Dynamic detection method and device combined with sub-runner product quality analysis

Abstract

The invention provides a dynamic detection method and device combined with sub-runner product quality analysis, and relates to the technical field of automatic detection.The method comprises the steps that K positioning serial number identifiers are generated; obtaining K sub-runner product quality inspection information sets, and performing same-index deviation identification on the K sub-runner product quality inspection information sets and the product quality standard information to generate K sub-runner product quality inspection deviation sets; obtaining K first detection coefficients; k sub-runner width intervals are obtained; building a runner extraction wheel disc based on the K first detection coefficients, and determining a first automatic detection scheme according to an extraction result; and the first automatic detection scheme is input into an automatic detection unit of the target injection molding production line for automatic product detection. According to the invention, the technical problems of low production efficiency and resource waste caused by unbalanced quality monitoring of the sub-runner in the prior art can be solved, and the technical effects of improving the production efficiency and reducing the resource waste are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic detection, and particularly relates to a dynamic detection method and device combining product quality analysis of a runner. BACKGROUND

[0002] Plastic mechanical forming performs a plastic processing procedure through injection molding, and is used for manufacturing a large number of identical plastic products, and has a wide application in the automobile, electronic, medical, packaging and consumer product industries.

[0003] At present, in the existing plastic mechanical forming process, the products of some runners are strictly monitored due to frequent detection, while the products of other runners have quality risks due to lack of detection, so that continuous detection of some runners will interfere with the normal production process, reduce the production efficiency, and even excessive detection of the runners may waste detection resources such as manpower, time and equipment and other required resources. Therefore, there is a need for a method to solve the above problems.

[0004] In summary, in the prior art, there is a technical problem that the imbalance of quality monitoring of the runners leads to reduced production efficiency and wasted resources. SUMMARY

[0005] The purpose of the present application is to provide a dynamic detection method and device combining product quality analysis of a runner, so as to solve the technical problem in the prior art that the imbalance of quality monitoring of the runners leads to reduced production efficiency and wasted resources.

[0006] In view of the above problems, the present application provides a dynamic detection method and device combining product quality analysis of a runner.

[0007] In a first aspect, the application provides a dynamic detection method combined with a runner product quality analysis, which is realized by a dynamic detection device combined with a runner product quality analysis, wherein the method comprises: according to the relative positions of K runners and a main runner in a target injection molding production line, performing serial number identification on the K runners to generate K positioning serial number identifications; respectively performing quality inspection information retrieval on products produced by the K runners within a historical window to obtain K runner product quality inspection information sets; performing the same index deviation identification on the K runner product quality inspection information sets and product quality standard information to generate K runner product quality inspection deviation sets, wherein the K runner product quality inspection deviation sets comprise K runner product quality inspection qualified deviation sets and K runner product quality inspection unqualified deviation sets; respectively counting the number of deviations in the K runner product quality inspection unqualified deviation sets and comparing the counting results with the number of deviations in the K runner product quality inspection deviation sets, and taking the results as K first detection coefficients; performing tolerance interval identification on the K runner product quality inspection qualified deviation sets to obtain K runner tolerance intervals; constructing a runner extraction wheel based on the sizes of the K first detection coefficients, performing multiple extractions on the runner extraction wheel according to a preset product sampling number, and determining a first automatic detection scheme according to the extraction results, wherein the first automatic detection scheme comprises M first sampling positioning serial number identifications, M first runner sampling numbers, and M runner tolerance intervals; and inputting the first automatic detection scheme into an automatic detection unit of the target injection molding production line at a first time node to perform product automatic detection.

[0008] In a second aspect, the application further provides a dynamic detection device for combined runner product quality analysis, for performing the dynamic detection method for combined runner product quality analysis as described in the first aspect, wherein the device comprises: a positioning serial number identification generation module, configured to serially identify K runners according to the relative positions of the K runners and the main runner in a target injection molding production line, and generate K positioning serial number identifications; a runner product quality inspection information set obtaining module, configured to respectively call quality inspection information of products produced by the K runners within a historical window, and obtain K runner product quality inspection information sets; a runner product quality inspection deviation set generation module, configured to identify deviations of the K runner product quality inspection information sets and product quality standard information according to the same index, and generate K runner product quality inspection deviation sets, wherein the K runner product quality inspection deviation sets comprise K runner product quality inspection qualified deviation sets and K runner product quality inspection unqualified deviation sets; a first detection coefficient obtaining module, configured to respectively count the number of deviations in the K runner product quality inspection unqualified deviation sets, compare the counting results with the number of deviations in the K runner product quality inspection deviation sets, and take the comparison results as K first detection coefficients; a runner tolerance interval obtaining module, configured to traverse the K runner product quality inspection qualified deviation sets to identify tolerance intervals, and obtain K runner tolerance intervals; a first automatic detection scheme determination module, configured to construct a runner extraction wheel based on the sizes of the K first detection coefficients, extract the runner extraction wheel multiple times according to a preset product sampling quantity, and determine a first automatic detection scheme according to the extraction results, wherein the first automatic detection scheme comprises M first sampling positioning serial number identifications, M first runner sampling quantities, and M runner tolerance intervals; and an automatic detection module, configured to input the first automatic detection scheme into an automatic detection unit of the target injection molding production line at a first time node to perform product automatic detection.

[0009] One or more technical solutions provided in the application have at least the following technical effects or advantages: The K sub-runners are sequentially numbered by identifying the relative positions of the K sub-runners and the main runner in the target injection molding production line, K positioning serial numbers are generated; product quality inspection information of the K sub-runners produced in a historical window is collected respectively, K sub-runner product quality inspection information sets are obtained; the K sub-runner product quality inspection information sets and product quality standard information are identified for the same index deviation, K sub-runner product quality inspection deviation sets are generated, wherein the K sub-runner product quality inspection deviation sets include K sub-runner product quality inspection qualified deviation sets and K sub-runner product quality inspection unqualified deviation sets; the number of deviations in the K sub-runner product quality inspection unqualified deviation sets is counted respectively, and the counting result is compared with the number of deviations in the K sub-runner product quality inspection deviation sets, and the result is taken as K first detection coefficients; the K sub-runner product quality inspection qualified deviation sets are traversed to identify the tolerance interval, and K sub-runner tolerance intervals are obtained; a sub-runner extraction wheel is constructed based on the size of the K first detection coefficients, the sub-runner extraction wheel is extracted multiple times according to a preset product sampling number, and a first automatic detection scheme is determined according to the extraction result, wherein the first automatic detection scheme includes M first sampling positioning serial numbers, M first sub-runner sampling numbers and M sub-runner tolerance intervals; the first automatic detection scheme is input into the automatic detection unit of the target injection molding production line at a first time node to perform product automatic detection, and the technical goal of balanced monitoring of plastic mechanical forming quality is achieved, and the technical effect of improving production efficiency and reducing resource waste is achieved.

[0010] The above description is only a summary of the technical solutions of the present application. In order to enable the technical means of the present application to be more clearly understood, and to be implemented according to the content of the specification, and in order to enable the above and other purposes, features and advantages of the present application to be more apparent and easy to understand, the following specific embodiments of the present application are described. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and those skilled in the art can obtain other drawings without creating laborious work based on the provided drawings.

[0012] Figure 1 The flowchart of the dynamic detection method of the sub-runner product quality analysis combined with the present application; Figure 2This is a schematic diagram of the dynamic detection device for combining product quality analysis of the diversion channel in this application.

[0013] Explanation of reference numerals in the attached figures: The module includes: a positioning serial number generation module 11; a branch channel product quality inspection information set acquisition module 12; a branch channel product quality inspection deviation set generation module 13; a first detection coefficient acquisition module 14; a branch channel tolerance range acquisition module 15; a first automatic detection scheme determination module 16; and an automatic detection module 17. Detailed Implementation

[0014] This application provides a dynamic detection method and apparatus that combines product quality analysis in the flow channel, solving the technical problem in the prior art where unbalanced quality monitoring in the flow channel leads to reduced production efficiency and resource waste. It achieves the technical effect of improving production efficiency and reducing resource waste.

[0015] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them. Example

[0016] Please see the appendix Figure 1 This application provides a dynamic detection method combining flow channel product quality analysis, wherein the method is applied to a dynamic detection device combining flow channel product quality analysis, and the method specifically includes the following steps: Step 1: Based on the relative positions of the K sub-runners and the main runner in the target injection molding production line, assign serial numbers to the K sub-runners to generate K positioning serial numbers.

[0017] Specifically, the target injection molding production line is the production line for the target injection molded product. The runner is the flow channel device in the injection mold, used to guide molten plastic from the injection molding machine nozzle to various cavities. The relative positions of K branch runners to the main runner in the target injection molding production line are determined, and each branch runner is assigned a serial number, forming K positioning serial numbers. For example, the identification can be based on the physical location of the branch runners on the production line or according to the logical sequence of the production flow. At least one branch runner exists; therefore, K is an integer greater than or equal to 1.

[0018] Step 2: Retrieve the quality inspection information of the products produced in the historical window for each of the K distribution channels to obtain a set of quality inspection information for the products of the K distribution channels.

[0019] Specifically, for each distribution channel, product quality inspection information produced within a historical window is retrieved. For example, a historical window can be set according to production needs, such as one week or one month. The collected information includes quality inspection data such as product dimensions, appearance defects, weight, and color. The quality inspection data is then categorized according to the distribution channel number, forming K sets of product quality inspection information for each distribution channel.

[0020] Step 3: Identify the deviations of the K branch channel product quality inspection information sets with the product quality standard information to generate K branch channel product quality inspection deviation sets, wherein the K branch channel product quality inspection deviation sets include K branch channel product quality inspection qualified deviation sets and K branch channel product quality inspection unqualified deviation sets.

[0021] Specifically, the product quality inspection information set for each distribution channel is compared with the product quality standard information, and deviations are identified for the same quality inspection indicators. For each quality inspection indicator of each product, the deviation between the actual measured value and the standard value is calculated. These deviations are categorized according to the distribution channel, generating K distribution channel product quality inspection deviation sets. Each set contains deviation information for all quality inspection indicators of the products in the corresponding distribution channel. Within the K distribution channel product quality inspection deviation sets, based on preset acceptance standards, the deviations of each product's quality inspection indicators are divided into acceptable deviations and unacceptable deviations. Acceptable deviations are aggregated into the K distribution channel product quality inspection acceptable deviation sets, while unacceptable deviations are aggregated into the K distribution channel product quality inspection unacceptable deviation sets.

[0022] Step 4: Count the number of deviations in the K sets of defective product quality inspections for each branch channel, and compare the statistical results with the number of deviations in the K sets of defective product quality inspections. Use the results as the K first detection coefficients.

[0023] Specifically, for each sub-channel's product quality inspection non-conforming deviation set, the number of non-conforming deviations is counted to determine the frequency and severity of quality problems occurring in each sub-channel during production. The number of deviations in each sub-channel's product quality inspection non-conforming deviation set is compared to the total number of deviations in the corresponding sub-channel's product quality inspection deviation set. The ratio of the number of non-conforming deviations to the total number of deviations is calculated and used as the first detection coefficient, providing a quality control indicator for each sub-channel and reflecting the proportion of non-conforming products in the total product output.

[0024] Step 5: Traverse the set of qualified deviations of the K branch channel products to identify the tolerance intervals and obtain the K branch channel tolerance intervals.

[0025] Specifically, tolerance ranges are identified for each of the K sub-channel product quality inspection acceptable deviation sets to determine the acceptable quality deviation range for each sub-channel, i.e., the tolerance range, which is used for subsequent product quality assessment. Given the consistency of products across different sub-channels, there is a certain risk when the quality inspection results for products in the same sub-channel differ from the general trend.

[0026] Step 6: Construct a flow channel extraction wheel based on the magnitude of the K first detection coefficients, and perform multiple extractions on the flow channel extraction wheel according to the preset product sampling quantity. Determine a first automatic detection scheme based on the extraction results. The first automatic detection scheme includes M first sampling positioning sequence numbers, M first flow channel sampling quantities, and M flow channel tolerance intervals.

[0027] Specifically, a sorting wheel is constructed based on the magnitudes of K first detection coefficients. Each first detection coefficient represents the rejection rate of a sorting channel; channels with higher rejection rates should have a greater probability of being sampled on the wheel. The constructed sorting wheel is used multiple times according to a preset product sampling quantity. Each sampling randomly selects a sorting channel and records the sampling location number. Based on the sampling results, a first automatic inspection scheme is determined, including M first sampling location numbers, M first sorting channel sampling quantities, and M sorting channel tolerance ranges. This first automatic inspection scheme guides product quality sampling activities on the production line.

[0028] Step 7: At the first time point, input the first automatic detection scheme into the automatic detection unit of the target injection molding production line for automatic product detection.

[0029] Specifically, at the first time point, the first automatic detection plan is input into the automatic detection unit of the target injection molding production line. The automatic detection unit will automatically detect the product according to the plan to ensure that the quality of the product meets the preset standards during the production process.

[0030] The dynamic detection method combining runner product quality analysis is applied to the dynamic detection device combining runner product quality analysis, which can achieve the technical goal of balanced monitoring of plastic mechanical molding quality, thereby improving production efficiency and reducing resource waste.

[0031] Furthermore, this application also includes: The difference between the K sets of quality inspection information of the sub-channel products and the product quality standard information is calculated to generate K sets of sub-channel product index deviations; according to the preset index deviation value threshold set, the K sets of sub-channel product index deviation values ​​are identified as qualified, and the K sets of qualified deviations and K sets of unqualified deviations of sub-channel products are obtained based on the identification results.

[0032] Specifically, for each set of quality inspection information of the sub-runner products, it is compared with the predetermined product quality standard information, and the difference between the actual measured value and the standard value of each product quality index is calculated, representing the deviation between the product index and the standard. Through repeated iteration, K sets of sub-runner product index deviations are generated, including all index deviation values of the corresponding sub-runner products.

[0033] Then, according to the preset set of threshold values of index deviations, each set of sub-runner product index deviations is evaluated. The preset set of threshold values of index deviations refers to the set of ranges of allowable deviation values for different indexes. Each product quality index deviation value is compared with the corresponding threshold value. If the deviation value is within the threshold range, the index is considered qualified; if the deviation value exceeds the threshold range, the index is considered unqualified. The products of each sub-runner are identified as qualified or unqualified. According to the results of the qualified identification, the products of each sub-runner are divided into a set of qualified quality inspection deviations and a set of unqualified quality inspection deviations. The K sets of sub-runner product qualified quality inspection deviations contain all product information with deviation values within the threshold range. The K sets of sub-runner product unqualified quality inspection deviations contain all product information with deviation values exceeding the threshold range.

[0034] By means of difference calculation and qualified identification, the product quality of plastic machinery forming is effectively monitored, problems are discovered and solved in a timely manner, thereby improving production efficiency and product quality.

[0035] Furthermore, this application also includes: Calculate the mean value of each of the K sets of sub-runner product qualified quality inspection deviations to generate K sub-runner product qualified quality inspection deviation means; construct K first tolerance regions based on the K sub-runner product qualified quality inspection deviation means and a preset tolerance step length, and calculate the K first region densities of the K first tolerance regions; starting from the edges of the K first tolerance regions, expand the K first tolerance regions according to the preset tolerance step length to obtain K second tolerance regions; calculate the K second region densities of the K second tolerance regions; make a size judgment based on the K first region densities and the K second region densities, and perform tolerance interval identification according to the judgment result to obtain the K sub-runner tolerance intervals.

[0036] Specifically, for each set of sub-runner product qualified quality inspection deviations, calculate the average value of all qualified deviations to generate K sub-runner product qualified quality inspection deviation means, representing the average deviation level within the qualified range of each sub-runner.

[0037] Then, based on the average quality inspection deviation of each product in each branch channel and the preset tolerance step size, a first tolerance region is constructed for each branch channel. The tolerance step size is determined according to quality standards and production requirements, and is used to determine the width of the tolerance region. The first tolerance region is an area that expands to both sides with the average quality inspection deviation of the K branch channel products as the center, i.e., the average deviation as the center, and the preset tolerance step size as the radius. For the first tolerance region of each branch channel, the density of qualified products in the region is calculated by counting the number of qualified products in the region, i.e., the density of the first region.

[0038] Next, starting from the edge of the first tolerance region of each branch channel, the system expands outwards to both sides according to a preset tolerance step size to obtain K second tolerance regions. The second tolerance regions are wider than the first tolerance regions and include additional areas beyond the first tolerance regions, allowing for more lenient quality assessment.

[0039] Next, for the second tolerance zone of each branch channel, the density of qualified products in that zone is calculated by counting the number of qualified products in the zone, i.e., the density of the second zone.

[0040] Then, the density of the first and second zones in each flow channel is used to determine the magnitude of the deviation. If the density of the first zone is higher than that of the second zone, it indicates that the product quality is concentrated within a smaller deviation range, suggesting more stable quality. If the density of the second zone is higher than that of the first zone, it indicates that more products have quality deviations distributed over a wider area, requiring attention to potential quality issues. Based on the product consistency across different flow channels, there is a certain risk when the quality inspection results for products in the same flow channel differ from the general trend. By obtaining the tolerance ranges for K flow channels, production parameters can be adjusted, processes optimized, or equipment maintenance performed to improve the consistency and stability of product quality.

[0041] By setting tolerance ranges, reasonable product quality standards can be determined, ensuring a balance between production efficiency and product quality.

[0042] Furthermore, this application also includes: When the density of the K first regions is less than or equal to the density of the K second regions, the K second tolerance regions continue to be diffused according to the preset tolerance step size, and tolerance intervals are identified based on the diffusion results, until the gain of the region density is less than or equal to the preset gain, at which point diffusion stops, and the K Nth tolerance regions are taken as the K target tolerance regions; the maximum and minimum quality inspection deviations of the branch channel products in the K target tolerance regions are used as the endpoint values ​​of the branch channel tolerance intervals to generate the K branch channel tolerance intervals.

[0043] Specifically, the density of the first and second regions in each distribution channel is compared. If the density of the first region is less than or equal to the density of the second region, it indicates that there are more qualified products within a wider deviation range. When the density of the first region is less than or equal to the density of the second region, diffusion continues in the second tolerance region according to a preset tolerance step size to obtain a suitable tolerance range, making the distribution of product quality more reasonable. During the diffusion process, the gain of region density is monitored, that is, the amount of increase in density after each diffusion. If the density gain is less than or equal to a preset gain threshold, it indicates that further diffusion will not significantly increase the number of qualified products, so diffusion can be stopped. After diffusion stops, the current K Nth tolerance regions are taken as the K target tolerance regions, representing the acceptable range of product quality deviation under the quality standard.

[0044] Then, based on the maximum and minimum values ​​of the acceptable deviation of the product quality inspection in the target tolerance region, the endpoint values ​​of the tolerance intervals for each runner are determined, representing the acceptable deviation range of product quality in each runner, i.e., K runner tolerance intervals. After generating the runner tolerance intervals, the production process is adjusted according to the tolerance intervals to optimize the quality control strategy.

[0045] By setting a tolerance range, the fluctuation range of product quality can be obtained, and corresponding measures can be taken to improve the consistency and reliability of product quality.

[0046] Furthermore, this application also includes: Calculate the ratio of each of the K first detection coefficients to the sum of the K first detection coefficients, and use the calculation results as K wheel probability coefficients; construct the channel extraction wheel based on the K wheel probability coefficients; perform multiple extractions on the channel extraction wheel according to a preset product extraction quantity to obtain multiple extraction results, wherein each extraction result includes an inspection positioning sequence number identifier; perform cluster analysis on the multiple extraction results based on the positioning sequence number identifier to generate M first inspection positioning sequence number identifiers and M first channel extraction quantities; match the M first inspection positioning sequence number identifiers with the K channel tolerance intervals to generate M channel tolerance intervals.

[0047] Specifically, for the first detection coefficient of each branch channel, the ratio of the first detection coefficient to the sum of all first detection coefficients is calculated, which represents the contribution of each branch channel to the total defect rate, and is called the K wheel probability coefficients.

[0048] Then, the calculated K wheel probability coefficients are used to construct the sorting wheel. The proportion of each sorting wheel on the wheel is proportional to its wheel probability coefficient, ensuring that sorting wheels with higher failure rates have a higher sampling probability.

[0049] Next, based on the preset product extraction quantity, the constructed distribution channel extraction wheel performs multiple extractions. Each extraction randomly selects a distribution channel and records the extraction location sequence number.

[0050] Next, cluster analysis is performed on the location serial numbers from multiple sampling results to group similar sampling location serial numbers, thereby identifying the sub-channels to be sampled. The cluster analysis yields M first sampling location serial numbers and the corresponding M sampling quantities for the first sub-channels.

[0051] Then, the M first sampling location serial numbers are matched with the K branch channel tolerance intervals to determine a suitable tolerance interval for each sampling location serial number, so as to evaluate product quality during sampling. Based on the matching results, M branch channel tolerance intervals are generated to guide the sampling process and ensure that the sampled products are acceptable in quality.

[0052] By using roulette wheel probability coefficients and cluster analysis, random sampling inspections of product quality during the plastic mechanical molding process can be achieved. This ensures the targeting and effectiveness of the sampling inspections, helps to identify potential quality problems in a timely manner, and allows for corresponding measures to improve the production process.

[0053] Furthermore, this application also includes: Based on the first time node and the preset inspection change cycle, a first change node is obtained; the sampling wheel of the sub-channel is sampled again according to the preset product sampling quantity to determine the first change inspection scheme; the first automatic inspection scheme and the first change inspection scheme are subjected to transition certification. If the certification is successful, the first change inspection scheme is used as the second automatic inspection scheme of the first change node, wherein the second automatic inspection scheme includes N second sampling positioning sequence number identifiers, N second sub-channel sampling quantities, and N sub-channel tolerance intervals; the second automatic inspection scheme is input into the automatic inspection unit of the target injection molding production line for automatic product inspection.

[0054] Specifically, products produced in different flow channels have relative stability. Before conducting product testing, it is necessary to confirm whether they have been tested, so as to avoid continuously testing some products produced in one flow channel while not testing others.

[0055] Then, the preset detection change cycle refers to the time interval between two consecutive changes to the automatic detection scheme, and the time interval is preset based on factors such as production needs, equipment maintenance plans, and quality fluctuations. Based on the first time node and the preset detection change cycle, the next change node, i.e., the first change node, is calculated.

[0056] Next, upon reaching the first change node, the sampling wheel in the distribution channel is sampled again according to the preset product sampling quantity to determine the first change detection plan, so as to reflect the current production status.

[0057] Next, the transition certification is an evaluation process used to determine whether a product has been tested, thereby avoiding the continued testing of some products produced in certain flow channels while neglecting to test others. The first automated testing scheme and the first modified testing scheme are compared. If the first modified testing scheme passes the transition certification, the second automated testing scheme will be adopted as the first change node. The second automated testing scheme includes N second sampling location serial numbers, N second flow channel sampling quantities, and N flow channel tolerance intervals to guide the next stage of production sampling.

[0058] Then, the second automatic inspection plan is input into the automatic inspection unit of the target injection molding production line. The automatic inspection unit will automatically inspect the products according to the new plan, ensuring that products produced in each runner are inspected.

[0059] By periodically changing testing protocols and implementing a certification process, we ensure that every product produced in each branch line is tested, while avoiding duplicate testing. This improves the effectiveness of quality control and production efficiency, helps to identify and resolve changes in the production process in a timely manner, and ensures the continuous stability of product quality.

[0060] Furthermore, this application also includes: A similarity recognizer is used to identify the similarity between the first automatic detection scheme and the first change detection scheme to obtain a first change similarity. It is then determined whether the first change similarity is less than or equal to a preset similarity. If yes, the authentication is successful; otherwise, the authentication fails, and the sampling wheel of the distribution channel is re-sampled according to the preset product sampling quantity to determine a second change detection scheme. A transition authentication is performed between the second change detection scheme and the first automatic detection scheme. If the authentication is successful, the second change detection scheme is used as the second automatic detection scheme for the first change node.

[0061] Specifically, a similarity recognizer is used to identify the similarity between the first automatic detection scheme and the first change detection scheme. Based on multiple parameters such as the sampling location sequence number, the number of sampling points in the branch channel, and the tolerance range, the similarity recognizer analyzes the similarity between the two schemes. The result of the similarity recognition is used as the first change similarity, representing the degree of similarity between the two schemes.

[0062] Then, it is determined whether the first change similarity is less than or equal to the preset similarity. If the similarity is less than or equal to the preset similarity, it means that the first change detection plan and the first automatic detection plan have sufficient differences, and the certification passes. If the similarity is greater than the preset similarity, it means that the first change detection plan and the first automatic detection plan are not significantly different, and the certification fails. If the certification fails, it is necessary to re-sample the distribution channel sampling wheel according to the preset product sampling quantity.

[0063] Next, a transition certification is performed between the second change detection plan and the first automatic detection plan. If the second change detection plan passes the transition certification, meaning the product has not been tested, then the second change detection plan is adopted as the second automatic detection plan for the first change node.

[0064] By using similarity recognition and transition authentication processes, we can avoid continuously testing some products produced in different flow channels while leaving others untested. This improves the flexibility and adaptability of the testing scheme, thereby enabling more effective control of product quality.

[0065] In summary, the dynamic detection method combining flow channel product quality analysis provided in this application has the following technical advantages: By assigning serial numbers to the K sub-runners and main runner in the target injection molding production line according to their relative positions, K positioning serial numbers are generated. Quality inspection information for products produced by each of the K sub-runners within a historical window is retrieved to obtain a set of quality inspection information for each of the K sub-runners. The quality inspection information sets of the K sub-runners are then compared with product quality standard information to identify deviations in the same indicators, generating a set of quality inspection deviations for each of the K sub-runners. This set of deviations includes a set of acceptable deviations and a set of unacceptable deviations. The number of deviations in each set of unacceptable deviations is counted, and the results are compared with the number of deviations in each set of quality inspection deviations. The results are used as K first detection coefficients; the tolerance range is identified by traversing the K sets of qualified deviations of product quality inspection in the sub-channels to obtain K sub-channel tolerance ranges; a sub-channel sampling wheel is constructed based on the magnitude of the K first detection coefficients, and the sub-channel sampling wheel is sampled multiple times according to the preset product sampling quantity. A first automatic detection scheme is determined based on the sampling results, wherein the first automatic detection scheme includes M first sampling positioning sequence numbers, M first sub-channel sampling quantities, and M sub-channel tolerance ranges; at the first time node, the first automatic detection scheme is input into the automatic detection unit of the target injection molding production line for automatic product detection, thereby achieving the technical goal of balanced monitoring of plastic mechanical molding quality and achieving the technical effects of improving production efficiency and reducing resource waste. Example

[0066] Based on the dynamic detection method combining flow channel product quality analysis in the foregoing embodiments, and with the same inventive concept, this application also provides a dynamic detection device combining flow channel product quality analysis. Please refer to the appendix. Figure 2 The device includes: The positioning sequence number generation module 11 is used to assign a sequence number to the K sub-runners based on their relative positions to the main runner in the target injection molding production line, thereby generating K positioning sequence number identifiers.

[0067] The sub-channel product quality inspection information set acquisition module 12 is used to retrieve the quality inspection information of the products produced in the historical window for the K sub-channels respectively, and obtain the K sub-channel product quality inspection information sets.

[0068] The sub-channel product quality inspection deviation set generation module 13 is used to identify the same index deviation between the K sub-channel product quality inspection information sets and the product quality standard information to generate K sub-channel product quality inspection deviation sets. The K sub-channel product quality inspection deviation sets include K sub-channel product quality inspection qualified deviation sets and K sub-channel product quality inspection unqualified deviation sets.

[0069] The first detection coefficient acquisition module 14 is used to count the number of deviations in the K sets of sub-channel product quality inspection non-conformity deviations respectively, and compare the statistical results with the number of deviations in the K sets of sub-channel product quality inspection deviations, and use the results as K first detection coefficients.

[0070] The flow channel tolerance interval acquisition module 15 is used to traverse the K sets of qualified deviations of flow channel products to identify tolerance intervals and obtain K flow channel tolerance intervals.

[0071] The first automatic detection scheme determination module 16 is used to construct a channel sampling wheel based on the magnitude of the K first detection coefficients, perform multiple samplings on the channel sampling wheel according to the preset product sampling quantity, and determine the first automatic detection scheme based on the sampling results. The first automatic detection scheme includes M first sampling positioning sequence number identifiers, M first channel sampling quantities, and M channel tolerance intervals.

[0072] Automatic detection module 17 is used to input the first automatic detection scheme into the automatic detection unit of the target injection molding production line for automatic product detection at a first time node.

[0073] Furthermore, the product quality inspection deviation set generation module 13 in the device is also used for: The difference between the K sets of quality inspection information of the sub-channel products and the product quality standard information is calculated to generate K sets of product index deviations for the sub-channel products. Based on the preset threshold set of indicator deviation values, the set of K branch channel product indicator deviation values ​​is identified as qualified, and the set of K branch channel product quality inspection qualified deviation and K branch channel product quality inspection unqualified deviation are obtained based on the identification results.

[0074] Furthermore, the flow channel tolerance range acquisition module 15 in the device is also used for: The mean value of the quality inspection pass deviation of the K branch channel products is calculated respectively to generate the mean value of the quality inspection pass deviation of the K branch channel products. Based on the average quality inspection deviation of the K branch channel products and the preset tolerance step size, K first tolerance regions are constructed, and the density of the K first tolerance regions is calculated. Starting from the edges of the K first tolerance regions, the K first tolerance regions are expanded according to the preset tolerance step size to obtain K second tolerance regions; Calculate the density of the K second regions in the K second tolerance regions; The size is determined based on the density of the K first regions and the density of the K second regions, and the tolerance range is identified based on the determination result to obtain the tolerance range of the K diversion channels.

[0075] Furthermore, the flow channel tolerance range acquisition module 15 in the device is also used for: When the density of the K first regions is less than or equal to the density of the K second regions, the K second tolerance regions continue to be diffused according to the preset tolerance step size, and tolerance intervals are identified based on the diffusion results, until the gain of the region density is less than or equal to the preset gain, at which point the diffusion stops, and the K Nth tolerance regions are taken as the K target tolerance regions. The K branch channel tolerance intervals are generated by using the maximum and minimum quality inspection deviations of the branch channel products in the K target tolerance regions as the endpoint values ​​of the branch channel tolerance intervals.

[0076] Furthermore, the first automatic detection scheme determination module 16 in the device is also used for: Calculate the ratio of each of the K first detection coefficients to the sum of the K first detection coefficients, and use the calculation results as the K roulette probability coefficients; The diversion channel extraction wheel is constructed based on the K wheel probability coefficients; The diversion channel extraction wheel is used to extract multiple times according to the preset product extraction quantity to obtain multiple extraction results. Each extraction result includes an inspection positioning sequence number identifier. Cluster analysis is performed on the multiple sampling results based on the location sequence identifier to generate M first sampling location sequence identifiers and M first diversion channel sampling quantities. The M first sampling location serial numbers are matched with the K branch channel tolerance intervals to generate M branch channel tolerance intervals.

[0077] Furthermore, the first automatic detection scheme determination module 16 in the device is also used for: The first change node is obtained based on the first time node and the preset detection change cycle; The sampling wheel of the diversion channel is used again to sample according to the preset product sampling quantity to determine the first change detection plan; The first automatic detection scheme and the first change detection scheme are subjected to transition authentication. If the authentication is successful, the first change detection scheme is used as the second automatic detection scheme for the first change node. The second automatic detection scheme includes N second sampling location sequence number identifiers, N second diversion channel sampling quantities, and N diversion channel tolerance intervals. The second automatic detection scheme is input into the automatic detection unit of the target injection molding production line for automatic product detection.

[0078] Furthermore, the first automatic detection scheme determination module 16 in the device is also used for: A similarity recognizer is used to identify the similarity between the first automatic detection scheme and the first change detection scheme to obtain a first change similarity. Determine whether the first change similarity is less than or equal to the preset similarity; if so, the authentication is successful. If not, the certification fails, and the sampling wheel of the diversion channel is re-sampled according to the preset product sampling quantity to determine the second change test plan; The second change detection scheme and the first automatic detection scheme are subjected to transition authentication. If the authentication is successful, the second change detection scheme is used as the second automatic detection scheme for the first change node.

[0079] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. The dynamic detection method and specific examples combining runner product quality analysis in the foregoing embodiment one are also applicable to the dynamic detection device combining runner product quality analysis in this embodiment. Through the foregoing detailed description of the dynamic detection method combining runner product quality analysis, those skilled in the art can clearly understand the dynamic detection device combining runner product quality analysis in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and relevant parts can be referred to in the method section.

[0080] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0081] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A dynamic detection method combining product quality analysis of the distribution channel, characterized in that, The method includes: Based on the relative positions of K sub-runners and the main runner in the target injection molding production line, the K sub-runners are numbered to generate K positioning serial numbers. The quality inspection information of the products produced in the historical window for each of the K branch channels is retrieved to obtain a set of product quality inspection information for the K branch channels. The K sets of quality inspection information for branch channel products are compared with the product quality standard information to identify deviations in the same index, thereby generating a set of K sets of quality inspection deviations for branch channel products. The set of K sets of quality inspection deviations for branch channel products includes a set of K sets of qualified deviations for branch channel products and a set of K sets of unqualified deviations for branch channel products. The number of deviations in the K sets of defective product quality inspections for each branch channel is counted, and the results are compared with the number of deviations in the K sets of defective product quality inspections for each branch channel. The results are used as the K first detection coefficients. By iterating through the set of qualified deviations of the K branch channel products, tolerance intervals are identified to obtain the K branch channel tolerance intervals; Based on the magnitude of the K first detection coefficients, a channel sampling wheel is constructed. The channel sampling wheel is sampled multiple times according to the preset product sampling quantity. A first automatic detection scheme is determined based on the sampling results. The first automatic detection scheme includes M first sampling positioning sequence numbers, M first channel sampling quantities, and M channel tolerance intervals. At the first time point, the first automatic detection scheme is input into the automatic detection unit of the target injection molding production line for automatic product detection.

2. The method as described in claim 1, characterized in that, The method involves identifying deviations in the same indicators between the K sets of quality inspection information for branch channel products and the product quality standard information to generate a set of K sets of quality inspection deviations for branch channel products. The difference between the K sets of quality inspection information of the sub-channel products and the product quality standard information is calculated to generate K sets of product index deviations for the sub-channel products. Based on the preset threshold set of indicator deviation values, the set of K branch channel product indicator deviation values ​​is identified as qualified, and the set of K branch channel product quality inspection qualified deviation and K branch channel product quality inspection unqualified deviation are obtained based on the identification results.

3. The method as described in claim 1, characterized in that, The method involves iterating through the set of acceptable deviations for the K branch channel products to identify tolerance intervals, thereby obtaining K branch channel tolerance intervals. The mean value of the quality inspection pass deviation of the K branch channel products is calculated respectively to generate the mean value of the quality inspection pass deviation of the K branch channel products. Based on the average quality inspection deviation of the K branch channel products and the preset tolerance step size, K first tolerance regions are constructed, and the density of the K first tolerance regions is calculated. Starting from the edges of the K first tolerance regions, the K first tolerance regions are expanded according to the preset tolerance step size to obtain K second tolerance regions; Calculate the density of the K second regions in the K second tolerance regions; The size is determined based on the density of the K first regions and the density of the K second regions, and the tolerance range is identified based on the determination result to obtain the tolerance range of the K diversion channels.

4. The method as described in claim 3, characterized in that, The method includes: When the density of the K first regions is less than or equal to the density of the K second regions, the K second tolerance regions continue to be diffused according to the preset tolerance step size, and tolerance intervals are identified based on the diffusion results, until the gain of the region density is less than or equal to the preset gain, at which point the diffusion stops, and the K Nth tolerance regions are taken as the K target tolerance regions. The K branch channel tolerance intervals are generated by using the maximum and minimum quality inspection deviations of the branch channel products in the K target tolerance regions as the endpoint values ​​of the branch channel tolerance intervals.

5. The method as described in claim 1, characterized in that, A flow channel extraction wheel is constructed based on the magnitude of the K first detection coefficients. Multiple extractions are performed on the flow channel extraction wheel according to a preset product sampling quantity. A first automatic detection scheme is determined based on the extraction results. The method includes: Calculate the ratio of each of the K first detection coefficients to the sum of the K first detection coefficients, and use the calculation results as the K roulette probability coefficients; The diversion channel extraction wheel is constructed based on the K wheel probability coefficients; The diversion channel extraction wheel is used to extract multiple times according to the preset product extraction quantity to obtain multiple extraction results. Each extraction result includes an inspection positioning sequence number identifier. Cluster analysis is performed on the multiple sampling results based on the location sequence identifier to generate M first sampling location sequence identifiers and M first diversion channel sampling quantities. The M first sampling location serial numbers are matched with the K branch channel tolerance intervals to generate M branch channel tolerance intervals.

6. The method as described in claim 1, characterized in that, The method includes: The first change node is obtained based on the first time node and the preset detection change cycle; The sampling wheel of the diversion channel is used again to sample according to the preset product sampling quantity to determine the first change detection plan; The first automatic detection scheme and the first change detection scheme are subjected to transition authentication. If the authentication is successful, the first change detection scheme is used as the second automatic detection scheme for the first change node. The second automatic detection scheme includes N second sampling location sequence number identifiers, N second diversion channel sampling quantities, and N diversion channel tolerance intervals. The second automatic detection scheme is input into the automatic detection unit of the target injection molding production line for automatic product detection.

7. The method as described in claim 6, characterized in that, The method includes: A similarity recognizer is used to identify the similarity between the first automatic detection scheme and the first change detection scheme to obtain a first change similarity. Determine whether the first change similarity is less than or equal to the preset similarity; if so, the authentication is successful. If not, the certification fails, and the sampling wheel of the diversion channel is re-sampled according to the preset product sampling quantity to determine the second change test plan; The second change detection scheme and the first automatic detection scheme are subjected to transition authentication. If the authentication is successful, the second change detection scheme is used as the second automatic detection scheme for the first change node.

8. A dynamic detection device for product quality analysis combined with a distribution channel, characterized in that, The apparatus for carrying out the method according to any one of claims 1 to 7, comprising: The positioning sequence number generation module is used to assign a sequence number to the K sub-runners and generate K positioning sequence number identifiers based on the relative positions of the K sub-runners and the main runner in the target injection molding production line. The module for obtaining the quality inspection information set of the sub-channel products is used to retrieve the quality inspection information of the products produced in the historical window for each of the K sub-channels, and obtain the quality inspection information set of the K sub-channel products. The sub-channel product quality inspection deviation set generation module is used to identify the same index deviation between the K sub-channel product quality inspection information sets and the product quality standard information to generate K sub-channel product quality inspection deviation sets. The K sub-channel product quality inspection deviation sets include K sub-channel product quality inspection qualified deviation sets and K sub-channel product quality inspection unqualified deviation sets. The first detection coefficient acquisition module is used to count the number of deviations in the K sets of sub-channel product quality inspection non-conformity deviations respectively, and compare the statistical results with the number of deviations in the K sets of sub-channel product quality inspection deviations, and use the results as K first detection coefficients; The sub-channel tolerance interval acquisition module is used to traverse the K sub-channel product quality inspection qualified deviation sets to identify tolerance intervals and obtain K sub-channel tolerance intervals. The first automatic detection scheme determination module is used to construct a channel sampling wheel based on the magnitude of the K first detection coefficients, perform multiple samplings on the channel sampling wheel according to the preset product sampling quantity, and determine the first automatic detection scheme based on the sampling results. The first automatic detection scheme includes M first sampling positioning sequence number identifiers, M first channel sampling quantities, and M channel tolerance intervals. An automatic detection module is used to input the first automatic detection scheme into the automatic detection unit of the target injection molding production line for automatic product detection at a first time point.

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