Dynamic detection method and device combined with product quality analysis of a flow splitter

Abstract

The application provides a dynamic detection method and device combined with sub-flow product quality analysis, relates to the technical field of automatic detection, and comprises the following steps: generating K positioning serial number identifiers; obtaining K sub-flow product quality inspection information sets and product quality standard information for same-index deviation identification, and generating K sub-flow product quality inspection deviation sets; obtaining K first detection coefficients; obtaining K sub-flow tolerance intervals; constructing a sub-flow extraction wheel based on the sizes of the K first detection coefficients, determining a first automatic detection scheme according to an extraction result; and inputting the first automatic detection scheme into an automatic detection unit of a target injection molding production line to perform product automatic detection. Through the application, the technical problem that the production efficiency is reduced and resources are wasted due to unbalanced quality monitoring of sub-flows in the prior art can be solved, and the technical effects of improving the production efficiency and reducing 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, 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:

[0010] 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; the product quality inspection information of the K sub-runners produced in the 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 the 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 statistical results are compared with the number of deviations in the K sub-runner product quality inspection deviation sets, and the results are 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 the 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.

[0011] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the following specific embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. It should be understood that the contents described in this part are not intended to identify the key or important features of the embodiments of the present application, nor are they intended 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

[0012] 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 on the basis of the provided drawings.

[0013] Figure 1 The flowchart of the dynamic detection method of the sub-runner product quality analysis of the present application is shown;

[0014] Figure 2This is a schematic diagram of the dynamic detection device for combining product quality analysis of the diversion channel in this application.

[0015] Explanation of reference numerals in the attached figures:

[0016] 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

[0017] 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.

[0018] 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

[0019] 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:

[0020] 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.

[0021] 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.

[0022] Step two: respectively, the K product quality inspection information of the product produced in the historical window is obtained.

[0023] Specifically, for each shunt, the product quality inspection information produced in the historical window is retrieved. For example, the historical window is set according to the production demand, such as a week, a month, etc. The collected information includes product size, appearance defect, weight, color and other quality inspection data. The quality inspection data is classified according to the shunt serial number, forming K product quality inspection information sets of the shunt.

[0024] Step three: the K product quality inspection information sets of the shunt are compared with the product quality standard information to identify the same index deviation, and K product quality inspection deviation sets of the shunt are generated, wherein the K product quality inspection deviation sets of the shunt include K product quality inspection qualified deviation sets of the shunt and K product quality inspection unqualified deviation sets of the shunt.

[0025] Specifically, for each product quality inspection information set of the shunt, the product quality standard information is compared, and the deviation is identified for the same quality inspection index. For each quality inspection index of each product, the deviation between the actual measurement value and the standard value is calculated. These deviations are classified according to the shunt to generate K product quality inspection deviation sets of the shunt, each set containing the deviation information of all quality inspection indexes of the corresponding shunt product. In the K product quality inspection deviation sets of the shunt, according to the pre-set qualified standard, the quality inspection index deviation of each product is divided into qualified deviation and unqualified deviation. The qualified deviation is summarized into K product quality inspection qualified deviation sets of the shunt, and the unqualified deviation is summarized into K product quality inspection unqualified deviation sets of the shunt.

[0026] Step four: the number of deviations of the K product quality inspection unqualified deviation sets of the shunt is respectively counted, and the statistical results are compared with the number of deviations in the K product quality inspection deviation sets of the shunt, and the results are taken as K first detection coefficients.

[0027] Specifically, for each product quality inspection unqualified deviation set of the shunt, the number of unqualified deviations is counted to determine the frequency and severity of quality problems in the production process of each shunt. The number of deviations of each product quality inspection unqualified deviation set of the shunt is compared with the total number of deviations of the corresponding product quality inspection deviation set of the shunt. The number of unqualified deviations and the total number of deviations are calculated by ratio to obtain the first detection coefficient, which provides an index for quality control of each shunt, reflecting the proportion of unqualified products in the total products.

[0028] Step five: iterate through the K product quality inspection qualified deviation sets of the shunt to identify the tolerance interval, and obtain K shunt tolerance intervals.

[0029] Specifically, the tolerance interval of each of the K sets of product quality inspection qualified deviation sets is identified in sequence to determine the acceptable quality deviation range of each shunt channel, i.e. the tolerance interval, which is used for subsequent product quality evaluation. According to the product consistency of different shunt channels, when the product quality inspection result of the same shunt channel is different from the general situation, there is a certain risk.

[0030] Step six: based on the size of the K first detection coefficients, a shunt channel extraction wheel is constructed, the shunt channel 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 number identifiers, M first shunt channel sampling numbers and M shunt channel tolerance intervals.

[0031] Specifically, based on the size of the K first detection coefficients, a shunt channel extraction wheel is constructed. The first detection coefficient represents the unqualified rate of each shunt channel. The higher the unqualified rate of the shunt channel, the greater the sampling probability of the shunt channel on the wheel. The constructed shunt channel extraction wheel is extracted multiple times according to a preset product sampling number. Each extraction will randomly select a shunt channel and record the sampling positioning serial number identifier. According to the extraction result, a first automatic detection scheme is determined, including M first sampling positioning serial number identifiers, M first shunt channel sampling numbers and M shunt channel tolerance intervals. The first automatic detection scheme will guide the product quality sampling activity on the production line.

[0032] Step seven: at the first time node, input the first automatic detection scheme into the automatic detection unit of the target injection molding production line for product automatic detection.

[0033] Specifically, at the first time node, the first automatic detection scheme is input into the automatic detection unit of the target injection molding production line. The automatic detection unit will perform product automatic detection according to the scheme to ensure that the quality of the product in the production process meets the preset standard.

[0034] The dynamic detection method combined with shunt channel product quality analysis is applied to a dynamic detection device combined with shunt channel product quality analysis, which can achieve the technical goal of balanced monitoring of plastic mechanical forming quality and achieve the technical effect of improving production efficiency and reducing resource waste.

[0035] Further, the present application also includes:

[0036] The K sets of shunt channel product quality inspection information sets and the product quality standard information are respectively subjected to difference calculation to generate K sets of shunt channel product index deviation sets; according to a preset index deviation value threshold set, the K sets of shunt channel product index deviation value sets are subjected to qualified identification, and K sets of shunt channel product quality inspection qualified deviation sets and K sets of shunt channel product quality inspection unqualified deviation sets are obtained according to the identification result.

[0037] Specifically, for each sub-flow product quality inspection information set, compare with the predetermined product quality standard information, calculate the difference between the actual measured value and the standard value of each product quality index, representing the deviation between the product index and the standard. Repeat iteration to generate K sub-flow product index deviation sets, containing all index deviation values of the corresponding sub-flow products.

[0038] Then, according to the preset index deviation value threshold set, evaluate each sub-flow product index deviation set. The preset index deviation value threshold set refers to the set of deviation value ranges allowed by different indexes. Each product quality index deviation value is compared with the corresponding threshold. 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. Identify the qualified or unqualified products of each sub-flow. According to the results of the qualified identification, divide the products of each sub-flow into a quality inspection qualified deviation set and a quality inspection unqualified deviation set. The K sub-flow product quality inspection qualified deviation set contains all product information with deviation values within the threshold range. The K sub-flow product quality inspection unqualified deviation set contains all product information with deviation values exceeding the threshold range.

[0039] Through the difference calculation and qualified identification method, the product quality of plastic mechanical forming is effectively monitored, problems are found and solved in a timely manner, thereby improving production efficiency and product quality.

[0040] Further, the present application also includes:

[0041] Respectively, mean value calculation is performed on the K sub-flow product quality inspection qualified deviation sets to generate K sub-flow product quality inspection qualified deviation means; based on the K sub-flow product quality inspection qualified deviation means and a preset tolerance step, K first tolerance regions are constructed, and K first region densities of the K first tolerance regions are calculated; starting from the edges of the K first tolerance regions, the K first tolerance regions are diffused according to the preset tolerance step to obtain K second tolerance regions; K second region densities of the K second tolerance regions are calculated; size judgment is performed according to the K first region densities and the K second region densities, and tolerance interval identification is performed according to the judgment result to obtain the K sub-flow tolerance intervals.

[0042] Specifically, for each sub-flow product quality inspection qualified deviation set, calculate the average value of all qualified deviations to generate K sub-flow product quality inspection qualified deviation means, representing the average deviation level of each sub-flow within the qualified range.

[0043] Then, based on the mean of the product quality inspection qualified deviation of each sub-flow channel and the preset tolerance step, a first tolerance region is constructed for each sub-flow channel. The tolerance step is determined according to the quality standard and production requirements, and is used to determine the width of the tolerance region. The first tolerance region is a region extending to both sides with the mean of the product quality inspection qualified deviation of the K sub-flow channels as the center, i.e., the center of the qualified deviation mean, and the preset tolerance step as the radius. For the first tolerance region of each sub-flow channel, the density of qualified products in the region, i.e., the first region density, is calculated by counting the number of qualified products in the region.

[0044] Next, starting from the edge of the first tolerance region of each sub-flow channel, the second tolerance region is obtained by diffusing to both sides according to the preset tolerance step. The second tolerance region is wider than the first tolerance region and includes an additional region outside the first tolerance region, which is used for more relaxed quality evaluation.

[0045] Next, for the second tolerance region of each sub-flow channel, the density of qualified products in the region, i.e., the second region density, is calculated by counting the number of qualified products in the region.

[0046] Then, the first region density and the second region density of each sub-flow channel are compared. If the first region density is higher than the second region density, it means that the product quality is concentrated in a smaller deviation range, and the quality is more stable. If the second region density is higher than the first region density, it means that more product quality deviations are distributed in a wider range, and potential quality problems need to be paid attention to. According to the consistency of products of different sub-flow channels, when the product quality inspection results of the same sub-flow channel are different from the general situation, there is a certain risk. According to the K tolerance intervals obtained, the production parameters can be adjusted, the process flow can be optimized, or the equipment can be maintained to improve the consistency and stability of product quality.

[0047] By setting the tolerance interval, a reasonable product quality standard is determined to ensure the balance between production efficiency and product quality.

[0048] Further, the present application also includes:

[0049] When the K first region densities are less than or equal to the K second region densities, the K second tolerance regions are further diffused according to the preset tolerance step, and the tolerance interval is identified according to the diffusion results until the gain of the region density is less than or equal to the preset gain, the diffusion is stopped, and the K N tolerance regions are taken as the K target tolerance regions; the maximum and minimum of the product quality inspection qualified deviation of the sub-flow channel in the K target tolerance regions are taken as the endpoint values of the sub-flow channel tolerance interval, and the K sub-flow channel tolerance intervals are generated.

[0050] Specifically, the first region density and the second region density of each sub-flow path are compared. If the first region density is less than or equal to the second region density, it indicates that there are more qualified products in a wider deviation range. When the first region density is less than or equal to the second region density, the second tolerance region is continued to be diffused according to the preset tolerance step, and a suitable tolerance interval is obtained, so that the distribution of product quality is more reasonable. During the diffusion process, the gain of the region density, i.e. the increase of the density after each diffusion, is monitored. If the gain of the density is less than or equal to the preset gain threshold, it indicates that further diffusion will not significantly increase the number of qualified products, and therefore the diffusion can be stopped. After stopping the diffusion, the current K N-th tolerance regions are taken as K target tolerance regions, which represent the acceptable product quality deviation range under the quality standard.

[0051] Then, according to the maximum and minimum values of the product quality inspection qualified deviation of the sub-flow path in the target tolerance region, the end point value of the sub-flow path tolerance interval is determined, which represents the acceptable deviation range of the product quality of each sub-flow path, i.e. K sub-flow path tolerance intervals. After generating the sub-flow path tolerance interval, the production process is adjusted according to the tolerance interval, and the quality control strategy is optimized.

[0052] Through the setting of the tolerance interval, the fluctuation range of the product quality is further obtained, and corresponding measures are taken to improve the consistency and reliability of the product quality.

[0053] Further, the present application also includes:

[0054] The ratio of the K first detection coefficients to the sum of the K first detection coefficients is calculated respectively, and the calculation result is taken as K roulette probability coefficients; the K roulette probability coefficients are used to construct the sub-flow path extraction roulette; the sub-flow path extraction roulette is extracted multiple times according to the preset product extraction quantity, and multiple extraction results are obtained, wherein each extraction result includes an inspection positioning serial number identifier; the multiple extraction results are clustered and analyzed based on the positioning serial number identifier, and M first inspection positioning serial number identifiers and M first sub-flow path inspection quantities are generated; the M first inspection positioning serial number identifiers are matched with the K sub-flow path tolerance intervals, and M sub-flow path tolerance intervals are generated.

[0055] Specifically, for the first detection coefficient of each sub-flow path, the ratio of the first detection coefficient to the sum of all first detection coefficients is calculated, which represents the contribution of each sub-flow path in the total rejection rate, and is called K roulette probability coefficients.

[0056] Then, the calculated K roulette probability coefficients are used to construct the sub-flow path extraction roulette. The proportion of each sub-flow path on the roulette is proportional to its roulette probability coefficient, ensuring that the sub-flow path with a higher rejection rate has a greater probability of inspection.

[0057] Next, according to the preset product extraction quantity, the constructed diversion channel extraction wheel is extracted multiple times. Each extraction randomly selects a diversion channel and records the extraction positioning serial number identifier.

[0058] Next, the positioning serial number identifiers in the multiple extraction results are subjected to cluster analysis, grouping similar extraction positioning serial number identifiers to identify the diversion channels to be extracted. The cluster analysis obtains M first extraction positioning serial number identifiers and corresponding M first diversion channel extraction quantities.

[0059] Then, the M first extraction positioning serial number identifiers are matched with K diversion channel tolerance intervals to determine a suitable tolerance interval for each extraction positioning serial number identifier for evaluating product quality during extraction. According to the matching results, M diversion channel tolerance intervals are generated to guide the extraction process and ensure that the extracted products are acceptable in quality.

[0060] Through the wheel probability coefficient and cluster analysis methods, random extraction of product quality during plastic mechanical forming is achieved, ensuring the relevance and effectiveness of the extraction, which helps to timely identify potential quality problems and take appropriate measures to improve the production process.

[0061] Further, the present application also includes:

[0062] According to the first time node and the preset detection change period, a first change node is obtained; the diversion channel extraction wheel is extracted again according to the preset product extraction quantity to determine a first changed detection scheme; the first automatic detection scheme and the first changed detection scheme are subjected to transition authentication, and if the authentication is passed, the first changed detection scheme is taken as a second automatic detection scheme of the first change node, wherein the second automatic detection scheme includes N second extraction positioning serial number identifiers, N second diversion channel extraction 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 product automatic detection.

[0063] Specifically, different diversion channel production products have relative stability, and it is necessary to confirm whether they have been detected before product detection, thereby avoiding continuous detection of a part of the diversion channel production products while another part of the channel production products is not detected.

[0064] Then, the preset detection change period refers to the time period of adjacent two automatic detection scheme changes, and the time interval is preset according to production demand, equipment maintenance plan, quality fluctuation, etc. According to the first time node and the preset detection change period, the next change node, i.e., the first change node, is calculated.

[0065] Then, when reaching the first change node, the product sampling wheel is sampled again according to the preset product sampling quantity, and a first change detection scheme is determined to reflect the current production status.

[0066] Next, the transition authentication is an evaluation process for determining whether the product has been detected, so as to avoid continuous detection of the product produced by part of the flow channel while no detection of the product produced by another part of the flow channel. The first automatic detection scheme and the first change detection scheme are compared. If the first change detection scheme passes the transition authentication, it will be adopted as the second automatic detection scheme of the first change node. The second automatic detection scheme includes N second sampling positioning serial numbers, N second flow channel sampling quantities, and N flow channel tolerance intervals, which guide the production sampling in the next stage.

[0067] Then, the second automatic detection scheme is input into the automatic detection unit of the target injection molding production line. The automatic detection unit will perform automatic detection of the product according to the new scheme, ensuring that each flow channel product is detected.

[0068] Through the periodic detection scheme change and transition authentication process, it is ensured that each flow channel product is detected, while avoiding repeated detection, thereby improving the effectiveness of quality control and production efficiency, helping to timely discover and solve changes in the production process, and ensuring the continuous stability of product quality.

[0069] Further, the application also includes:

[0070] The similarity identifier is used to identify the similarity between the first automatic detection scheme and the first change detection scheme, and obtain a first change similarity. It is determined whether the first change similarity is less than or equal to a preset similarity. If yes, the authentication passes; if no, the authentication fails, and the product sampling wheel is sampled again according to the preset product sampling quantity to determine a second change detection scheme. The second change detection scheme and the first automatic detection scheme are subjected to transition authentication. If the authentication passes, the second change detection scheme is taken as the second automatic detection scheme of the first change node.

[0071] Specifically, the similarity identifier is used to identify the similarity between the first automatic detection scheme and the first change detection scheme. Based on multiple parameters such as sampling positioning serial numbers, flow channel sampling quantities, and tolerance intervals, the similarity between the two schemes is analyzed by the similarity identifier. The result of similarity identification is obtained as the first change similarity, which represents the degree of similarity between the two schemes.

[0072] Then, it is judged whether the first change similarity is less than or equal to a preset similarity. If the similarity is less than or equal to the preset similarity, it indicates that the first change detection scheme has sufficient difference from the first automatic detection scheme, and the authentication passes. If the similarity is greater than the preset similarity, it indicates that the first change detection scheme has little difference from the first automatic detection scheme, and the authentication fails. If the authentication fails, the product sampling wheel of the shunt is extracted again according to the preset product sampling quantity.

[0073] Then, the second change detection scheme and the first automatic detection scheme are subjected to transition authentication. If the second change detection scheme passes the transition authentication, that is, the product has not been detected, the second change detection scheme is adopted as the second automatic detection scheme of the first change node.

[0074] Through the similarity identification and transition authentication process, continuous detection of a part of the shunt production and no detection of another part of the shunt production are avoided, the flexibility and adaptability of the detection scheme are improved, and the product quality is more effectively controlled.

[0075] In summary, the dynamic detection method provided by the application has the following technical effects:

[0076] By identifying the serial numbers of the K shunts according to the relative positions of the K shunts and the main runner in the target injection molding production line, K positioning serial number identifiers are generated; the product quality inspection information of the K shunts produced in a historical window is retrieved respectively to obtain K shunt product quality inspection information sets; the K shunt product quality inspection information sets and the product quality standard information are subjected to same-index deviation identification to generate K shunt product quality inspection deviation sets, wherein the K shunt product quality inspection deviation sets include K shunt product quality inspection qualified deviation sets and K shunt product quality inspection unqualified deviation sets; the number of deviations in the K shunt product quality inspection unqualified deviation sets is counted respectively, and the counting results are compared with the number of deviations in the K shunt product quality inspection deviation sets, and the results are taken as K first detection coefficients; the K shunt product quality inspection qualified deviation sets are traversed to identify tolerance intervals to obtain K shunt tolerance intervals; a shunt sampling wheel is constructed based on the sizes of the K first detection coefficients, the shunt sampling wheel is sampled multiple times according to a preset product sampling quantity, and a first automatic detection scheme is determined according to the sampling results, wherein the first automatic detection scheme includes M first sampling positioning serial number identifiers, M first shunt sampling quantities, and M shunt tolerance intervals; at a first time node, the first automatic detection scheme is input into an automatic detection unit of the target injection molding production line for product automatic detection, achieving the technical goal of balanced monitoring of plastic mechanical molding quality and achieving the technical effect of improving production efficiency and reducing resource waste. Embodiments

[0077] Based on the dynamic detection method combined with the product quality analysis of the runner in the foregoing embodiments, the application also provides a dynamic detection device combined with the product quality analysis of the runner. Please refer to the accompanying drawings. Figure 2 The device comprises:

[0078] A positioning serial number identification generation module 11 is configured to serially identify K runners in a target injection molding production line according to the relative positions of the K runners and a main runner, and generate K positioning serial number identifications.

[0079] A runner product quality inspection information set obtaining module 12 is 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.

[0080] A runner product quality inspection deviation set generation module 13 is configured to identify deviations of the K runner product quality inspection information sets from product quality standard information, 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.

[0081] A first detection coefficient obtaining module 14 is 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.

[0082] A runner tolerance interval obtaining module 15 is configured to traverse the K runner product quality inspection qualified deviation sets to identify tolerance intervals, and obtain K runner tolerance intervals.

[0083] A first automatic detection scheme determination module 16 is 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 extraction quantity, and determine a first automatic detection scheme according to the extraction results, wherein the first automatic detection scheme comprises M first extraction positioning serial number identifications, M first runner extraction quantities, and M runner tolerance intervals.

[0084] An automatic detection module 17 is configured to input the first automatic detection scheme into an automatic detection unit of the target injection molding production line to perform product automatic detection at a first time node.

[0085] Further, the shunt channel product quality inspection deviation set generation module 13 in the device is further configured to:

[0086] respectively perform difference calculation on the K shunt channel product quality inspection information sets and the product quality standard information to generate K shunt channel product index deviation sets;

[0087] According to a preset index deviation value threshold set, perform qualified identification on the K shunt channel product index deviation value sets, and obtain K shunt channel product quality inspection qualified deviation sets and K shunt channel product quality inspection unqualified deviation sets according to the identification results.

[0088] Further, the shunt channel tolerance interval obtaining module 15 in the device is further configured to:

[0089] respectively perform mean value calculation on the K shunt channel product quality inspection qualified deviation sets to generate K shunt channel product quality inspection qualified deviation means;

[0090] Based on the K shunt channel product quality inspection qualified deviation means and a preset tolerance step, construct K first tolerance regions, and calculate K first region densities of the K first tolerance regions;

[0091] Starting from edges of the K first tolerance regions, diffuse the K first tolerance regions according to the preset tolerance step to obtain K second tolerance regions;

[0092] Calculate K second region densities of the K second tolerance regions;

[0093] According to the K first region densities and the K second region densities, perform size judgment, and according to the judgment results, perform tolerance interval identification to obtain the K shunt channel tolerance intervals.

[0094] Further, the shunt channel tolerance interval obtaining module 15 in the device is further configured to:

[0095] When the K first region densities are less than or equal to the K second region densities, continue to diffuse the K second tolerance regions according to the preset tolerance step, and according to the diffusion results, perform tolerance interval identification, and when the gain of the region density is less than or equal to a preset gain, stop the diffusion, and take K Nth tolerance regions as K target tolerance regions;

[0096] According to the maximum and minimum of the product quality inspection deviation of the diversion channel in the K target tolerance regions as the end point values of the diversion channel tolerance interval, the K diversion channel tolerance intervals are generated.

[0097] Further, the first automatic detection scheme determination module 16 in the device is further used for:

[0098] The ratio of the K first detection coefficients and the sum of the K first detection coefficients is calculated respectively, and the calculation result is taken as K roulette probability coefficients;

[0099] The diversion channel extraction roulette is constructed according to the K roulette probability coefficients;

[0100] The diversion channel extraction roulette is extracted multiple times according to the preset product extraction quantity, and multiple extraction results are obtained, wherein each extraction result includes an inspection positioning serial number identifier;

[0101] Based on the positioning serial number identifier, the multiple extraction results are clustered and analyzed to generate M first inspection positioning serial number identifiers and M first diversion channel inspection quantities;

[0102] According to the matching of the M first inspection positioning serial number identifiers and the K diversion channel tolerance intervals, M diversion channel tolerance intervals are generated.

[0103] Further, the first automatic detection scheme determination module 16 in the device is further used for:

[0104] According to the first time node and the preset detection change period, a first change node is obtained;

[0105] The diversion channel extraction roulette is extracted again according to the preset product inspection quantity to determine a first changed detection scheme;

[0106] The first automatic detection scheme and the first changed detection scheme are authenticated, and if the authentication is passed, the first changed detection scheme is taken as a second automatic detection scheme of the first change node, wherein the second automatic detection scheme includes N second inspection positioning serial number identifiers, N second diversion channel inspection quantities and N diversion channel tolerance intervals;

[0107] The second automatic detection scheme is input into the automatic detection unit of the target injection molding production line for product automatic detection.

[0108] Further, the first automatic detection scheme determination module 16 in the device is further used for:

[0109] The similarity between the first automatic detection scheme and the first changed detection scheme is identified by using a similarity identifier to obtain a first change similarity.

[0110] determining whether the first change similarity is less than or equal to a preset similarity, and if yes, the authentication is passed;

[0111] if no, the authentication is failed, the diversion channel extraction wheel is extracted again according to a preset product sampling quantity, and a second change detection scheme is determined;

[0112] transition authentication is performed on the second change detection scheme and the first automatic detection scheme, and if the authentication is passed, the second change detection scheme is taken as a second automatic detection scheme of the first change node.

[0113] Each of the embodiments in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The dynamic detection method and specific example of the dynamic detection device for product quality analysis of the diversion channel in the foregoing embodiment one are also applicable to the dynamic detection device for product quality analysis of the diversion channel in the embodiment. Through the foregoing detailed description of the dynamic detection method for product quality analysis of the diversion channel, those skilled in the art can clearly know the dynamic detection device for product quality analysis of the diversion channel in the embodiment. Therefore, for the sake of brevity of the specification, the dynamic detection device for product quality analysis of the diversion channel is not described in detail herein. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the related part is described in the method part.

[0114] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0115] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application also intends to include these modifications and variations.

Claims

1. A dynamic detection method combined with product quality analysis of a diverter, characterized in that, The method comprises: According to the relative position of K sub-channels and the main runner in the target injection molding production line, the K sub-channels are sequentially numbered and K positioning serial numbers are generated; Respectively, the product quality inspection information of the K sub-channels produced in the historical window is called to obtain K sub-channel product quality inspection information sets; The K sub-channel product quality inspection information sets and the product quality standard information are identified for the same index deviation to generate K sub-channel product quality inspection deviation sets, wherein 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 number of deviations in the K sub-channel product quality inspection unqualified deviation sets is respectively counted, and the statistical results are compared with the number of deviations in the K sub-channel product quality inspection deviation sets, and the results are taken as K first detection coefficients; The K sub-channel product quality inspection qualified deviation sets are iterated to identify the tolerance interval to obtain K sub-channel tolerance intervals; Based on the size of the K first detection coefficients, a sub-channel extraction wheel is constructed, the sub-channel 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-channel sampling numbers and M sub-channel tolerance intervals; At a first time node, the first automatic detection scheme is input into the automatic detection unit of the target injection molding production line for product automatic detection.

2. The method of claim 1, wherein, The K sub-channel product quality inspection information sets and the product quality standard information are identified for the same index deviation to generate K sub-channel product quality inspection deviation sets, the method comprising: The K sub-channel product quality inspection information sets and the product quality standard information are respectively difference calculated to generate K sub-channel product index deviation sets; According to a preset index deviation value threshold set, the K sub-channel product index deviation value sets are identified for qualification, and K sub-channel product quality inspection qualified deviation sets and K sub-channel product quality inspection unqualified deviation sets are obtained according to the identification results.

3. The method of claim 1, wherein, The K sub-channel product quality inspection qualified deviation sets are iterated to identify the tolerance interval to obtain K sub-channel tolerance intervals, the method comprising: The K sub-channel product quality inspection qualified deviation sets are respectively mean calculated to generate K sub-channel product quality inspection qualified deviation means; Based on the K sub-channel product quality inspection qualified deviation means and a preset tolerance step, K first tolerance regions are constructed, and K first region densities of the K first tolerance regions are calculated; From the edge of the K first tolerance regions, the K first tolerance regions are diffused according to the preset tolerance step to obtain K second tolerance regions; The K second region densities of the K second tolerance regions are calculated; According to the size judgment of the K first region densities and the K second region densities, the tolerance interval is identified according to the judgment result to obtain the K sub-channel tolerance intervals.

4. The method of claim 3, wherein, The method comprises: When the K first region densities are less than or equal to the K second region densities, continue to diffuse the K second tolerance regions according to the preset tolerance step, and identify the tolerance intervals according to the diffusion results, until the gain of the region density is less than or equal to the preset gain, stop the diffusion, and take the K Nth tolerance regions as the K target tolerance regions; According to the maximum and minimum of the deviation of the product quality inspection of the K target tolerance regions, the end point values of the K tolerance intervals of the diversion channel are generated.

5. The method of claim 1, wherein, Based on the size of the K first detection coefficients, a diversion channel extraction wheel is constructed, the diversion channel extraction wheel is extracted multiple times according to a preset product extraction quantity, and a first automatic detection scheme is determined according to the extraction results, and the method comprises: The ratio of the K first detection coefficients to the sum of the K first detection coefficients is calculated respectively, and the calculation result is taken as the K wheel probability coefficients; According to the K wheel probability coefficients, the diversion channel extraction wheel is constructed; The diversion channel extraction wheel is extracted multiple times according to a preset product extraction quantity, and a plurality of extraction results are obtained, wherein each extraction result comprises an extraction positioning serial number identifier; Based on the positioning serial number identifier, the plurality of extraction results are clustered and analyzed to generate M first extraction positioning serial number identifiers and M first diversion channel extraction quantities; According to the M first extraction positioning serial number identifiers and the K tolerance intervals of the diversion channel, M tolerance intervals of the diversion channel are generated.

6. The method of claim 1, wherein, The method comprises: According to the first time node and a preset detection change period, a first change node is obtained; The diversion channel extraction wheel is extracted again according to a preset product extraction quantity to determine a first change detection scheme; The first automatic detection scheme and the first change detection scheme are authenticated, and if the authentication is passed, the first change detection scheme is taken as a second automatic detection scheme of the first change node, wherein the second automatic detection scheme comprises N second extraction positioning serial number identifiers, N second diversion channel extraction quantities and N tolerance intervals of the diversion channel; The second automatic detection scheme is input into the automatic detection unit of the target injection molding production line for product automatic detection.

7. The method of claim 6, wherein, The method comprises: The similarity between the first automatic detection scheme and the first change detection scheme is identified by using a similarity identifier to obtain a first change similarity; It is judged whether the first change similarity is less than or equal to a preset similarity, if yes, the authentication is passed; If not, the authentication fails, the diversion channel extraction wheel is extracted again according to a preset product extraction quantity to determine a second change detection scheme; The second change detection scheme and the first automatic detection scheme are authenticated, and if the authentication is passed, the second change detection scheme is taken as a second automatic detection scheme of the first change node.

8. A dynamic detection device combined with product quality analysis of a diverter, characterized in that, The device for implementing the steps of the method in any one of claims 1 to 7 comprises: The positioning serial number identification generation module is configured to perform serial number identification on the K sub-runners according to relative positions of the K sub-runners and the main runner in the target injection molding production line, and generate K positioning serial number identifications. The sub-runner product quality inspection information set obtaining module is configured to perform quality inspection information retrieval on products produced by the K sub-runners in a historical window, respectively, and obtain K sub-runner product quality inspection information sets. The sub-runner product quality inspection deviation set generation module is configured to perform same-index deviation identification on the K sub-runner product quality inspection information sets and product quality standard information, and generate K sub-runner product quality inspection deviation sets, 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 first detection coefficient obtaining module is configured to respectively count the number of deviations in the K sub-runner product quality inspection unqualified deviation sets, compare the statistical results with the number of deviations in the K sub-runner product quality inspection deviation sets, and take the results as K first detection coefficients. The sub-runner tolerance interval obtaining module is configured to traverse the K sub-runner product quality inspection qualified deviation sets to identify tolerance intervals, and obtain K sub-runner tolerance intervals. The first automatic detection scheme determination module is configured to construct a sub-runner extraction wheel based on the sizes of the K first detection coefficients, extract the sub-runner extraction wheel multiple times according to a preset product sampling quantity, and determine a first automatic detection scheme according to an extraction result, wherein the first automatic detection scheme includes M first sampling positioning serial number identifications, M first sub-runner sampling quantities, and M sub-runner tolerance intervals. The automatic detection module is 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.

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