information, in-transit status, declaration status, demand, and historical receipt records can be displayed clearly and efficiently. Through one-click operation, the daily work of spare parts management is simplified, which improves work efficiency and the scientificity and rationality of spare parts declaration.

[0005] Based on the above solution, it was found that in the existing technology, the management of parts is usually concentrated on inventory management and declaration processes. There is a lack of in-depth analysis and judgment of defective parts that appear in the production process. Especially in the decision-making of repairing and scrapping parts, traditional methods often rely on simple inventory status and lack a comprehensive assessment of part quality and repairability. In other words, the existing system fails to conduct scientific repair needs analysis based on the actual damage of parts, which can easily lead to some repairable parts being mistakenly scrapped, or parts that do not meet the requirements being used. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the present invention provides a workshop spare parts declaration management system, which solves the problem of lack of effective parts repair and scrap judgment in the existing technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a workshop spare parts declaration management system, comprising: a data acquisition and classification unit, and a defect judgment unit; the data acquisition and classification unit is used to obtain a number of produced parts after a preset workshop production cycle, classify them into types, obtain several part types, analyze the basic integrity index and long-term toughness index of each part in each part type, and perform a comprehensive analysis to obtain a comprehensive production evaluation index for each part in each part type, the calculation formula of which is as follows: ;in, For the Part Type The comprehensive production evaluation index of each component, For the Part Type The basic integrity index of each component, is the integrity impact coefficient stored in the database, is the integrity adjustment coefficient stored in the database, For the Part Type The long-term resilience index of each component, is the toughness influence coefficient stored in the database, is the toughness adjustment coefficient stored in the database, is the interaction coefficient stored in the database, is the interaction adjustment coefficient stored in the database, , is the number of part types, , is the number of parts; the defect judgment unit is used to judge and analyze the comprehensive production evaluation index of each part in each part type with the preset production evaluation interval, and screen out production defective parts according to the judgment and analysis results.

[0008] Furthermore, the specific steps for analyzing the basic integrity index of each component in each part type are as follows: obtain the basic production property data of each component in each part type and the surface roughness value and surface hardness value at each measuring position, and preprocess them, wherein the basic production property data include actual size value, design size value, actual weight value, and design weight value; comprehensively analyze the surface roughness value and surface hardness value at each measuring position of each component in each part type after preprocessing to obtain the surface roughness index and surface hardness index of each component in each part type; comprehensively analyze the basic production property data of each component in each part type after preprocessing in combination with the corresponding surface roughness index and surface hardness index to obtain the basic integrity index of each component in each part type.

[0009] Furthermore, the specific formula for calculating the basic integrity index of each component in each part type is as follows: ;in, For the Part Type The basic integrity index of each component, After preprocessing Part Type The actual size value of each component, After preprocessing Design dimension values ​​for various part types, is the size influence coefficient stored in the database, After preprocessing Part Type The actual weight of each component, After preprocessing The design weight value of each part type, is the weight influence coefficient stored in the database, For the Part Type The surface roughness index of each component, is the surface roughness influence coefficient stored in the database, For the Part Type The surface hardness index of each component, is the surface hardness influence coefficient stored in the database, , , is the number of part types, , is the number of parts.

[0010] Furthermore, the specific steps for analyzing the long-term toughness index of each component in each part type are as follows: obtaining and preprocessing the crack quantity value and the residual stress value and grain size value at each measuring position of each component in each part type, and performing preprocessing respectively; comprehensively analyzing the residual stress value and grain size value at each measuring position of each component in each part type after preprocessing, and obtaining the residual stress index and grain size index of each component in each part type; comprehensively analyzing the crack quantity value, residual stress index, and grain size index of each component in each part type after preprocessing, and obtaining the long-term toughness index of each component in each part type.

[0011] Furthermore, it also includes: a maintenance analysis unit and a maintenance judgment unit; the maintenance analysis unit is used to analyze the maintenance evaluation index of each defective component in each defective part type; the maintenance judgment unit is used to judge and analyze the maintenance evaluation index of each defective component in each defective part type and the preset maintenance evaluation interval, and divide it into repairable defective components and scrapped defective components according to the judgment and analysis results, and generate a component declaration report and send it to relevant staff.

[0012] Furthermore, for each defective component in each defective part type, the specific steps of analyzing the maintenance assessment index are as follows: for each defective component in each defective part type, obtain the crack depth value of each crack, and perform a comprehensive analysis to obtain the crack depth index of each defective component in each defective part type; for each defective component in each defective part type, analyze the processing error index; obtain the material fatigue index, surface damage index, repair time prediction value, and repair cost prediction value of each defective component in each defective part type, and perform a comprehensive analysis in combination with the crack depth index and processing error index of the corresponding defective component to obtain the maintenance assessment index of each defective component in each defective part type.

[0013] Furthermore, the specific formula for calculating the repair assessment index of each defective component in each defective part type is as follows: ;in, For the The first type of defective part Repair assessment index of defective parts, For the The first type of defective part Material fatigue index of defective parts, is the material fatigue recovery coefficient stored in the database, For the The first type of defective part The surface damage index of defective parts, is the surface damage repair coefficient stored in the database, For the The first type of defective part The crack depth index of each defective component, is the crack depth repair coefficient stored in the database, For the The first type of defective part The processing error index of defective parts, is the machining error repair coefficient stored in the database, , For the The first type of defective part The predicted repair time for each defective component, is the repair time coefficient stored in the database, For the The first type of defective part The estimated cost of repairing a defective component, is the repair cost coefficient stored in the database, , , is the number of defective part types, , is the number of defective parts.

[0014] Furthermore, for each defective component in each defective part type, the specific steps of analyzing the processing error index are as follows: reading the actual size value and design size value, the actual weight value and design weight value of each defective component in each defective part type and performing absolute difference analysis respectively to obtain the size error and weight error of each defective component in each defective part type; preprocessing the size error and weight error of each defective component in each defective part type, and performing comprehensive analysis after preprocessing to obtain the processing error index of each defective component in each defective part type.

[0015] Furthermore, the specific steps for obtaining the surface damage index of each defective component in each defective component type are as follows: reading the surface roughness index of each defective component in each defective component type, and obtaining the scratch depth index and wear layer thickness index of each defective component in each defective component type, and performing preprocessing; performing a comprehensive analysis on the surface roughness index, scratch depth index, and wear layer thickness index of each defective component in each defective component type after preprocessing to obtain the surface damage index of each defective component in each defective component type.

[0016] Furthermore, the specific steps for obtaining the repair time prediction value and repair cost prediction value of each defective component in each defective part type are: obtaining the historical repair time value and historical repair cost value of the historical defective repair parts corresponding to each defective component in each defective part type, and performing comprehensive analysis respectively to obtain them.

[0017] The present invention has the following beneficial effects:

[0018] (1) The workshop's spare parts declaration management system conducts a comprehensive analysis of each component through a comprehensive production evaluation index. This index combines the basic integrity index and the long-term toughness index. By analyzing the actual size, weight, surface quality, residual stress and other parameters of the component, the system can accurately evaluate the production quality of the component and provide a comprehensive evaluation result for each part type. Compared with traditional quality evaluation methods, this method can more comprehensively and meticulously identify potential problems of components and discover quality risks in advance. Specifically, the system will compare the component's evaluation index with these intervals based on the preset production evaluation intervals, thereby accurately screening out production defective components. This enables the workshop to discover unqualified components in a timely manner, preventing these components from entering the next stage of use or entering the production line, reducing hidden quality problems and potential production risks. Traditional methods can often only rely on simple size detection and weight judgment, but lack comprehensive analysis of other key parameters such as surface roughness and residual stress, which can easily lead to deviations in quality judgment.

[0019] (2) The workshop spare parts declaration management system, the maintenance assessment index, through the multi-dimensional analysis of defective parts, such as crack depth, surface damage, fatigue index, etc., accurately assesses the maintenance needs of each part. This process does not rely solely on traditional manual experience, but is based on historical data, maintenance history, material fatigue, repair cost and other data for intelligent analysis to assess the feasibility of repairing each defective part. Through this system, the workshop can avoid wrong repair or scrapping decisions. For example, if the maintenance assessment index of a part is high, it means that its repair cost is too high or it is difficult to restore to the original design requirements after repair. The system will automatically recommend scrapping instead of repairing. On the contrary, for parts with lower repair difficulty, the system will recommend repair. This intelligent maintenance decision not only reduces resource waste, such as unnecessary repairs or premature scrapping, but also improves the overall work efficiency of the workshop. In addition, the estimation of repair time and cost in the maintenance assessment process predicts the repair time and cost by analyzing historical data and uses it as a reference in the repair decision. This can effectively reduce the uncertainty in the maintenance process and ensure the smooth progress of the maintenance process.

[0020] (3) The workshop spare parts declaration management system integrates all analysis and evaluation modules, such as basic integrity index, long-term resilience index, maintenance assessment index, etc., into the system, and uses automated data analysis and judgment to complete the entire process of parts declaration, defect screening, and maintenance decision-making. The system automatically monitors, evaluates, and makes repair decisions on workshop parts in real time through modules such as data acquisition division unit, defect judgment unit, maintenance analysis unit, and maintenance judgment unit. This automated process significantly improves the efficiency of workshop management, avoids human bias in manual screening and decision-making, and ensures that each part undergoes standardized and precise analysis. Automated decision-making not only reduces the cost of manual intervention, but also enables timely adjustment of management strategies to cope with dynamic changes in workshop production. Data-driven analysis makes workshop management more scientific and accurate, and improves the overall level of intelligence.

[0021] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a block diagram of a workshop spare parts declaration management system of the present invention.

[0023] Figure 2 The present invention provides a flowchart of the specific steps for analyzing the long-term toughness index of each component in each part type in a workshop spare parts declaration management system.

[0024] Figure 3 This is a flowchart of the specific steps for analyzing the maintenance evaluation index for each defective component in each defective part type in a workshop spare parts reporting management system of the present invention. DETAILED DESCRIPTION

[0025] See also Figure 1 An embodiment of the present invention provides a technical solution: a workshop spare parts declaration management system, comprising: a data acquisition and division unit and a defect judgment unit; the data acquisition and division unit is used to obtain a number of produced parts (such as printer steel brackets, dot matrix printer semi-finished products, hardware accessories, etc.) after a preset workshop production cycle (such as two hours) is completed, and classify them into types to obtain several part types, and analyze the basic integrity index and long-term toughness index of each part in each part type, and perform a comprehensive analysis to obtain a comprehensive production evaluation index of each part in each part type; the defect judgment unit is used to judge and analyze the comprehensive production evaluation index of each part in each part type with a preset production evaluation interval, and screen out production defective parts according to the judgment and analysis results (that is, parts within the production evaluation interval are considered qualified, and parts below the lower limit of the production evaluation interval are considered defective parts).

[0026] In addition, when spare parts are produced in the workshop, the remaining raw materials in the inventory are detected in real time, and when the remaining raw materials are lower than the set threshold, the raw material request information is sent to the relevant staff.

[0027] The specific formula for calculating the comprehensive production assessment index of each component in each part type is as follows: ;in, For the Part Type The comprehensive production evaluation index of each component, For the Part Type The basic integrity index of each component, is the integrity impact coefficient stored in the database, is the integrity adjustment coefficient stored in the database, For the Part Type The long-term resilience index of each component, is the toughness influence coefficient stored in the database, is the toughness adjustment coefficient stored in the database, is the interaction coefficient stored in the database, is the interaction adjustment coefficient stored in the database, , is the number of part types, , is the number of parts.

[0028] What needs to be explained is that 、 、 The specific steps to obtain are: The influence of the integrity control system stored in the database on the comprehensive production evaluation of a certain component is generally obtained by: collecting the production process data of the component from the relevant production system, especially the data related to the component integrity, such as production process and material characteristics, and using the historical data to perform regression analysis or variance analysis to obtain the degree of influence of the component integrity on the comprehensive production evaluation. The regression analysis method can be used to measure the weight of the influence; The quality impact coefficients stored in the database specifically target the impact of changes in the physical properties or quality of parts. The acquisition steps include: using standardized quality control tools (such as dimensional measuring instruments and material strength testing) to evaluate part quality; evaluating the impact of quality changes on comprehensive production evaluation based on experimental and historical production data; and calculating the specific impact of quality factors on part production evaluation using statistical methods (such as regression models). It represents the interaction coefficient stored in the database, taking into account the interaction between the various physical properties of parts (such as size, weight, hardness, etc.). The acquisition steps include: using experimental and production data to analyze the interaction effects between different physical properties, using methods such as multiple regression analysis to consider the joint impact of multiple physical properties on part evaluation, and fusing data from different sources and different characteristics to ensure a comprehensive analysis of the contribution of interactions to the comprehensive evaluation index.

[0029] 、 、 The specific steps to obtain are: The integrity adjustment coefficient stored in the database is used to adjust the impact of component integrity data on comprehensive production assessment. The acquisition steps generally include: collecting historical data related to component integrity during the production process, including failure rates, repair records, etc., and determining the value of the integrity adjustment coefficient through statistical analysis or empirical methods to reflect changes in component integrity during production; The quality adjustment coefficient stored in the database is used to adjust the impact of component quality data on the comprehensive production evaluation. The acquisition steps include: monitoring the quality changes of components in real time, determining the appropriate value of the quality adjustment coefficient through data analysis, and adjusting the impact weight of component quality on the evaluation based on historical quality data and current production status; It represents the interaction adjustment coefficient stored in the database, which adjusts the contribution of the interaction of the physical properties of the parts to the evaluation index. The acquisition steps include: collecting the change data of the physical properties of the parts, analyzing the interaction effects between them, using statistical modeling (such as collaborative regression analysis) to determine the interaction adjustment coefficient, and optimizing and adjusting it to reflect the correlation of physical properties in actual production.

[0030] Specifically, the specific steps for analyzing the basic integrity index of each component in each part type are as follows: obtain the basic production property data of each component in each part type and the surface roughness value and surface hardness value at each measuring position, and perform preprocessing (such as standardization processing, the purpose of which is to eliminate the dimensional effect). The basic production property data include actual size value, design size value, actual weight value, and design weight value. Among them, the size of the component can be measured in an appropriate way (such as diameter, length, etc.) according to the actual situation; the surface roughness value and surface hardness value at each measuring position of each component in each part type after preprocessing are comprehensively analyzed (i.e., mean analysis) to obtain the surface roughness index and surface hardness index of each component in each part type; the basic production property data of each component in each part type after preprocessing are combined with the corresponding surface roughness index and surface hardness index for comprehensive analysis to obtain the basic integrity index of each component in each part type.

[0031] Among them, the surface roughness value can be measured by a surface roughness meter. The higher the roughness, the more serious the surface damage and the more difficult it is to repair.

[0032] The surface hardness value can be measured and obtained through Rockwell hardness test (Rockwell), Vickers hardness test (Vickers), and Brinell hardness test (Brinell).

[0033] Rockwell hardness test (Rockwell): Use a durometer to measure the hardness of a material by applying a standardized load and using different indenter types (such as a diamond ball or steel ball).

[0034] Vickers hardness test: A diamond pyramid-shaped indenter is used to apply a load and measure the diagonal length of the indentation to obtain a hardness value.

[0035] Brinell hardness test: A hard steel ball indenter is pressed into the material surface under a certain pressure, and the diameter of the indentation is used to evaluate the hardness.

[0036] The actual size value can be measured by tools such as calipers, laser rangefinders, and coordinate measuring machines. Calipers and laser rangefinders are used for small-scale size measurement, while coordinate measuring machines can perform accurate three-dimensional size measurement and are suitable for parts with complex shapes.

[0037] Design dimension values ​​can be obtained from design drawings or CAD (computer-aided design) models of components, usually provided by engineers during the design phase.

[0038] The actual weight value can be measured by using an electronic balance or precision weighing instrument to measure the actual weight of the parts. For larger or heavier parts, weighing equipment such as a crane scale may be required.

[0039] The design weight value can be calculated from the CAD design file based on parameters such as the component geometry, material density and volume, and is usually provided by engineers during the design phase.

[0040] The specific formula for calculating the basic integrity index of each component in each part type is as follows: ;in, For the Part Type The basic integrity index of each component, After preprocessing Part Type The actual size value of each component, After preprocessing Design dimension values ​​for various part types, is the size influence coefficient stored in the database, After preprocessing Part Type The actual weight of each component, After preprocessing The design weight value of each part type, is the weight influence coefficient stored in the database, For the Part Type The surface roughness index of each component, is the surface roughness influence coefficient stored in the database, For the Part Type The surface hardness index of each component, is the surface hardness influence coefficient stored in the database, , , is the number of part types, , is the number of parts.

[0041] What needs to be explained is that 、 、 、 The specific steps to obtain are: Based on the design size of the part, it is determined by evaluating the actual design size of the part and its adaptability to other components. The design size of the part may be affected by tolerances and errors during the manufacturing process, and these errors may have long-term effects on the overall structure and function of the part. Therefore, It mainly starts from the design stage and calculates based on the standard size and functional requirements of the parts; The main consideration is the dimensional changes of parts during storage, especially the physical changes of parts in the storage environment. Storage conditions (such as pressure, humidity, temperature, etc.) may cause slight deformation of parts and affect their size. Therefore, It is derived by analyzing the deformation or dimensional deviation that may occur in the parts storage environment; It is related to the surface roughness of the parts. The roughness of the parts surface directly affects its friction, wear and corrosion resistance in long-term use. A rough surface may lead to higher friction and wear, thereby affecting the durability of the parts. By measuring the surface roughness of parts and combining it with the storage and use conditions of the parts, it reflects the potential impact of surface roughness on the long-term performance of parts; It is closely related to the surface hardness of the parts. Hardness is the ability of the parts to resist external forces (such as friction, impact and wear). The higher the surface hardness, the longer the durability and service life of the parts. By measuring the surface hardness of parts and analyzing the possible changes in hardness during storage and use, the impact of this factor on the integrity of parts is reflected.

[0042] In this implementation plan, by accurately calculating the basic integrity index of parts, a comprehensive evaluation of the quality of parts is provided, ensuring the reliability of the production process and the applicability of parts. By introducing basic production property data (such as size, weight) and surface quality indicators (such as roughness, hardness), the system can evaluate the overall quality of parts in multiple dimensions, avoiding the one-sided judgment that may be caused by a single indicator. For example, surface roughness and surface hardness directly affect the wear resistance and long-term performance of parts, and these factors are often overlooked. The system eliminates the dimensional effect through standardization, making the comparison of different parameters more scientific and ensuring the consistency of the evaluation results. When obtaining and processing basic data such as actual size, design size, actual weight, design weight, etc., precise tools such as calipers, laser rangefinders, and three-coordinate measuring machines are used. Measurements are taken using measuring machines, etc., ensuring the accuracy and reliability of the data. By combining surface quality indicators, the system further conducts in-depth analysis of parts and components to derive a basic integrity index, which not only reflects the current production quality of parts and components, but also provides a scientific basis for subsequent repair decisions. This method can effectively screen out defective parts in production, and through comprehensive analysis of factors such as dimensional errors, weight differences, and surface damage, potential quality problems can be identified in advance. Ultimately, through more accurate quality assessments, workshops can more scientifically determine which parts can be repaired and which should be scrapped, thereby improving production efficiency, reducing resource waste, and ensuring the safety and durability of parts and components during use. This comprehensive and accurate assessment method has greatly improved the level of intelligent workshop management and reduced the risk of human error.

[0043] Specifically, if Figure 2As shown, the specific steps for analyzing the long-term toughness index of each component in each part type are as follows: obtain and preprocess the crack number value and the residual stress value and grain size value at each measurement position of each component in each part type, and perform preprocessing (such as standardization, the purpose of which is to eliminate the dimension effect), wherein the grain size often refers to the average diameter of the grain. The grain itself may be irregular in shape. In actual measurement, it is usually approximated as a circle and expressed by diameter; the residual stress value and grain size value at each measurement position of each component in each part type after preprocessing are comprehensively analyzed (i.e., mean analysis) to obtain the residual stress index and grain size index of each component in each part type; the crack number value, residual stress index, and grain size index of each component in each part type after preprocessing are comprehensively analyzed to obtain the long-term toughness index of each component in each part type.

[0044] Among them, the residual stress value and grain size value can be measured and obtained by X-ray diffraction method, neutron diffraction method, hole drilling method, and photoelasticity method.

[0045] X-ray diffraction (XRD): It measures the distribution of stress by utilizing the interaction between X-rays and materials. It is particularly suitable for residual stress analysis in surface layers.

[0046] Neutron diffraction: Similar to X-ray diffraction, but suitable for analyzing thicker or deeper materials and can effectively measure stress at greater depths.

[0047] Hole drilling method: Residual stress is calculated by drilling a hole in the material surface and measuring the stress release inside the hole.

[0048] Photoelasticity: By using optical instruments, the changes in the optical properties of the material under stress are analyzed to obtain the stress distribution.

[0049] The grain size value can be obtained by microscopy or X-ray diffraction (XRD) measurement.

[0050] Microscopy: Using an optical microscope or scanning electron microscope (SEM), the grain size is measured by observing the microstructure of the material. The grain size under the microscope can be quantified using image analysis software.

[0051] X-ray diffraction (XRD): Analyzes the average grain size through X-ray diffraction patterns. This method is suitable for analyzing the crystal structure of materials and can effectively infer the grain size.

[0052] The specific formula for calculating the long-term toughness index of each component in each part type is as follows: ;in, For the Part Type The long-term resilience index of each component, After preprocessing Part Type The number of cracks in each component, is the crack influence coefficient stored in the database, For the Part Type The residual stress index of each component, is the residual stress influence coefficient stored in the database, For the Part Type The grain size index of each component, is the grain size influence coefficient stored in the database, , , is the number of part types, , is the number of parts.

[0053] What needs to be explained is that 、 、 The specific steps to obtain are: This factor reflects the performance changes of parts in a specific working environment, mainly considering the long-term impact of the environment on the parts, such as the impact of external factors such as temperature, humidity, and corrosion on the material and structure of the parts. This coefficient is usually obtained by combining experimental data and environmental monitoring data to evaluate the durability and reliability of parts in a specific environment. Taking into account the impact of the material and size of the part on its long-term performance, especially its behavior under dynamic loads and cyclic changes, this coefficient usually needs to be calculated based on the material properties of the part (such as strength, rigidity, fatigue life, etc.) as well as its size, shape and other parameters, reflecting the adaptability and stability of the material and structure under different working conditions; This factor measures the impact of a part's storage method and conditions on its long-term performance. Parts may be affected by vibration, friction, compression, and other factors during storage, all of which may affect their ultimate performance. Therefore, this factor is usually calculated based on the part's location, storage environment (such as proper moisture isolation and temperature control), and external influencing conditions (such as stacking pressure).

[0054] In this implementation plan, through scientific multi-parameter analysis, the long-term toughness of parts is comprehensively evaluated, providing production workshops with more accurate quality assessment and decision-making basis. By analyzing multiple key factors such as the number of cracks, residual stress, grain size, etc. of each component and combining them with standardization, the differences between different dimensions can be effectively eliminated to ensure the consistency and comparability of data. These parameters are obtained using precise testing technologies such as X-ray diffraction, neutron diffraction, hole drilling, and microscopy to ensure high accuracy and high reliability of the measurement results. By comprehensively analyzing these data, the long-term toughness index obtained can accurately reflect the durability and fatigue resistance of parts in long-term use. This method The advantage of this method is that microstructural characteristics such as residual stress and grain size directly affect the material's resistance to deformation and crack growth. Traditional quality assessments often ignore these influencing factors, but this solution incorporates them into the assessment, significantly improving the accuracy of predicting the long-term reliability of components. The comprehensive analysis of the number of cracks, residual stress and grain size provides the workshop with more scientific maintenance and scrapping decision-making support, avoiding misjudgments in traditional methods and reducing resource waste. In addition, standardized data processing not only improves analysis efficiency, but also enhances the comparability of evaluation results between different part types, making the entire component management process more intelligent and automated, effectively improving workshop management level and production efficiency.

[0055] Specifically, it also includes: a maintenance analysis unit and a maintenance judgment unit; the maintenance analysis unit is used to analyze the maintenance evaluation index of each defective part in each defective part type; the maintenance judgment unit is used to judge and analyze the maintenance evaluation index of each defective part in each defective part type and the preset maintenance evaluation interval, and divide it into repairable defective parts and scrapped defective parts according to the judgment and analysis results (that is, parts within the maintenance evaluation interval are regarded as repairable defective parts, and parts below the lower limit of the maintenance evaluation interval are regarded as scrapped defective parts), and generate a parts declaration report and send it to relevant staff.

[0056] Among them, the parts declaration report includes the production date, basic integrity index, long-term toughness index, comprehensive production assessment index, and maintenance assessment index of each part in each part type, and is generated in a tabular form.

[0057] In this implementation, the combination of a repair analysis unit and a repair judgment unit enables automated repair assessment and decision-making, significantly improving the efficiency and accuracy of parts management in the workshop. The system comprehensively analyzes the parts' repair assessment index and automatically categorizes them as repairable or scrap based on multiple factors (such as crack depth, material fatigue, and machining errors), eliminating the errors and subjective biases of manual judgment. This intelligent process reduces human intervention and ensures scientific and consistent repair decisions. Furthermore, the system generates detailed parts declaration reports, including key data such as the part's production date, basic integrity index, and long-term toughness index, providing clear quality and repair information for workshop management. Automated repair decisions ensure optimal resource allocation, reduce repair costs and unnecessary waste, and ensure smooth production processes. Ultimately, the system provides a transparent, efficient, and data-driven management solution for the workshop, optimizing parts usage and repair decisions, and improving overall production efficiency and parts utilization.

[0058] Specifically, if Figure 3 As shown, for each defective component in each defective part type, the specific steps of analyzing the repair evaluation index are as follows: for each defective component in each defective part type, obtain the crack depth value of each crack, and perform a comprehensive analysis (i.e., sum analysis) to obtain the crack depth index of each defective component in each defective part type; for each defective component in each defective part type, analyze the processing error index separately, and the specific steps are: perform a weighted analysis on the dimensional error (the absolute difference between the actual dimensional value and the designed dimensional value) and the weight error (the absolute difference between the actual weight value and the designed weight value) of each defective component in each defective part type; obtain the material fatigue index, surface damage index, repair time prediction value, and repair cost prediction value of each defective component in each defective part type, and perform a comprehensive analysis in combination with the crack depth index and processing error index of the corresponding defective component to obtain the repair evaluation index of each defective component in each defective part type.

[0059] Among them, the crack depth value can be measured and obtained through ultrasonic testing, X-ray testing, eddy current testing, and surface flaw detection.

[0060] Ultrasonic testing method: The depth of the crack is detected by the difference in ultrasonic wave speed and propagation time. Ultrasonic waves can effectively penetrate the material and detect internal defects.

[0061] X-ray detection method: X-rays penetrate the material and estimate the depth of the crack based on the difference in X-ray absorption caused by the crack.

[0062] Eddy current testing method: Detect cracks on the surface and near the surface of the material through electromagnetic induction, and then measure the crack depth.

[0063] Surface flaw detection method: Determine the depth by observing the crack morphology through a microscope, usually used for smaller cracks.

[0064] The specific steps for obtaining the material fatigue index are: testing through standard fatigue testing equipment (such as a tensile fatigue testing machine) to obtain the fatigue limit of defective parts, and based on the experimental results, obtain the fatigue strength coefficient of the material under a certain load and temperature, and then perform multiplication analysis to obtain the material fatigue index.

[0065] The specific formula for calculating the repair assessment index of each defective component in each defective part type is as follows: ;in, For the The first type of defective part Repair assessment index of defective parts, For the The first type of defective part Material fatigue index of defective parts, is the material fatigue recovery coefficient stored in the database, For the The first type of defective part The surface damage index of defective parts, is the surface damage repair coefficient stored in the database, For the The first type of defective part The crack depth index of each defective component, is the crack depth repair coefficient stored in the database, For the The first type of defective part The processing error index of defective parts, is the machining error repair coefficient stored in the database, , For the The first type of defective part The predicted repair time for each defective component, is the repair time coefficient stored in the database, For the The first type of defective part The estimated cost of repairing a defective component, is the repair cost coefficient stored in the database, , , is the number of defective part types, , is the number of defective parts.

[0066] What needs to be explained is that 、 、 、 The specific steps to obtain are: It reflects the impact of environmental factors on the performance of parts. Environmental factors include external conditions such as temperature, humidity, corrosion, vibration, etc. These conditions may cause the performance of parts to degrade or change. Therefore, It is necessary to conduct long-term tracking and analysis of the use of parts in specific environments and collect experimental data to revise the basic integrity index. For example, in high humidity or high temperature environments, some materials may soften or corrode, affecting their structural stability. It is related to the changes of parts during storage. During storage, parts may be deformed or worn due to improper stacking, external pressure, vibration and other factors, which will affect the subsequent performance of the parts. Therefore, This correction factor is obtained by evaluating the environmental stress and physical shock that the parts may encounter in the warehouse or during storage. This usually requires detailed recording and analysis of the storage conditions (such as temperature, humidity, storage method, etc.) of the parts to derive the correction factor. It is mainly to correct the errors that may be encountered during the operation of the parts. For example, during the assembly and use process, improper operation (such as overloading, incorrect assembly method, etc.) may cause the loss or damage of the part function. Therefore, By analyzing errors or anomalies during operation and combining historical data, we can estimate the damage that parts may face in actual applications. The acquisition of this coefficient usually depends on operating standards, failure mode analysis, and the actual operation of the equipment. Consider the impact of design defects or imperfect design stages on part performance. If a part's design has defects or unreasonableness, it may lead to performance degradation or premature failure during use. It is obtained through evaluation, simulation and actual measurement data analysis in the design phase. Design review and comparative testing are often used to discover possible deficiencies in the design phase, which can then be corrected through this coefficient.

[0067] 、 The specific steps to obtain are: The main purpose is to modify the performance requirements of the parts in specific application scenarios, especially considering factors such as the load, pressure or stress that the parts need to withstand. In different application scenarios, the parts may need to cope with different workloads and extreme working conditions. Therefore, Obtained by analyzing the load conditions of the parts in actual applications. Actual working conditions test data, historical usage and simulation experiments are often used to determine this factor. Related to the working environment of the parts, especially the performance when used under extreme conditions (such as high temperature, high pressure, extreme cold, etc.). This is obtained by analyzing the long-term operation of parts in special working environments. For example, in a high temperature environment, the material of the parts may deform, melt or chemically react, resulting in changes in their performance. Therefore, The acquisition of performance data depends on long-term usage data under specific working conditions and analysis of the impact of environmental factors on component performance.

[0068] In this implementation plan, a scientific and reasonable repair evaluation method is provided by accurately analyzing multiple key factors of defective parts (such as crack depth, processing error, material fatigue, surface damage, etc.). By comprehensively analyzing the crack depth and processing error of each part, combined with the crack depth index, processing error index and other influencing factors, the system can more accurately evaluate the repair feasibility of the parts and provide data support for maintenance decisions. First, by using advanced technologies such as ultrasonic testing, X-ray testing, eddy current testing and surface flaw detection, the system can accurately obtain the crack depth of the parts. These methods can efficiently and non-destructively detect The system can identify cracks and quantify their depth, providing an important basis for subsequent repair judgments. Secondly, through weighted analysis of dimensional error and weight error, the system effectively evaluates the processing error index of parts. The comprehensive analysis of these errors helps to judge the difficulty of repairing parts. In addition, the system combines factors such as material fatigue strength, surface damage, repair time and repair cost to form a comprehensive repair assessment index. Through these comprehensive analyses, the system can more accurately predict the repair effect and required resources of parts, avoiding the premature scrapping of repairable parts or the incorrect repair of difficult-to-repair parts, reducing unnecessary waste of resources.

[0069] Specifically, for each defective component in each defective part type, the specific steps of analyzing the processing error index are as follows: read the actual size value and design size value, the actual weight value and design weight value of each defective component in each defective part type and perform absolute difference analysis (i.e., the absolute value of the difference) to obtain the size error and weight error of each defective component in each defective part type; pre-process the size error and weight error of each defective component in each defective part type (e.g., standardization processing, the purpose of which is to eliminate dimensional effects), and perform a comprehensive analysis (i.e., weighted analysis) after pre-processing to obtain the processing error index of each defective component in each defective part type.

[0070] In this implementation plan, by accurately calculating and analyzing the machining error index of parts, the manufacturing accuracy and repair feasibility of parts are effectively evaluated, thereby providing a scientific basis for repair and scrapping decisions. Specifically, the system first analyzes the difference between the actual size and the designed size, and the difference between the actual weight and the designed weight of each defective part, and calculates the size error and weight error. This approach can objectively and directly reveal the accuracy problems of parts in the production process, and provide basic data for subsequent repair evaluations. Then, by standardizing these error values, the system can eliminate the influence of different dimensions and units, making various error items comparable in comprehensive analysis. This standard The standardization step helps ensure that various parameters are not affected by their dimensions during calculation, thereby ensuring the accuracy and reliability of the evaluation. By performing a weighted analysis on the standardized dimensional error and weight error, the system can comprehensively analyze the impact of different errors on component quality and obtain a final processing error index. This processing error index provides a quantitative basis for maintenance decisions and can help workshops identify parts that are difficult to repair due to processing errors. Compared with the traditional method that relies on manual experience, this data-based and standardized analysis method can effectively improve the accuracy of maintenance judgments, reduce unnecessary waste of resources, and ensure that the maintenance priorities of parts are scientifically divided, thereby improving production efficiency and the service life of parts.

[0071] Specifically, the specific steps for obtaining the surface damage index of each defective component in each defective component type are as follows: read the surface roughness index of each defective component in each defective component type, and obtain the scratch depth index (i.e., the average result of the scratch depth values ​​of several measurement points) and the wear layer thickness index (i.e., the average result of the wear layer thickness values ​​of several measurement points) of each defective component in each defective component type, and perform preprocessing (e.g., standardization processing, the purpose of which is to eliminate the dimensional effect); perform a comprehensive analysis (i.e., weighted analysis) on the surface roughness index, scratch depth index, and wear layer thickness index of each defective component in each defective component type after preprocessing to obtain the surface damage index of each defective component in each defective component type.

[0072] The scratch depth value can be measured using a microscope or a laser scanner. The deeper the scratch, the more serious the damage.

[0073] The wear layer thickness value can be measured by surface thickness measurement tools (such as X-ray or ultrasonic thickness gauge).

[0074] In this implementation scheme, through accurate surface damage index analysis, the damage condition of the component surface can be comprehensively evaluated, providing an important basis for subsequent repair decisions. First, by accurately measuring the surface quality indicators of the component such as surface roughness, scratch depth, wear layer thickness, etc., the system can understand the degree of damage to the component surface in detail. Specifically, the measurement of scratch depth and wear layer thickness can be completed by high-precision tools such as microscopes, laser scanners, X-rays or ultrasonic thickness gauges. These measurement tools can accurately capture subtle damage on the surface of components and provide real damage data. After data acquisition, the system eliminates the dimensional influence between different parameters through standardization processing, making various data comparable, thereby ensuring the accuracy of comprehensive analysis. The standardized surface roughness index, The scratch depth index and wear layer thickness index will undergo weighted analysis to obtain the final surface damage index. This weighted analysis step ensures the comprehensiveness and scientific nature of the evaluation results by combining the impact of various indicators on the surface damage of parts. Through this multi-dimensional damage analysis, the system can more accurately identify the severity of surface damage and help workshops determine which parts are difficult to repair due to surface damage and which can be effectively repaired. Compared with traditional manual inspection or single indicator analysis methods, this solution based on precise measurement and weighted analysis can effectively improve the scientific nature of maintenance decisions, avoid repairing parts that are not worth repairing, and save repair resources. Ultimately, the system improves the stability of the production process, the service life of parts, and optimizes the maintenance process through more intelligent data analysis.

[0075] Specifically, the specific steps for obtaining the repair time prediction value and repair cost prediction value of each defective component in each defective part type are: obtaining the historical repair time value and historical repair cost value of the historical defective repair parts corresponding to each defective component in each defective part type, and performing comprehensive analysis on them respectively (i.e., mean analysis).

[0076] In this implementation plan, historical data is analyzed to predict the repair time and repair cost of parts to achieve more accurate and scientific maintenance decisions. The system obtains the historical repair time value and historical repair cost value of each defective part, performs mean analysis on these data, and thus obtains a repair time and cost prediction based on historical experience. This method utilizes the accumulated actual repair data to make the prediction closer to the actual situation and avoids the deviation of relying solely on theoretical models or manual experience. Through the analysis of historical data, workshop managers can clearly understand the average time and cost required to repair similar defective parts, so as to arrange maintenance plans more reasonably, optimize resource allocation, and avoid excessive or insufficient maintenance resource investment. In addition, accurate repair time and cost predictions can also help workshops evaluate the economy and feasibility of repairs and avoid unnecessary waste of resources. Compared with traditional repair time and cost prediction methods, this prediction based on historical data is more data-supported and practical, effectively improving the efficiency of the maintenance process and the accuracy of decision-making.

[0077] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0078] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A workshop spare parts declaration management system, characterized in that: include: Data acquisition and division unit, defect judgment unit; The data acquisition and classification unit is used to obtain several parts produced after the preset workshop production cycle ends, and classify them into types to obtain several part types, and analyze the basic integrity index and long-term toughness index of each part in each part type, and perform comprehensive analysis to obtain the comprehensive production evaluation index of each part in each part type. The calculation formula is as follows: ； in, 、 、 In order Part Type Comprehensive production assessment index, basic integrity index, and long-term resilience index of each component. 、 、 They are the integrity influence coefficient, toughness influence coefficient, and interaction influence coefficient stored in the database, 、 、 , which are the integrity adjustment coefficient, toughness adjustment coefficient, and interaction adjustment coefficient stored in the database respectively. , is the number of part types, , is the number of parts; The defect judgment unit is used to judge and analyze the comprehensive production evaluation index of each component in each part type and the preset production evaluation interval, and screen production defective components based on the judgment and analysis results.

2. The workshop spare parts declaration management system according to claim 1 is characterized in that: The specific steps for analyzing the basic integrity of each component in each part type are as follows: Obtaining production basic property data of each component of each part type and surface roughness values ​​and surface hardness values ​​at each measurement position, and preprocessing the data, wherein the production basic property data includes actual size values, design size values, actual weight values, and design weight values; Comprehensively analyze the surface roughness value and surface hardness value of each component at each measurement position of each component in each part type after pretreatment to obtain the surface roughness index and surface hardness index of each component in each part type; The pre-processed production basic property data of each component in each part type are combined with the corresponding surface roughness index and surface hardness index for comprehensive analysis to obtain the basic integrity index of each component in each part type.

3. The workshop spare parts declaration management system according to claim 2 is characterized in that: The specific formula for calculating the basic integrity index of each component in each part type is as follows: ； in, For the Part Type The basic integrity index of each component, 、 、 、 、 、 After preprocessing, Part Type The basic integrity index, actual size value, design size value, actual weight value, design weight value, surface roughness index, surface hardness index of each component, 、 、 、 The influence coefficients of size, weight, surface roughness and surface hardness stored in the database are: , , is the number of part types, , is the number of parts.

4. The workshop spare parts declaration management system according to claim 1 is characterized in that: The specific steps for analyzing the long-term toughness index of each component in each part type are as follows: Obtain and pre-process the crack quantity value of each component in each part type and the residual stress value and grain size value at each measurement position, and pre-process them separately; Comprehensively analyze the residual stress value and grain size value at each measurement position of each component in each part type after pretreatment to obtain the residual stress index and grain size index of each component in each part type; The crack quantity value, residual stress index and grain size index of each component in each part type after pretreatment are comprehensively analyzed to obtain the long-term toughness index of each component in each part type.

5. The workshop spare parts declaration management system according to claim 1 is characterized in that: Also includes: Maintenance analysis unit, maintenance judgment unit; The maintenance analysis unit is used to analyze the maintenance evaluation index for each defective component in each defective part type; The maintenance judgment unit is used to judge and analyze the maintenance evaluation index of each defective component in each defective part type and the preset maintenance evaluation interval, and classify them into repairable defective components and scrapped defective components based on the judgment and analysis results, and generate a component declaration report and send it to relevant staff.

6. The workshop spare parts declaration management system according to claim 5, characterized in that: For each defective component in each defective part type, the specific steps for analyzing the repair assessment index are as follows: For each defective component in each defective component type, the crack depth value of each crack is obtained respectively, and a comprehensive analysis is performed to obtain the crack depth index of each defective component in each defective component type; For each defective component in each defective part type, the machining error index is analyzed separately; The material fatigue index, surface damage index, repair time prediction value, and repair cost prediction value of each defective component in each defective part type are obtained, and a comprehensive analysis is performed in combination with the crack depth index and processing error index of the corresponding defective component to obtain the repair assessment index of each defective component in each defective part type.

7. The workshop spare parts declaration management system according to claim 6, characterized in that: The specific formula for calculating the repair assessment index of each defective component in each defective part type is as follows: ； in, 、 、 、 、 、 、 In order The first type of defective part Repair assessment index, material fatigue index, surface damage index, crack depth index, processing error index, repair time prediction value, repair cost prediction value of each defective component, 、 、 、 These are the material fatigue repair coefficient, surface damage repair coefficient, crack depth repair coefficient, and processing error repair coefficient stored in the database. , 、 They are the repair time coefficient and repair cost coefficient stored in the database, , , is the number of defective part types, , is the number of defective parts.

8. The workshop spare parts declaration management system according to claim 6, characterized in that: For each defective component in each defective part type, the specific steps for analyzing the processing error index are as follows: Read the actual size value and design size value, actual weight value and design weight value of each defective part in each defective part type and perform absolute difference analysis on each of them to obtain the size error and weight error of each defective part in each defective part type; The size error and weight error of each defective component in each defective component type are preprocessed, and a comprehensive analysis is performed after the preprocessing to obtain the processing error index of each defective component in each defective component type.

9. The workshop spare parts declaration management system according to claim 6, characterized in that: The specific steps for obtaining the surface damage index of each defective component in each defective part type are as follows: Reading the surface roughness index of each defective component in each defective component type, and obtaining the scratch depth index and the wear layer thickness index of each defective component in each defective component type, and performing preprocessing; The surface roughness index, scratch depth index and wear layer thickness index of each defective component in each defective component type after pretreatment are comprehensively analyzed to obtain the surface damage index of each defective component in each defective component type.

10. The workshop spare parts declaration management system according to claim 6, characterized in that: The specific steps for obtaining the repair time prediction value and repair cost prediction value of each defective component in each defective part type are: obtaining the historical repair time value and historical repair cost value of the historical defective repair parts corresponding to each defective component in each defective part type, and performing comprehensive analysis to obtain them respectively.

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