A production control optimization method and system for fastening functional parts

Abstract

The present invention discloses a production control optimization method and system for fastening functional parts. The thermal distribution image of the production sample is obtained through heat treatment, the quality change is evaluated, and the comprehensive impact on the quality is analyzed by combining the data of the degree of oxidation of the molten material and the temperature drop rate. Based on this, the preparation amount and time of the molten material are dynamically adjusted to form a first production control plan; at the same time, the temperature drop rate of the curing process is accurately controlled to formulate a second control plan. The present invention effectively integrates the key parameters in the production process, realizes the accurate prediction and control of the production quality of fastening functional parts, and significantly improves the production efficiency and product quality stability.

Description

Technical Field

[0001] The present invention relates to the technical field of production control, and in particular to a production control optimization method and system for fastening functional parts. Background Art

[0002] In the production process of fastening functional parts, quality control is a key factor to ensure product performance and service life. However, traditional production control methods often rely on experience and post-testing, which makes it difficult to accurately grasp the quality changes in the production process in real time. In particular, the degree of oxidation of the molten material and the temperature drop rate during the solidification process have a significant impact on the quality of fastening functional parts, but traditional methods are difficult to accurately control these parameters.

[0003] First, the degree of oxidation of the molten material directly affects its physical and chemical properties. The gas generated during the oxidation process or the excessive degree of oxidation reduces the viscosity of the molten material, causing cracks between the molten material, which in turn affects the mechanical properties and durability of the product. If the degree of oxidation is too high, it will lead to problems such as reduced product strength and easy breakage. However, traditional production methods usually lack real-time monitoring and precise control of the degree of oxidation of the molten material, resulting in large fluctuations in product quality.

[0004] Secondly, the temperature drop rate during the curing process is also an important factor affecting product quality. Temperature drop too fast or too slow may lead to problems such as uneven stress distribution and shrinkage deformation inside the product. Traditional production methods often set curing temperature and time based on experience, which is difficult to adapt to the actual needs of different batches of products, resulting in difficult to stabilize product quality.

[0005] Therefore, a production control optimization method and system that can monitor and accurately control key parameters in the production process in real time is needed to improve the production efficiency and product quality stability of fastening functional parts. The present invention is proposed to meet this demand. The thermal distribution image of the production sample is obtained by heat treatment, and the data of the oxidation degree and temperature drop rate of the molten material are combined to achieve accurate control of the production process. Summary of the invention

[0006] In order to solve at least one of the above technical problems, the present invention proposes a production control optimization method and system for fastening functional parts.

[0007] A first aspect of the present invention provides a method for optimizing production control of a fastening functional part, comprising:

[0008] Acquire production samples of the fastening functional parts in a preset time period, and perform heat treatment on the production samples to draw a thermal distribution image of the production samples;

[0009] Calculating the spatial smoothness of the thermal distribution image, performing quality assessment on the production sample according to the spatial smoothness, and obtaining quality change data of the production sample within a preset time period;

[0010] Obtaining oxidation degree change data of the molten material within a preset time period and temperature drop rate change data during the solidification process of the production sample, performing correlation analysis on the quality change data, the oxidation degree change data, and the temperature drop rate change data, determining the influence of the oxidation degree and temperature drop change of the molten material on the quality of the production sample, and obtaining oxidation-quality influence data and temperature-quality influence data;

[0011] Determine the preparation amount and preparation time of the molten material according to the oxidation-quality impact data and the production demand data of the current batch of fastening functional parts to obtain a first production control plan;

[0012] Controlling the temperature drop rate of the fastening functional component during the curing process according to the temperature-quality influence data;

[0013] The production of the fastening functional parts is controlled according to the first production control scheme and the second production control scheme.

[0014] In this solution, the production samples of the fastening functional parts in a preset time period are obtained, and the production samples are heat-treated to draw a thermal distribution image of the production samples, specifically:

[0015] Acquire production samples of the same batch of fastening functional parts within a preset time period according to a preset time period, and heat the production samples;

[0016] The appearance temperature distribution of the production sample is obtained based on the infrared thermal imaging device, and the appearance temperature distribution is visualized to obtain a thermal distribution image of the production sample.

[0017] In this solution, the spatial smoothness of the thermal distribution image is calculated, and the quality of the production sample is evaluated according to the spatial smoothness to obtain the quality change data of the production sample within a preset time period, specifically:

[0018] The thermal distribution image is converted into a grayscale image, and the gradient of each pixel in the grayscale image in the x direction and the y direction is calculated by a Sobel operator to obtain the horizontal gradient and the vertical gradient of the grayscale image;

[0019] Calculating the gradient amplitude of each pixel of the grayscale image according to the horizontal gradient and the vertical gradient, and calculating the average gradient amplitude and the gradient amplitude standard deviation of the grayscale image according to the gradient amplitude of each pixel;

[0020] Determine the spatial smoothness of the thermal distribution image according to the average gradient amplitude and the gradient amplitude standard deviation, mark the area where the spatial smoothness is less than a preset value, and obtain the area to be evaluated for quality;

[0021] Acquire spatial smoothness features formed by different defect types of the fastening functional part, wherein the spatial smoothness features include smoothness reduction degree features, smoothness reduction range features, and shape features of the smoothness reduction range;

[0022] A defect type recognition model is constructed based on a convolutional neural network, and the spatial smoothness features formed by different defect types of the fastening functional parts are introduced into the defect type recognition model for learning and training;

[0023] Extracting the spatial smoothness features of the area to be evaluated for quality, importing the spatial smoothness features of the area to be evaluated for quality into the trained defect type recognition model to perform defect type recognition, and obtaining a defect type recognition result of the production sample;

[0024] Determine the defect range and defect quantity of each production sample according to the defect type result and the spatial smoothness characteristics of the area to be quality evaluated, and perform quality evaluation on the production sample according to the defect range and defect quantity to obtain a first quality evaluation result of each production sample;

[0025] Obtaining standard size information of the fastening functional part and size information of the production sample, comparing the standard size information with the size information, evaluating the production size accuracy of the production sample, and obtaining a second quality evaluation result

[0026] The first quality assessment result and the second quality assessment result are sorted according to the acquisition time of the production sample to obtain quality change data of the production sample within a preset time period, and the quality change data includes the first quality change data and the second quality change data.

[0027] In this solution, the oxidation degree change data of the molten material within a preset time period and the temperature drop rate change data of the production sample solidification process are obtained, and a correlation analysis is performed based on the quality change data, the oxidation degree change data, and the temperature drop change data to determine the influence of the oxidation degree and temperature drop change of the molten material on the quality of the production sample, and obtain the oxidation-quality influence data and the temperature-quality influence data, which are specifically:

[0028] Acquiring atomic absorption spectrum change data of the molten material of the fastening functional component within a preset time period, performing material analysis on the atomic absorption spectrum change data, and determining material change data of the molten material within the preset time period;

[0029] Determining the change in oxidation degree of the molten material within a preset time period according to the material change data to obtain oxidation degree change data;

[0030] Acquire temperature drop change data of the molten material of the production sample during the solidification process of the molten material, and determine temperature drop rate change data of the production sample within a preset time period according to the temperature drop change data;

[0031] Sorting the first mass change data, the second mass change data, the oxidation degree change data, and the temperature drop rate change data in time series to determine the first mass, the second mass, the oxidation degree, and the temperature drop rate at each time node;

[0032] The first mass and oxidation degree at each time point are used to construct a first data matrix, and the second mass and temperature drop rate at each time node are used to construct a second data matrix;

[0033] Performing data normalization processing on the first data matrix and the second data matrix to obtain a standardized first data matrix and a standardized second data matrix, calculating a covariance matrix of each data matrix, and performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors of each covariance matrix;

[0034] Select the eigenvectors corresponding to the first two largest eigenvalues ​​to form the first principal component and the second principal component, calculate the loadings of the first principal component and the second principal component, and construct the first loading matrix and the second loading matrix;

[0035] The first loading matrix and the second loading matrix are analyzed to determine the influence of the oxidation degree of the molten material on the first mass of the production sample and the influence of the temperature drop rate of the molten material on the second mass of the production sample, and obtain oxidation-quality influence data and temperature-quality influence data. The oxidation-quality influence data and temperature-quality influence data include correlation data on the influence of the oxidation degree of the molten material on the first mass of the production sample and the temperature drop rate of the molten material on the second mass of the production sample, influence degree data, and production quality data of the production samples corresponding to different oxidation degrees and temperature drop rates.

[0036] In this solution, the preparation amount and preparation time of the molten material are determined according to the oxidation-quality impact data and the production demand data of the current batch of fastening functional parts to obtain a first production control solution, which is specifically:

[0037] Acquire production demand data of the current batch of fastening functional parts, wherein the production demand data includes a first quality requirement and a production quantity requirement of the fastening functional parts;

[0038] Obtaining production process standard data of fastening functional parts and production capacity data of fastening functional parts production machinery, determining the production volume of fastening functional parts per unit time and the amount of molten material used for a single fastening functional part according to the production process standard data and production capacity data, and obtaining the production capacity data of the fastening functional parts production machinery;

[0039] Calculate the production time required for the fastening functional parts production machinery to complete the production quantity requirement of the current batch of fastening functional parts according to the production quantity requirement of the current batch of fastening functional parts and the production capacity data of the fastening functional parts production machinery, and mark it as the first time threshold;

[0040] The first quality requirement of the current batch of fastening functional parts is calibrated as the minimum production quality threshold, and the oxidation degree of the molten material when the fastening functional parts reach the minimum production quality threshold during the production process is calculated according to the oxidation-quality impact data, and the time required for the molten material to reach the oxidation degree is calculated and calibrated as the second time threshold;

[0041] When the first time threshold is less than the second time threshold, the preparation amount of the molten material is calculated according to the production quantity requirement and the usage amount of the molten material of a single fastening functional component;

[0042] When the first time threshold is greater than the second time threshold, the total preparation amount of the molten material is calculated according to the production quantity demand and the molten material usage of a single fastening functional part, the total production amount of the fastening functional parts when the production time of the fastening functional part production machinery reaches the first time threshold is calculated according to the production capacity data of the fastening functional part production machinery, the preparation amount of the molten material when the first time threshold is reached is calculated according to the total production amount and the molten material usage of a single fastening functional part, and the single preparation amount of the first N times of molten material preparation when producing the current batch of fastening functional parts is obtained, the total preparation amount is divided by the single preparation amount and the remainder is taken, the quotient obtained is the N value, and the remainder is the last molten material preparation amount, the preparation time of the next molten material is determined according to the production time of the single preparation amount of the molten material, and the first production control plan for the fastening functional parts is obtained.

[0043] In this solution, the temperature drop rate of the production sample during the curing process is controlled according to the temperature-quality effect to obtain a second production control solution, which is specifically:

[0044] Determining the effect of the temperature drop rate on the degree of shrinkage during the curing process of the fastening functional component according to the temperature-quality influence data;

[0045] Determine the temperature drop rate range in which the fastening functional part will not cause shrinkage of the production sample during the curing process according to the influence of the temperature drop rate on the shrinkage degree of the fastening functional part during the curing process;

[0046] The temperature drop rate of the fastening functional parts during the curing process is controlled according to the temperature drop rate interval to obtain a second production control scheme.

[0047] The second aspect of the present invention further provides a production control optimization system for fastening functional parts, the system comprising: a memory, a processor, the memory comprising a production control optimization method program for fastening functional parts, and when the production control optimization method program for fastening functional parts is executed by the processor, the following steps are implemented:

[0048] Acquire production samples of the fastening functional parts in a preset time period, and perform heat treatment on the production samples to draw a thermal distribution image of the production samples;

[0049] Calculating the spatial smoothness of the thermal distribution image, performing quality assessment on the production sample according to the spatial smoothness, and obtaining quality change data of the production sample within a preset time period;

[0050] Obtaining oxidation degree change data of the molten material within a preset time period and temperature drop rate change data during the solidification process of the production sample, performing correlation analysis on the quality change data, the oxidation degree change data, and the temperature drop rate change data, determining the influence of the oxidation degree and temperature drop change of the molten material on the quality of the production sample, and obtaining oxidation-quality influence data and temperature-quality influence data;

[0051] Determine the preparation amount and preparation time of the molten material according to the oxidation-quality impact data and the production demand data of the current batch of fastening functional parts to obtain a first production control plan;

[0052] Controlling the temperature drop rate of the fastening functional component during the curing process according to the temperature-quality influence data;

[0053] The production of the fastening functional parts is controlled according to the first production control scheme and the second production control scheme.

[0054] The present invention discloses a production control optimization method and system for fastening functional parts. The thermal distribution image of the production sample is obtained through heat treatment, the quality change is evaluated, and the comprehensive impact on the quality is analyzed by combining the data of the degree of oxidation of the molten material and the temperature drop rate. Based on this, the preparation amount and time of the molten material are dynamically adjusted to form a first production control plan; at the same time, the temperature drop rate of the curing process is accurately controlled to formulate a second control plan. The present invention effectively integrates the key parameters in the production process, realizes the accurate prediction and control of the production quality of fastening functional parts, and significantly improves the production efficiency and product quality stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A flow chart showing a method for optimizing production control of a fastening functional part according to the present invention is shown;

[0056] Figure 2 A flow chart of drawing a thermal distribution image of a production sample according to the present invention is shown;

[0057] Figure 3 A flow chart showing a second production control scheme obtained by the present invention is shown;

[0058] Figure 4 A block diagram of a production control optimization system for fastening functional parts of the present invention is shown. DETAILED DESCRIPTION

[0059] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0060] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0061] Figure 1 A flow chart of a method for optimizing production control of fastening functional parts according to the present invention is shown.

[0062] like Figure 1 As shown, the first aspect of the present invention provides a method for optimizing production control of a fastening functional part, comprising:

[0063] S102, obtaining production samples of the fastening functional parts in a preset time period, and performing heat treatment on the production samples to draw a thermal distribution image of the production samples;

[0064] S104, calculating the spatial smoothness of the thermal distribution image, and performing quality assessment on the production sample according to the spatial smoothness to obtain quality change data of the production sample within a preset time period;

[0065] S106, obtaining oxidation degree change data of the molten material and temperature drop rate change data of the production sample solidification process within a preset time period, performing correlation analysis on the quality change data, the oxidation degree change data, and the temperature drop rate change data, determining the influence of the oxidation degree and temperature drop change of the molten material on the quality of the production sample, and obtaining oxidation-quality influence data and temperature-quality influence data;

[0066] S108, determining the preparation amount and preparation time of the molten material according to the oxidation-quality impact data and the production demand data of the current batch of fastening functional parts, and obtaining a first production control plan;

[0067] S110, controlling the temperature drop rate of the fastening functional component during the curing process according to the temperature-quality influence data to obtain a second production control plan;

[0068] S112, controlling the production of the fastening functional parts according to the first production control scheme and the second production control scheme.

[0069] It should be noted that by heating the production samples, the thermal distribution images of the production samples are obtained to evaluate the spatial smoothness of the images. The thermal distribution images can intuitively show the temperature distribution of the production samples in different areas. The spatial smoothness reflects the uniformity of the thermal distribution images. In the case of quality defects in the production samples, the defects will cause the temperature conduction medium to change, resulting in discontinuity of temperature conduction, which will cause changes in the uniformity of the thermal distribution images. Therefore, the defects of the production samples are identified based on the spatial smoothness and then the quality is evaluated. The quality change data of the production samples within the preset time period are compared with the oxidation degree change data of the molten material and the production samples. The correlation analysis of the temperature drop rate change data during the curing process is carried out to determine the influence of different oxidation degrees of the molten material and different temperature drop rates during the molten material curing process on the quality of the final molding of the fastening functional parts. The preparation amount and preparation time of the molten material are determined according to the oxidation-quality influence data to avoid excessive preparation that leads to excessive oxidation degree of the molten material over time, resulting in the production of fastening functional parts failing to meet quality requirements; the temperature drop rate of the fastening functional parts during the curing process is controlled according to the temperature-quality influence data to avoid shrinkage of the fastening functional parts due to too rapid temperature drop, or excessive cross-linking and embrittlement due to too slow temperature drop, so as to ensure the stability and durability of the product.

[0070] Figure 2 A flow chart of drawing a thermal distribution image of a production sample according to the present invention is shown.

[0071] According to an embodiment of the present invention, the production samples of the fastening functional parts in a preset time period are obtained, and the production samples are heat-treated to draw a thermal distribution image of the production samples, specifically:

[0072] S202, obtaining production samples of fastening functional parts of the same batch within a preset time period according to a preset time period, and performing a heating treatment on the production samples;

[0073] S204, obtaining the appearance temperature distribution of the production sample based on the infrared thermal imaging device, and visualizing the appearance temperature distribution to obtain a thermal distribution image of the production sample.

[0074] It should be noted that the heating treatment is carried out by heating the entire production sample to a preset temperature threshold and leaving it alone for a preset time. Since when there are defects, the defect locations exhibit different temperature drop rates, leaving it alone for a preset time can expose the temperature non-uniformity of the defect location, and then obtain a thermal distribution image, so that the defects can be identified later through the thermal distribution image.

[0075] According to an embodiment of the present invention, the spatial smoothness of the thermal distribution image is calculated, and the quality of the production sample is evaluated according to the spatial smoothness to obtain the quality change data of the production sample within a preset time period, specifically:

[0076] The thermal distribution image is converted into a grayscale image, and the gradient of each pixel in the grayscale image in the x direction and the y direction is calculated by a Sobel operator to obtain the horizontal gradient and the vertical gradient of the grayscale image;

[0077] Calculating the gradient amplitude of each pixel of the grayscale image according to the horizontal gradient and the vertical gradient, and calculating the average gradient amplitude and the gradient amplitude standard deviation of the grayscale image according to the gradient amplitude of each pixel;

[0078] Determine the spatial smoothness of the thermal distribution image according to the average gradient amplitude and the gradient amplitude standard deviation, mark the area where the spatial smoothness is less than a preset value, and obtain the area to be evaluated for quality;

[0079] Acquire spatial smoothness features formed by different defect types of the fastening functional part, wherein the spatial smoothness features include smoothness reduction degree features, smoothness reduction range features, and shape features of the smoothness reduction range;

[0080] A defect type recognition model is constructed based on a convolutional neural network, and the spatial smoothness features formed by different defect types of the fastening functional parts are introduced into the defect type recognition model for learning and training;

[0081] Extracting the spatial smoothness features of the area to be evaluated for quality, importing the spatial smoothness features of the area to be evaluated for quality into the trained defect type recognition model to perform defect type recognition, and obtaining a defect type recognition result of the production sample;

[0082] Determine the defect range and defect quantity of each production sample according to the defect type result and the spatial smoothness characteristics of the area to be quality evaluated, and perform quality evaluation on the production sample according to the defect range and defect quantity to obtain a first quality evaluation result of each production sample;

[0083] Obtaining standard size information of the fastening functional part and size information of the production sample, comparing the standard size information with the size information, evaluating the production size accuracy of the production sample, and obtaining a second quality evaluation result

[0084] The first quality assessment result and the second quality assessment result are sorted according to the acquisition time of the production sample to obtain quality change data of the production sample within a preset time period, and the quality change data includes the first quality change data and the second quality change data.

[0085] It should be noted that the gradient amplitude of each pixel of the thermal distribution image is calculated by the Sobel operator, and the standard deviation between each adjacent gradient amplitude and the average amplitude of the thermal distribution image. The larger the standard deviation, the smaller the spatial smoothness of the thermal distribution image. The smaller the smoothness, the more likely it is that there are defects in the area. Therefore, the spatial smoothness features of such areas are extracted and imported into the defect type recognition model based on the convolutional neural network trained by the spatial smoothness features formed by different defect types of historical fastening functional parts to perform defect type recognition. Quality assessment is performed based on the defect type recognition result to obtain a first quality assessment result. The larger the defect range, the higher the defect type. , the more defects there are, the lower the quality; and the size information of the production sample is compared with the standard size to evaluate the second quality of the production sample and obtain the second quality evaluation result; the different defect types include bubbles and cracks; by calculating the spatial smoothness of the thermal distribution image and combining the Sobel operator to extract the gradient information of the image, the defective areas that may exist in the production sample can be accurately identified, which not only improves the accuracy of defect detection, but also realizes automated detection, greatly improving production efficiency and detection speed; the first quality is the evaluation quality of the fastening functional parts through defects, and the second quality is the evaluation quality of the fastening functional parts through appearance dimensions.

[0086] According to an embodiment of the present invention, the oxidation degree change data of the molten material within a preset time period and the temperature drop rate change data during the solidification process of the production sample are obtained, and a correlation analysis is performed based on the quality change data, the oxidation degree change data, and the temperature drop change data to determine the influence of the oxidation degree and temperature drop change of the molten material on the quality of the production sample, and obtain the oxidation-quality influence data and the temperature-quality influence data, which are specifically:

[0087] Acquiring atomic absorption spectrum change data of the molten material of the fastening functional component within a preset time period, performing material analysis on the atomic absorption spectrum change data, and determining material change data of the molten material within the preset time period;

[0088] Determining the change in oxidation degree of the molten material within a preset time period according to the material change data to obtain oxidation degree change data;

[0089] Acquire temperature drop change data of the molten material of the production sample during the solidification process of the molten material, and determine temperature drop rate change data of the production sample within a preset time period according to the temperature drop change data;

[0090] Sorting the first mass change data, the second mass change data, the oxidation degree change data, and the temperature drop rate change data in time series to determine the first mass, the second mass, the oxidation degree, and the temperature drop rate at each time node;

[0091] The first mass and oxidation degree at each time point are used to construct a first data matrix, and the second mass and temperature drop rate at each time node are used to construct a second data matrix;

[0092] Performing data normalization processing on the first data matrix and the second data matrix to obtain a standardized first data matrix and a standardized second data matrix, calculating a covariance matrix of each data matrix, and performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors of each covariance matrix;

[0093] Select the eigenvectors corresponding to the first two largest eigenvalues ​​to form the first principal component and the second principal component, calculate the loadings of the first principal component and the second principal component, and construct the first loading matrix and the second loading matrix;

[0094] The first loading matrix and the second loading matrix are analyzed to determine the influence of the oxidation degree of the molten material on the first mass of the production sample and the influence of the temperature drop rate of the molten material on the second mass of the production sample, and obtain oxidation-quality influence data and temperature-quality influence data. The oxidation-quality influence data and temperature-quality influence data include correlation data on the influence of the oxidation degree of the molten material on the first mass of the production sample and the temperature drop rate of the molten material on the second mass of the production sample, influence degree data, and production quality data of the production samples corresponding to different oxidation degrees and temperature drop rates.

[0095] It should be noted that by introducing the PCA (principal component analysis) algorithm, the quality change data are correlated with the oxidation degree change data and the temperature drop change data to determine the impact of the oxidation degree and temperature drop changes of the molten material on the quality of the production samples; by processing the first quality change data, the second quality change data, the oxidation degree change data and the temperature drop rate change data through PCA, these complex data sets can be simplified into several key principal components, making it easier to analyze the relationship between them; in the process of converting data, PCA will sort the principal components according to the size of the eigenvalues, and the principal components with larger eigenvalues ​​contain more information. By calculating the load between each principal component and the original variable (i.e., the coefficient of each variable in the principal component), it is possible to analyze which original variables have the greatest impact on the principal component. In this scheme, by constructing the first data matrix and the second data matrix, and calculating their covariance matrix, eigenvalue, and eigenvector, the correlation between the degree of oxidation of the melt and the rate of temperature drop and the quality of the production sample can be identified; the load matrix obtained by PCA analysis can quantify the degree of influence of the degree of oxidation of the melt and the rate of temperature drop on the quality of the production sample. The greater the absolute value of the load, the greater the influence of the variable on the principal component, which also reflects the degree of influence of the variable on the quality of the production sample; the load is equal to the eigenvector multiplied by the square root of the eigenvalue, which is the weight of each original variable in the new principal component space. The contribution of each variable to the principal component can be understood by looking at the size and sign of the load; for example, if the load of the first principal component is higher on the degree of oxidation variable, it means that the change in the degree of oxidation has a greater impact on the change in the first quality. Similarly, the load of the second principal component can explain the impact of the change in temperature drop on the change in the second quality.

[0096] According to an embodiment of the present invention, the preparation amount and preparation time of the molten material are determined according to the oxidation-quality impact data and the production demand data of the current batch of fastening functional parts to obtain a first production control scheme, which is specifically:

[0097] Acquire production demand data of the current batch of fastening functional parts, wherein the production demand data includes a first quality requirement and a production quantity requirement of the fastening functional parts;

[0098] Obtaining production process standard data of fastening functional parts and production capacity data of fastening functional parts production machinery, determining the production volume of fastening functional parts per unit time and the amount of molten material used for a single fastening functional part according to the production process standard data and production capacity data, and obtaining the production capacity data of the fastening functional parts production machinery;

[0099] Calculate the production time required for the fastening functional parts production machinery to complete the production quantity requirement of the current batch of fastening functional parts according to the production quantity requirement of the current batch of fastening functional parts and the production capacity data of the fastening functional parts production machinery, and mark it as the first time threshold;

[0100] The first quality requirement of the current batch of fastening functional parts is calibrated as the minimum production quality threshold, and the oxidation degree of the molten material when the fastening functional parts reach the minimum production quality threshold during the production process is calculated according to the oxidation-quality impact data, and the time required for the molten material to reach the oxidation degree is calculated and calibrated as the second time threshold;

[0101] When the first time threshold is less than the second time threshold, the preparation amount of the molten material is calculated according to the production quantity requirement and the usage amount of the molten material of a single fastening functional component;

[0102] When the first time threshold is greater than the second time threshold, the total preparation amount of the molten material is calculated according to the production quantity demand and the molten material usage of a single fastening functional part, the total production amount of the fastening functional parts when the production time of the fastening functional part production machinery reaches the first time threshold is calculated according to the production capacity data of the fastening functional part production machinery, the preparation amount of the molten material when the first time threshold is reached is calculated according to the total production amount and the molten material usage of a single fastening functional part, and the single preparation amount of the first N times of molten material preparation when producing the current batch of fastening functional parts is obtained, the total preparation amount is divided by the single preparation amount and the remainder is taken, the quotient obtained is the N value, and the remainder is the last molten material preparation amount, the preparation time of the next molten material is determined according to the production time of the single preparation amount of the molten material, and the first production control plan for the fastening functional parts is obtained.

[0103] It should be noted that, according to the oxidation-quality impact data, different oxidation degrees will cause different degrees of quality impact on the fastening functional parts. Therefore, by obtaining the production demand data of the current batch of fastening functional parts, the time required to produce the batch of fastening functional parts is determined according to the production capacity data of the fastening functional parts, and the first time threshold is obtained. The first time threshold is when the fastening functional parts produced by the molten material at this oxidation degree just meet the first quality requirement; the oxidation degree of the molten material when the fastening functional parts reach the said minimum production quality threshold during the production process is calculated according to the oxidation-quality impact data, and the time required for the molten material to reach the said oxidation degree is calculated, which is calibrated as the second time threshold; because the oxidation of the molten material The higher the degree of oxidation, the lower the first quality of the fastening functional parts; when the first time threshold is less than the second time threshold, when the fastening functional parts production equipment completes the production of this batch of fastening functional parts, the oxidation degree of the molten material has not yet reached the oxidation degree of the molten material corresponding to the first quality requirement, and the production has been completed. At this time, it is only necessary to calculate the preparation amount of the molten material; when the first time threshold is less than the second time threshold, when the fastening functional parts production equipment completes the production of this batch of fastening functional parts, the oxidation degree of the molten material reaches the oxidation degree of the molten material corresponding to the first quality requirement, and the fastening functional parts have not yet been completed. Therefore, it is necessary to judge the preparation amount of the molten material when the first time threshold is reached, because when the first time threshold is reached, the preparation amount of the molten material is When the threshold is reached, the production quality corresponding to the oxidation degree of the molten material is just the first quality requirement. If the molten material continues to oxidize, the production quality of the fastening functional parts produced subsequently will not be able to meet the first quality requirement. Therefore, when the single preparation amount of the molten material is just used up, the oxidation degree of the molten material just reaches the oxidation degree of the molten material corresponding to the first quality requirement; when the first time threshold is greater than the second time threshold, the total preparation amount of the molten material may be large and require multiple preparations. When the first N preparation amounts are just used up, the oxidation degree of the molten material just reaches the oxidation degree of the molten material corresponding to the first quality requirement. In order to avoid excessive preparation of the molten material in the last time, the total preparation amount is divided by the single preparation amount to take the remainder. It is the last preparation quantity; finally determine the preparation time of the molten material, the preparation time of the molten material is when the molten material prepared for a single time is just used up, then the next molten material will be prepared, in fact, it is when the production time of the fastening functional parts production machinery just reaches the first time threshold that the preparation is carried out, because the molten material is just used up when the production time just reaches the first time threshold; by determining the preparation quantity and preparation time of the molten material, it is avoided that the production quality corresponding to the oxidation degree of the molten material is lower than the first quality requirement, and it is avoided that the oxidation degree of the molten material is too high due to excessive preparation of the molten material, before the fastening functional parts production machinery has used up the molten material, and the fastening functional parts produced do not meet the first quality requirement.

[0104] Figure 3 A flow chart of the second production control scheme obtained by the present invention is shown.

[0105] According to an embodiment of the present invention, the temperature drop rate of the production sample during the curing process is controlled according to the temperature-quality influence to obtain a second production control scheme, which is specifically:

[0106] S302, determining the influence of the temperature drop rate on the shrinkage degree of the fastening functional component during the curing process according to the temperature-quality influence data;

[0107] S304, determining a temperature drop rate range in which the fastening functional component will not cause shrinkage of the production sample during the curing process according to the influence of the temperature drop rate on the shrinkage degree of the fastening functional component during the curing process;

[0108] S306, controlling the temperature drop rate of the fastening functional component during the curing process according to the temperature drop rate interval to obtain a second production control solution.

[0109] It should be noted that the second production control scheme avoids the problem of shrinkage of production samples caused by excessive temperature fluctuations or improper temperature drop rate by accurately controlling the temperature drop rate of the fastening functional parts during the curing process. The temperature drop rate is one of the key factors affecting the quality of material curing. An unreasonable temperature drop rate may lead to uneven stress distribution inside the material, and then cause deformation and other problems. By optimizing the temperature drop rate, the defective rate caused by poor curing can be significantly reduced and the product qualification rate can be improved.

[0110] According to an embodiment of the present invention, it also includes:

[0111] Implementing the first production control scheme and the second production control scheme to obtain production quality information of fastening functional parts produced according to the first production control scheme and the second production control scheme;

[0112] Analyze the production qualification rate of the current batch of fastening functional parts according to the production quality information, and when the production qualification rate is still lower than the preset qualification rate after implementing the first production control scheme and the second production control scheme, mark the fastening functional part production machine as a fault state;

[0113] According to the production quality information of fastening functional parts, high-frequency quality problems of fastening functional parts and high-frequency locations where quality problems occur are counted;

[0114] According to the high-frequency quality problems and the high-frequency locations where the quality problems occur, the faults of the fastening functional parts production machinery are traced to determine the fault items and fault locations, and the fastening functional parts production machinery is inspected and maintained according to the fault items and fault locations.

[0115] It should be noted that after the implementation of the first production control plan and the second production control plan, if the production quality of the fastening functional parts is still lower than the production quality requirements, it can be considered that there is a problem with the production equipment. By analyzing the high-frequency quality problems and the high-frequency locations where the quality problems occur, tracing the faults, analyzing the possible fault items and fault locations, and performing fault inspections on the production machinery, mechanical faults can be effectively eliminated and the normal operation of the machinery can be restored, thereby improving the stability of the production process and the qualified rate of the products. After eliminating the mechanical faults, the fastening functional parts produced will be more in line with the quality standards, reduce the defect rate, and ensure that the products produced can meet the preset quality standards.

[0116] According to an embodiment of the present invention, it also includes:

[0117] Acquire production process data of fastening functional parts, and acquire standard control parameters of fastening functional parts production machinery of each production process according to the production process data;

[0118] Acquire work control data and executed workflow data of the fastening functional parts produced by the fastening functional parts production machinery within a preset time period;

[0119] Extracting standard control parameters of the executed workflow from the standard control parameters, sorting the extracted standard control parameters according to the executed workflow, and obtaining standard work control parameter data of the workflow of the fastening functional part production machinery within a preset time period;

[0120] Compare the work control data within a preset time period with the standard work control parameter data, calculate the Euclidean distance between the work control data and the standard work control parameter data, and evaluate the control difference between the work control data and the standard work control parameter data according to the Euclidean distance to obtain a control difference evaluation result;

[0121] When the control difference evaluation result is greater than the preset difference value, the semi-finished fastening functional parts produced at this time will be marked as defective, the defective products will be scrapped and the fastening functional parts production machinery will be controlled not to carry out subsequent production steps.

[0122] It should be noted that by comparing the Euclidean distance between the actual working control data and the standard control parameter data, the accuracy and consistency of the production process can be monitored in real time. When the difference is greater than the preset value, it can be considered that the quality of the fastening functional parts produced by the fastening functional parts production machinery within the preset time period does not meet the standards. This is because the fastening functional parts have not yet been completed and are semi-finished products. When the semi-finished products do not meet the standards, the semi-finished products can be immediately marked as defective and scrapped when the production process deviates from the standards. This can effectively prevent unqualified products from flowing into the next production stage, thereby reducing the risk of producing unqualified products in the production process, and by stopping production immediately when control differences are found, the waste of resources and materials required to produce unqualified products is reduced. Scrapping defective products and preventing them from being further processed can reduce additional production costs and resource waste caused by quality problems, thereby reducing overall production costs.

[0123] Figure 4 A block diagram of a production control optimization system for fastening functional parts of the present invention is shown.

[0124] The second aspect of the present invention further provides a production control optimization system 4 for fastening functional parts, the system comprising: a memory 41, a processor 42, the memory comprising a production control optimization method program for fastening functional parts, and when the production control optimization method program for fastening functional parts is executed by the processor, the following steps are implemented:

[0125] Acquire production samples of the fastening functional parts in a preset time period, and perform heat treatment on the production samples to draw a thermal distribution image of the production samples;

[0126] Calculating the spatial smoothness of the thermal distribution image, performing quality assessment on the production sample according to the spatial smoothness, and obtaining quality change data of the production sample within a preset time period;

[0127] Obtaining oxidation degree change data of the molten material within a preset time period and temperature drop rate change data during the solidification process of the production sample, performing correlation analysis on the quality change data, the oxidation degree change data, and the temperature drop rate change data, determining the influence of the oxidation degree and temperature drop change of the molten material on the quality of the production sample, and obtaining oxidation-quality influence data and temperature-quality influence data;

[0128] Determine the preparation amount and preparation time of the molten material according to the oxidation-quality impact data and the production demand data of the current batch of fastening functional parts to obtain a first production control plan;

[0129] Controlling the temperature drop rate of the fastening functional component during the curing process according to the temperature-quality influence data to obtain a second production control plan;

[0130] The production of the fastening functional parts is controlled according to the first production control scheme and the second production control scheme.

[0131] The present invention discloses a production control optimization method and system for fastening functional parts. The thermal distribution image of the production sample is obtained through heat treatment, the quality change is evaluated, and the comprehensive impact on the quality is analyzed by combining the data of the degree of oxidation of the molten material and the temperature drop rate. Based on this, the preparation amount and time of the molten material are dynamically adjusted to form a first production control plan; at the same time, the temperature drop rate of the curing process is accurately controlled to formulate a second control plan. The present invention effectively integrates the key parameters in the production process, realizes the accurate prediction and control of the production quality of fastening functional parts, and significantly improves the production efficiency and product quality stability.

[0132] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0133] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0134] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0135] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0136] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0137] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for optimizing production control of fastening functional parts, characterized in that: The following steps are involved: Acquire production samples of the fastening functional parts in a preset time period, and perform heat treatment on the production samples to draw a thermal distribution image of the production samples; Calculating the spatial domain smoothness of the thermal distribution image, performing quality assessment on the production sample according to the spatial domain smoothness, and obtaining quality change data of the production sample within a preset time period; Obtaining oxidation degree change data of the molten material within a preset time period and temperature drop rate change data during the solidification process of the production sample, performing correlation analysis on the quality change data, the oxidation degree change data, and the temperature drop rate change data, determining the influence of the oxidation degree and temperature drop change of the molten material on the quality of the production sample, and obtaining oxidation-quality influence data and temperature-quality influence data; Determine the preparation amount and preparation time of the molten material according to the oxidation-quality impact data and the production demand data of the current batch of fastening functional parts to obtain a first production control plan; Controlling the temperature drop rate of the fastening functional component during the curing process according to the temperature-quality influence data; Controlling the production of the fastening functional parts according to the first production control scheme and the second production control scheme; The temperature drop rate of the fastening functional part during the curing process is controlled according to the temperature-quality influence data to obtain a second production control scheme, which is specifically: Determining the effect of the temperature drop rate on the degree of shrinkage during the curing process of the fastening functional component according to the temperature-quality influence data; Determine the temperature drop rate range in which the fastening functional part will not cause shrinkage of the production sample during the curing process according to the influence of the temperature drop rate on the shrinkage degree of the fastening functional part during the curing process; The temperature drop rate of the fastening functional parts during the curing process is controlled according to the temperature drop rate interval to obtain a second production control scheme.

2. A method for optimizing production control of fastening functional parts according to claim 1, characterized in that: The obtaining of production samples of the fastening functional parts in a preset time period and heat treatment of the production samples to draw a thermal distribution image of the production samples is specifically as follows: Acquire production samples of the same batch of fastening functional parts within a preset time period according to a preset time period, and heat the production samples; The appearance temperature distribution of the production sample is obtained based on the infrared thermal imaging device, and the appearance temperature distribution is visualized to obtain a thermal distribution image of the production sample.

3. The method for optimizing production control of fastening functional parts according to claim 1, characterized in that: The calculating of the spatial smoothness of the thermal distribution image, and performing quality assessment on the production sample according to the spatial smoothness, to obtain quality change data of the production sample within a preset time period, is specifically: The thermal distribution image is converted into a grayscale image, and the gradient of each pixel in the grayscale image in the x direction and the y direction is calculated by a Sobel operator to obtain the horizontal gradient and the vertical gradient of the grayscale image; Calculating the gradient amplitude of each pixel of the grayscale image according to the horizontal gradient and the vertical gradient, and calculating the average gradient amplitude and the gradient amplitude standard deviation of the grayscale image according to the gradient amplitude of each pixel; Determine the spatial smoothness of the thermal distribution image according to the average gradient amplitude and the gradient amplitude standard deviation, mark the area where the spatial smoothness is less than a preset value, and obtain the area to be evaluated for quality; Acquire spatial smoothness features formed by different defect types of the fastening functional part, wherein the spatial smoothness features include smoothness reduction degree features, smoothness reduction range features, and shape features of the smoothness reduction range; A defect type recognition model is constructed based on a convolutional neural network, and the spatial smoothness features formed by different defect types of the fastening functional parts are introduced into the defect type recognition model for learning and training; Extracting the spatial smoothness features of the area to be evaluated for quality, importing the spatial smoothness features of the area to be evaluated for quality into the trained defect type recognition model to perform defect type recognition, and obtaining a defect type recognition result of the production sample; Determine the defect range and defect quantity of each production sample according to the defect type identification result and the spatial smoothness characteristics of the area to be quality evaluated, and perform quality evaluation on the production sample according to the defect range and defect quantity to obtain a first quality evaluation result of each production sample; Obtaining standard size information of the fastening functional part and size information of the production sample, comparing the standard size information with the size information, evaluating the production size accuracy of the production sample, and obtaining a second quality evaluation result The first quality assessment result and the second quality assessment result are sorted according to the acquisition time of the production sample to obtain quality change data of the production sample within a preset time period, and the quality change data includes the first quality change data and the second quality change data.

4. A method for optimizing production control of fastening functional parts according to claim 3, characterized in that: The method obtains the oxidation degree change data of the molten material within a preset time period and the temperature drop rate change data during the solidification process of the production sample, performs correlation analysis on the quality change data, the oxidation degree change data, and the temperature drop change data, determines the influence of the oxidation degree and temperature drop change of the molten material on the quality of the production sample, and obtains the oxidation-quality influence data and the temperature-quality influence data, which are specifically: Acquiring atomic absorption spectrum change data of the molten material of the fastening functional component within a preset time period, performing material analysis on the atomic absorption spectrum change data, and determining material change data of the molten material within the preset time period; Determining the change in oxidation degree of the molten material within a preset time period according to the material change data to obtain oxidation degree change data; Acquire temperature drop change data of the molten material of the production sample during the solidification process of the molten material, and determine temperature drop rate change data of the production sample within a preset time period according to the temperature drop change data; Sorting the first mass change data, the second mass change data, the oxidation degree change data, and the temperature drop rate change data in time series to determine the first mass, the second mass, the oxidation degree, and the temperature drop rate at each time node; The first mass and oxidation degree at each time point are used to construct a first data matrix, and the second mass and temperature drop rate at each time node are used to construct a second data matrix; Performing data normalization processing on the first data matrix and the second data matrix to obtain a standardized first data matrix and a standardized second data matrix, calculating a covariance matrix of each data matrix, and performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors of each covariance matrix; Select the eigenvectors corresponding to the first two largest eigenvalues ​​to form the first principal component and the second principal component, calculate the loadings of the first principal component and the second principal component, and construct the first loading matrix and the second loading matrix; The first loading matrix and the second loading matrix are analyzed to determine the influence of the oxidation degree of the molten material on the first mass of the production sample and the influence of the temperature drop rate of the molten material on the second mass of the production sample, and obtain oxidation-quality influence data and temperature-quality influence data. The oxidation-quality influence data and temperature-quality influence data include correlation data on the influence of the oxidation degree of the molten material on the first mass of the production sample and the temperature drop rate of the molten material on the second mass of the production sample, influence degree data, and production quality data of the production samples corresponding to different oxidation degrees and temperature drop rates.

5. The method for optimizing production control of fastening functional parts according to claim 1, characterized in that: The preparation amount and preparation time of the molten material are determined according to the oxidation-quality impact data and the production demand data of the current batch of fastening functional parts to obtain a first production control plan, which is specifically: Acquire production demand data of the current batch of fastening functional parts, wherein the production demand data includes a first quality requirement and a production quantity requirement of the fastening functional parts; Obtaining production process standard data of fastening functional parts and production capacity data of fastening functional parts production machinery, determining the production volume of fastening functional parts per unit time and the amount of molten material used for a single fastening functional part according to the production process standard data and production capacity data, and obtaining the production capacity data of the fastening functional parts production machinery; Calculate the production time required for the fastening functional parts production machinery to complete the production quantity requirement of the current batch of fastening functional parts according to the production quantity requirement of the current batch of fastening functional parts and the production capacity data of the fastening functional parts production machinery, and mark it as the first time threshold; The first quality requirement of the current batch of fastening functional parts is calibrated as the minimum production quality threshold, and the oxidation degree of the molten material when the fastening functional parts reach the minimum production quality threshold during the production process is calculated according to the oxidation-quality impact data, and the time required for the molten material to reach the oxidation degree is calculated and calibrated as the second time threshold; When the first time threshold is less than the second time threshold, the preparation amount of the molten material is calculated according to the production quantity requirement and the usage amount of the molten material of a single fastening functional component; When the first time threshold is greater than the second time threshold, the total preparation amount of the molten material is calculated according to the production quantity demand and the molten material usage of a single fastening functional part, the total production amount of the fastening functional parts when the production time of the fastening functional part production machinery reaches the first time threshold is calculated according to the production capacity data of the fastening functional part production machinery, the preparation amount of the molten material when the first time threshold is reached is calculated according to the total production amount and the molten material usage of a single fastening functional part, and the single preparation amount of the first N times of molten material preparation when producing the current batch of fastening functional parts is obtained, the total preparation amount is divided by the single preparation amount and the remainder is taken, the quotient obtained is the N value, and the remainder is the last molten material preparation amount, the preparation time of the next molten material is determined according to the production time of the single preparation amount of the molten material, and the first production control plan for the fastening functional parts is obtained.

6. A production control optimization system for fastening functional parts, characterized in that: The production control optimization system for fastening functional parts includes a storage device and a processor. The storage device includes a production control optimization method program for fastening functional parts. When the production control optimization method program for fastening functional parts is executed by the processor, the following steps are implemented: Acquire production samples of the fastening functional parts in a preset time period, and perform heat treatment on the production samples to draw a thermal distribution image of the production samples; Calculating the spatial domain smoothness of the thermal distribution image, performing quality assessment on the production sample according to the spatial domain smoothness, and obtaining quality change data of the production sample within a preset time period; Obtaining oxidation degree change data of the molten material within a preset time period and temperature drop rate change data during the solidification process of the production sample, performing correlation analysis on the quality change data, the oxidation degree change data, and the temperature drop rate change data, determining the influence of the oxidation degree and temperature drop change of the molten material on the quality of the production sample, and obtaining oxidation-quality influence data and temperature-quality influence data; Determine the preparation amount and preparation time of the molten material according to the oxidation-quality impact data and the production demand data of the current batch of fastening functional parts to obtain a first production control plan; Controlling the temperature drop rate of the fastening functional component during the curing process according to the temperature-quality influence data; Controlling the production of the fastening functional parts according to the first production control scheme and the second production control scheme; The temperature drop rate of the fastening functional part during the curing process is controlled according to the temperature-quality influence data to obtain a second production control scheme, which is specifically: Determining the effect of the temperature drop rate on the degree of shrinkage during the curing process of the fastening functional component according to the temperature-quality influence data; Determine the temperature drop rate range in which the fastening functional part will not cause shrinkage of the production sample during the curing process according to the influence of the temperature drop rate on the shrinkage degree of the fastening functional part during the curing process; The temperature drop rate of the fastening functional parts during the curing process is controlled according to the temperature drop rate interval to obtain a second production control scheme.

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