CN114330926A - Production quality control method and system with self-optimization mechanism - Google Patents
Production quality control method and system with self-optimization mechanism Download PDFInfo
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Abstract
The application relates to a production quality control method with a self-optimization mechanism, which comprises the following steps: judging the deviation condition of the actual production condition and the standard production process according to the production condition in the actual production process; collecting quality information of the product; judging the influence capacity of different process parameters on the product quality condition according to the deviation condition of the actual production parameters and the standard process parameters and the product quality condition matched with the deviation condition of the corresponding type of process parameters; and screening the process optimization direction according to the influence capacity of the process deviation condition and the deviation of different process parameters on the product quality condition. The method provides two types of optimization directions, the first type of optimization direction is a non-critical process parameter with high deviation, and the process parameter is adjusted, so that the production cost is reduced while the product quality is ensured; the second type of optimization direction is key process parameters with safety problems, the process parameters are adjusted, the product quality safety problems are solved, and the product quality safety risks are reduced.
Description
Technical Field
The present application relates to the field of production quality optimization, and in particular, to a production quality control method and system with a self-optimization mechanism.
Background
The product involves a plurality of successive, coupled processes in the manufacturing process, each of which requires the set values and quality indicators of the process parameters to be controlled within certain limits in order to ensure the final quality of the finished product. In the prior art, a centralized manufacturing mode can be adopted for products with relatively single variety and large batch, namely, according to production experience, each procedure is accurately controlled through preset process parameters to ensure the final quality of finished products. However, when the method faces different production sites, production equipment or production links, the product quality is easy to be different, and the standard process parameters are difficult to adapt to the requirements of all production factories.
CN202010751566.3 discloses a technological parameter optimization method for a multi-process industrial production process, which adopts the technical scheme that: analyzing the production process, and identifying the final quality index of the industrial production process, the quality index of each procedure and the process parameters in the procedures; on the basis of the batch number, correlating the final quality index with the quality indexes of all working procedures and the process parameters in the working procedures to form a batch record, and acquiring a plurality of batch records to form a modeling data set; screening out a final quality index and quality index data of each process from the modeling data set to form a final quality index prediction data set; screening out quality indexes of all working procedures and process parameter data in the working procedures from the modeling data set to form a quality index prediction data set of all the working procedures; respectively establishing a final quality index prediction model and each process quality index prediction model based on the final quality index prediction data set and each process quality index prediction data set; constructing a first-layer optimization problem and solving the first-layer optimization problem based on the final quality index prediction model to obtain the optimal quality index of each process for optimizing the final quality index; and taking the obtained optimal quality indexes of all the procedures as targets of all the procedures, constructing and solving a second-layer optimization problem based on the quality index prediction models of all the procedures to obtain process parameters of all the procedures, wherein the quality indexes of all the procedures are optimal.
The technological parameter optimization method for the multi-process industrial production process has the following advantages: adding the quality indexes of all the procedures between the technological parameters of the multi-procedure production and the final quality indexes, and searching the optimal combination of the technological parameters by adopting a double-layer gradual optimization method; based on the data modeling and optimization solving method, the optimal process parameter combination can be obtained only by collecting actual production data and forming a modeling data set without the need of abundant production experience of production personnel and a large number of parameter combination tests; the quality indexes of all the procedures are added between the process parameters and the final quality indexes, so that the number of input variables of the model is reduced, the influence of the association between the parameters of different procedures on the model is avoided, and the precision of the model and the reliability of an optimization result are improved; the optimal quality indexes of all the procedures are obtained through the optimal final quality index model, the optimal process parameters of all the procedures are obtained through the optimal quality indexes of all the procedures, and therefore in the production process, the high tobacco shred finishing rate can be obtained by controlling the process parameters to be the optimal process parameters, and the tobacco shred finishing efficiency is greatly improved.
However, the optimization method of the process parameters for the multi-process industrial production process also has the following disadvantages: the process optimization direction is single, and the overall optimization consciousness is lacked.
Therefore, there is a need for a method or system for multi-directionally optimizing a manufacturing process.
Disclosure of Invention
In order to solve the problem of single process optimization direction, the application provides a production quality control method and system with a self-optimization mechanism.
The application provides a production quality control method with a self-optimization mechanism, which comprises the following steps:
step S1, evaluating the actual production condition, and judging the deviation condition of the actual production condition and the standard production process according to the production condition in the actual production process so as to judge whether the actual production condition is qualified;
step S2, counting the product quality, collecting the quality information of the product, and matching the production record data of the corresponding batch;
step S3, evaluating the influence degree of process deviation, and judging the influence capacity of different process parameters on the product quality condition according to the deviation condition of the actual production parameters and the standard process parameters and the product quality condition matched with the deviation condition of the corresponding type of process parameters;
and step S4, optimizing the process, screening the process optimization direction according to the influence capacity of the process deviation situation and the deviation of different process parameters on the product quality situation, and adjusting the production process.
Further, the step S1 includes: step S11, collecting production record data, collecting real-time production record data by a production record terminal, marking production time and uploading to a quality management platform; step S12, collecting process parameter data, extracting standard process parameter data corresponding to production records from a production process parameter database, and uploading the standard process parameter data to a quality management platform; step S13, evaluating the deviation degree, judging the deviation degree of the production record data and the corresponding process parameters according to the deviation condition of the collected production record data and the corresponding process parameters; and step S14, judging the actual production condition, and judging whether the actual production condition reaches the standard according to the production record data and the deviation degree of the corresponding process parameters.
By adopting the technical scheme, whether the production condition in the actual production process is qualified or not can be judged, the production problem is identified, and the production quality is ensured.
Further, in step S13, the deviation degree evaluation method includes:
wherein, λ is a deviation index of a certain production parameter to a standard process parameter in an actual production process, the larger λ is, the larger the deviation of the certain production parameter to the standard process parameter in the actual production process is, and the smaller λ is, the smaller the deviation of the certain production parameter to the standard process parameter in the actual production process is;
y is expressed as a parameter value of the production parameter in the actual production process; "y is expressed as the average value of the parameters of the production parameter in the actual production process; y' is expressed as the parameter value of the standard process parameter corresponding to the production parameter;
in the step S14, if the deviation index λ of the production parameter from the standard process parameter is higher than the set threshold in the actual production process, the deviation degree exceeds the standard, and the production condition is not qualified; if the deviation indexes lambda of all production parameters to the standard process parameters in the actual production process are lower than the set threshold, the deviation degree does not exceed the standard, and the production condition is qualified.
By adopting the technical scheme, the deviation index is introduced, and the deviation condition of the actual production condition to the standard process parameter is evaluated, so that whether the production condition meets the qualification requirement is judged, and the intuition degree of the judgment of the production condition is improved.
Further, the step S3 includes: step S31, collecting process deviation data, extracting deviation degree data of actual production parameters and standard process parameters in historical production records, and corresponding to production batches; step S32, evaluation of influence ability, namely extracting product quality data corresponding to the deviation data of the production process from the product quality parameter database 23, judging the influence ability conditions of the single process parameter and the single product quality parameter respectively, and uploading the influence ability data of each process parameter and each product quality parameter to a quality management platform; and step S33, forming a database, wherein the single process parameters are respectively in one-to-one correspondence with the influence capacity data of the single product quality parameters, and the process deviation influence database is formed.
Further, in step S32, the method for evaluating the influence of variation includes:
wherein epsilon is an index of influence of a deviation index of a certain production parameter on a certain product quality parameter in the actual production process, the larger epsilon is, the larger is the influence of the deviation index of the certain production parameter on the certain product quality parameter in the actual production process, and the smaller epsilon is, the smaller is the influence of the deviation index of the certain production parameter on the certain product quality parameter in the actual production process;
i is expressed as a production case number in the historical production record; n is expressed as the number of production cases in the historical production record; alpha is alphaiThe parameter value of the product quality parameter in the production case with the number i is expressed; lambda [ alpha ]iThe index of deviation of the production parameter from the standard process parameter in the production case numbered i is indicated.
By adopting the technical scheme, the process deviation influence ability index is introduced, the influence ability of the process deviation on each quality parameter is evaluated, and a data basis is provided for identifying critical process parameters and non-critical process parameters.
Further, the step S4 includes: step S41, identifying the characteristics of the process parameters, marking the process parameters with the influence capability higher than the set threshold as critical process parameters and marking the process parameters with the influence capability lower than the set threshold as non-critical process parameters according to the influence capability data of the process parameters and the product quality parameters; s42, screening a first-class process optimization direction, and selecting a non-critical process parameter with higher deviation as the first-class process optimization direction to adjust according to the production record data and the deviation condition of the corresponding process parameter; step S43, judging the quality safety of the product, and judging whether the quality safety problem exists in the production process by combining the deviation condition of the actual production condition and the standard process parameter in the production process and the influence capability data of the actual production condition and the standard process parameter with the product quality parameter; s44, screening a second type of process optimization direction, screening quality parameter types with quality safety problems, and selecting a process parameter type with the corresponding influence capability higher than a set threshold value as the second type of process optimization direction; and step S45, adopting process optimization measures, and adopting corresponding optimization measures according to the screened first type process optimization direction and the screened second type process optimization direction.
By adopting the technical scheme, two kinds of optimization directions are respectively provided, the first kind of optimization direction is a non-critical process parameter with higher deviation, and the process parameter is adjusted, so that the production cost is reduced while the product quality is ensured; the second type of optimization direction is key process parameters with safety problems, the process parameters are adjusted, the product quality safety problems are solved, and the product quality safety risks are reduced.
Further, in step S42, the method for determining the product quality safety problem in the production process includes:
wherein eta is a safety index of a certain product quality parameter in the production process to be evaluated, the larger eta is, the stronger the safety capability of the product quality parameter in the production process to be evaluated is, and the smaller eta is, the weaker the safety capability of the product quality parameter in the production process to be evaluated is;
j represents the type number of the production parameter or the corresponding process parameter; m represents allThe type and quantity of the production parameters or the corresponding process parameters; epsilonjThe production parameter deviation index with the category number j and the influence capability index of the product quality parameter are expressed in the production process to be evaluated; lambda [ alpha ]jThe deviation index of the production parameter with the category number j to the standard process parameter in the production process to be evaluated is expressed;
if the safety index eta of the product quality parameter in the production process to be evaluated is lower than a set threshold, the product quality safety problem exists in the production process; and if the safety indexes eta of all the product quality parameters in the production process to be evaluated exceed the set threshold, the product quality safety problem does not exist in the production process.
By adopting the technical scheme, the quality parameter safety index is introduced, the quality safety condition of the production process is evaluated, and the identification capability of the quality safety problem of the production process is improved.
A production quality control system with a self-optimizing mechanism comprising: a quality management platform and a production recording terminal;
the production recording terminal is connected with the quality management platform and used for uploading production recording data;
the quality management platform comprises: a memory; a processor connected with the memory; the production condition evaluation module runs on the processor and is used for evaluating the actual production condition according to a specified algorithm; the process deviation influence evaluation module runs on the processor and is used for evaluating the influence capacity of the process deviation condition on the product quality according to a specified algorithm; and the quality safety evaluation module runs on the processor and is used for evaluating the quality safety condition of the production process according to a specified algorithm.
Further, the method also comprises the following steps: the production process parameter database is arranged in the memory and used for storing production process parameter data; the production record database is arranged in the memory, is connected with the production record terminal and is used for receiving and storing production record data; the product quality parameter database is arranged in the memory and used for storing product quality parameter data; and the process deviation influence degree database is arranged in the memory, is connected with the process deviation influence evaluation module and is used for receiving and storing the process deviation influence degree data.
Further, the method also comprises the following steps: and the optimization direction screening module runs on the processor, is respectively connected with the production condition evaluation module, the process deviation influence evaluation module and the quality safety evaluation module, and is used for receiving the evaluation data and screening the first-type and second-type process optimization directions.
To sum up, the application comprises the following beneficial technical effects:
1. two kinds of optimization directions are provided, the first kind of optimization direction is a non-critical process parameter with higher deviation, and the process parameter is adjusted, so that the production cost is reduced while the product quality is ensured; the second type of optimization direction is a key process parameter with safety problems, and the process parameter is adjusted to solve the product quality safety problems and reduce the product quality safety risks;
2. by introducing the deviation index, the deviation condition of the actual production condition to the standard process parameter is evaluated, so that whether the production condition is qualified or not is judged, and the intuitionistic degree of judging the production condition is improved;
3. by introducing the process deviation influence ability index, the influence ability of the process deviation on each quality parameter is evaluated, and a data basis is provided for identifying key process parameters and non-key process parameters;
4. by introducing the quality parameter safety index, the quality safety condition of the production process is evaluated, and the identification capability of the quality safety problem of the production process is improved.
Drawings
Fig. 1 is a flowchart illustrating a method for controlling production quality with a self-optimization mechanism according to an embodiment of the present disclosure.
Fig. 2 is a block diagram of a production quality control system with a self-optimization mechanism according to an embodiment of the present application.
Description of reference numerals:
1. a quality management platform; 2. a memory; 21. a production process parameter database; 22. a production record database; 23. a product quality parameter database; 24. a process deviation influence volume database; 3. a processor; 4. a production condition evaluation module; 5. a process deviation influence evaluation module; 6. a quality safety evaluation module; 7. an optimized direction screening module;
8. and (5) producing the recording terminal.
Detailed Description
The following description of the embodiments with reference to the drawings is provided to describe the embodiments, and the embodiments of the present application, such as the shapes and configurations of the components, the mutual positions and connection relationships of the components, the functions and working principles of the components, the manufacturing processes and the operation and use methods, etc., will be further described in detail to help those skilled in the art to more fully, accurately and deeply understand the inventive concepts and technical solutions of the present invention. For convenience of description, the directions mentioned in the present application shall be those shown in the drawings.
Referring to fig. 1-2, a production quality control method with a self-optimization mechanism includes the following steps:
step S1, evaluating the actual production condition, and judging the deviation condition of the actual production condition and the standard production process according to the production condition in the actual production process so as to judge whether the actual production condition is qualified;
step S2, counting the product quality, collecting the quality information of the product, and matching the production record data of the corresponding batch;
step S3, evaluating the influence degree of process deviation, and judging the influence capacity of different process parameters on the product quality condition according to the deviation condition of the actual production parameters and the standard process parameters and the product quality condition matched with the deviation condition of the corresponding type of process parameters;
and step S4, optimizing the process, screening the process optimization direction according to the influence capacity of the process deviation situation and the deviation of different process parameters on the product quality situation, and adjusting the production process.
The step S1 includes: step S11, collecting production record data, collecting real-time production record data by the production record terminal 8, marking production time and uploading to the quality management platform 1; step S12, collecting process parameter data, extracting standard process parameter data corresponding to production records from the production process parameter database 21, and uploading the standard process parameter data to the quality management platform 1; step S13, evaluating the deviation degree, judging the deviation degree of the production record data and the corresponding process parameters according to the deviation condition of the collected production record data and the corresponding process parameters; and step S14, judging the actual production condition, and judging whether the actual production condition reaches the standard according to the production record data and the deviation degree of the corresponding process parameters.
In step S13, the deviation degree evaluation method includes:
wherein, λ is a deviation index of a certain production parameter to a standard process parameter in an actual production process, the larger λ is, the larger the deviation of the certain production parameter to the standard process parameter in the actual production process is, and the smaller λ is, the smaller the deviation of the certain production parameter to the standard process parameter in the actual production process is;
y is expressed as a parameter value of the production parameter in the actual production process; "y is expressed as the average value of the parameters of the production parameter in the actual production process; y' is expressed as the parameter value of the standard process parameter corresponding to the production parameter;
in the step S14, if the deviation index λ of the production parameter from the standard process parameter is higher than the set threshold in the actual production process, the deviation degree exceeds the standard, and the production condition is not qualified; if the deviation indexes lambda of all production parameters to the standard process parameters in the actual production process are lower than the set threshold, the deviation degree does not exceed the standard, and the production condition is qualified.
The step S3 includes: step S31, collecting process deviation data, extracting deviation degree data of actual production parameters and standard process parameters in historical production records, and corresponding to production batches; step S32, evaluation of influence ability, namely, extracting product quality data corresponding to the deviation data of the production process from the product quality parameter database 23, judging the influence ability conditions of the single process parameter and the single product quality parameter respectively, and uploading the influence ability data of each process parameter and each product quality parameter to the quality management platform 1; step S33, a database is formed, and the influence capability data of the single process parameters are respectively one-to-one corresponding to the single product quality parameters, and a process deviation influence database 24 is formed.
In step S32, the method for evaluating the influence of deviation includes:
wherein epsilon is an index of influence of a deviation index of a certain production parameter on a certain product quality parameter in the actual production process, the larger epsilon is, the larger is the influence of the deviation index of the certain production parameter on the certain product quality parameter in the actual production process, and the smaller epsilon is, the smaller is the influence of the deviation index of the certain production parameter on the certain product quality parameter in the actual production process;
i is expressed as a production case number in the historical production record; n is expressed as the number of production cases in the historical production record; alpha is alphaiThe parameter value of the product quality parameter in the production case with the number i is expressed; lambda [ alpha ]iThe index of deviation of the production parameter from the standard process parameter in the production case numbered i is indicated.
The step S4 includes: step S41, identifying the characteristics of the process parameters, marking the process parameters with the influence capability higher than the set threshold as critical process parameters and marking the process parameters with the influence capability lower than the set threshold as non-critical process parameters according to the influence capability data of the process parameters and the product quality parameters; s42, screening a first-class process optimization direction, and selecting a non-critical process parameter with higher deviation as the first-class process optimization direction to adjust according to the production record data and the deviation condition of the corresponding process parameter; step S43, judging the quality safety of the product, and judging whether the quality safety problem exists in the production process by combining the deviation condition of the actual production condition and the standard process parameter in the production process and the influence capability data of the actual production condition and the standard process parameter with the product quality parameter; s44, screening a second type of process optimization direction, screening quality parameter types with quality safety problems, and selecting a process parameter type with the corresponding influence capability higher than a set threshold value as the second type of process optimization direction; and step S45, adopting process optimization measures, and adopting corresponding optimization measures according to the screened first type process optimization direction and the screened second type process optimization direction.
In step S42, the method for determining the product quality safety problem in the production process includes:
wherein eta is a safety index of a certain product quality parameter in the production process to be evaluated, the larger eta is, the stronger the safety capability of the product quality parameter in the production process to be evaluated is, and the smaller eta is, the weaker the safety capability of the product quality parameter in the production process to be evaluated is;
j represents the type number of the production parameter or the corresponding process parameter; m represents all production parameters or the kinds and the numbers of the corresponding process parameters; epsilonjThe production parameter deviation index with the category number j and the influence capability index of the product quality parameter are expressed in the production process to be evaluated; lambda [ alpha ]jThe deviation index of the production parameter with the category number j to the standard process parameter in the production process to be evaluated is expressed;
if the safety index eta of the product quality parameter in the production process to be evaluated is lower than a set threshold, the product quality safety problem exists in the production process; and if the safety indexes eta of all the product quality parameters in the production process to be evaluated exceed the set threshold, the product quality safety problem does not exist in the production process.
A production quality control system with a self-optimizing mechanism comprising: a quality management platform 1 and a production recording terminal 8;
the production recording terminal 8 is connected with the quality management platform 1 and is used for uploading production recording data;
the quality management platform 1 includes: a memory 2; a processor 3 connected to the memory 2; the production condition evaluation module 4 runs on the processor 3 and is used for evaluating the actual production condition according to a specified algorithm; the process deviation influence evaluation module 5 runs on the processor 3 and is used for evaluating the influence capacity of the process deviation condition on the product quality according to a specified algorithm; and the quality safety evaluation module 6 runs on the processor 3 and is used for evaluating the quality safety condition of the production process according to a specified algorithm.
Further comprising: a production process parameter database 21 arranged in the memory 2 for storing production process parameter data; a production record database 22, arranged in the memory 2, connected with the production record terminal 8, for receiving and storing production record data; a product quality parameter database 23, provided in the memory 2, for storing product quality parameter data; and the process deviation influence database 24 is arranged in the memory 2, is connected with the process deviation influence evaluation module 5, and is used for receiving and storing the process deviation influence data.
Further comprising: and the optimization direction screening module 7 runs on the processor 3, is respectively connected with the production condition evaluation module 4, the process deviation influence evaluation module 5 and the quality safety evaluation module 6, and is used for receiving evaluation data and screening the first-type and second-type process optimization directions.
In the embodiment of the application, the working principle of the production quality control method and system with the self-optimization mechanism is as follows: two kinds of optimization directions are respectively provided, the first kind of optimization direction is a non-critical process parameter with higher deviation, and the process parameter is adjusted, so that the production cost is reduced while the product quality is ensured; the second type of optimization direction is key process parameters with safety problems, the process parameters are adjusted, the product quality safety problems are solved, and the product quality safety risks are reduced.
In the embodiment of the application, the deviation condition of the actual production condition to the standard process parameter is evaluated by introducing the deviation index, so that whether the production condition is qualified or not is judged, and the intuitionistic degree of judging the production condition is improved; by introducing the process deviation influence ability index, the influence ability of the process deviation on each quality parameter is evaluated, and a data basis is provided for identifying key process parameters and non-key process parameters; by introducing the quality parameter safety index, the quality safety condition of the production process is evaluated, and the identification capability of the quality safety problem of the production process is improved.
The present invention and its embodiments have been described above in an illustrative manner, and the description is not intended to be limiting, and the drawings are only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if the person skilled in the art receives the teaching, the technical scheme and the embodiments similar to the technical scheme are not creatively designed without departing from the spirit of the invention, and the invention shall fall into the protection scope of the invention.
Claims (10)
1. A production quality control method with a self-optimization mechanism is characterized by comprising the following steps:
step S1, evaluating the actual production condition, and judging the deviation condition of the actual production condition and the standard production process according to the production condition in the actual production process so as to judge whether the actual production condition is qualified;
step S2, counting the product quality, collecting the quality information of the product, and matching the production record data of the corresponding batch;
step S3, evaluating the influence degree of process deviation, and judging the influence capacity of different process parameters on the product quality condition according to the deviation condition of the actual production parameters and the standard process parameters and the product quality condition matched with the deviation condition of the corresponding type of process parameters;
and step S4, optimizing the process, screening the process optimization direction according to the influence capacity of the process deviation situation and the deviation of different process parameters on the product quality situation, and adjusting the production process.
2. The production quality control method with self-optimization mechanism as claimed in claim 1, wherein:
the step S1 includes: step S11, collecting production record data, collecting real-time production record data by a production record terminal (8), marking production time and uploading the data to a quality management platform (1); step S12, collecting process parameter data, extracting standard process parameter data corresponding to production records from a production process parameter database (21), and uploading the standard process parameter data to a quality management platform (1); step S13, evaluating the deviation degree, judging the deviation degree of the production record data and the corresponding process parameters according to the deviation condition of the collected production record data and the corresponding process parameters; and step S14, judging the actual production condition, and judging whether the actual production condition reaches the standard according to the production record data and the deviation degree of the corresponding process parameters.
3. The production quality control method with self-optimization mechanism according to claim 2, characterized in that:
in step S13, the deviation degree evaluation method includes:
wherein, λ is a deviation index of a certain production parameter to a standard process parameter in an actual production process, the larger λ is, the larger the deviation of the certain production parameter to the standard process parameter in the actual production process is, and the smaller λ is, the smaller the deviation of the certain production parameter to the standard process parameter in the actual production process is;
y is expressed as a parameter value of the production parameter in the actual production process; "y is expressed as the average value of the parameters of the production parameter in the actual production process; y' is expressed as the parameter value of the standard process parameter corresponding to the production parameter;
in the step S14, if the deviation index λ of the production parameter from the standard process parameter is higher than the set threshold in the actual production process, the deviation degree exceeds the standard, and the production condition is not qualified; if the deviation indexes lambda of all production parameters to the standard process parameters in the actual production process are lower than the set threshold, the deviation degree does not exceed the standard, and the production condition is qualified.
4. The production quality control method with self-optimization mechanism according to claim 3, characterized in that:
the step S3 includes: step S31, collecting process deviation data, extracting deviation degree data of actual production parameters and standard process parameters in historical production records, and corresponding to production batches; step S32, evaluation of influence ability, namely, extracting product quality data corresponding to the deviation data of the production process from the product quality parameter database (23), judging the influence ability conditions of the single process parameter and the single product quality parameter respectively, and uploading the influence ability data of each process parameter and each product quality parameter to the quality management platform (1); and step S33, forming a database, wherein the single process parameters are respectively in one-to-one correspondence with the influence capacity data of the single product quality parameters to form a process deviation influence database (24).
5. The production quality control method with self-optimization mechanism as claimed in claim 4, wherein:
in step S32, the method for evaluating the influence of deviation includes:
wherein epsilon is an index of influence of a deviation index of a certain production parameter on a certain product quality parameter in the actual production process, the larger epsilon is, the larger is the influence of the deviation index of the certain production parameter on the certain product quality parameter in the actual production process, and the smaller epsilon is, the smaller is the influence of the deviation index of the certain production parameter on the certain product quality parameter in the actual production process;
i is expressed as a production case number in the historical production record; n is expressed as the number of production cases in the historical production record; alpha is alphaiThe parameter value of the product quality parameter in the production case with the number i is expressed; lambda [ alpha ]iThe index of deviation of the production parameter from the standard process parameter in the production case numbered i is indicated.
6. The production quality control method with self-optimization mechanism as claimed in claim 5, wherein:
the step S4 includes: step S41, identifying the characteristics of the process parameters, marking the process parameters with the influence capability higher than the set threshold as critical process parameters and marking the process parameters with the influence capability lower than the set threshold as non-critical process parameters according to the influence capability data of the process parameters and the product quality parameters; s42, screening a first-class process optimization direction, and selecting a non-critical process parameter with higher deviation as the first-class process optimization direction to adjust according to the production record data and the deviation condition of the corresponding process parameter; step S43, judging the quality safety of the product, and judging whether the quality safety problem exists in the production process by combining the deviation condition of the actual production condition and the standard process parameter in the production process and the influence capability data of the actual production condition and the standard process parameter with the product quality parameter; s44, screening a second type of process optimization direction, screening quality parameter types with quality safety problems, and selecting a process parameter type with the corresponding influence capability higher than a set threshold value as the second type of process optimization direction; and step S45, adopting process optimization measures, and adopting corresponding optimization measures according to the screened first type process optimization direction and the screened second type process optimization direction.
7. The production quality control method with self-optimization mechanism as claimed in claim 6, wherein:
in step S42, the method for determining the product quality safety problem in the production process includes:
wherein eta is a safety index of a certain product quality parameter in the production process to be evaluated, the larger eta is, the stronger the safety capability of the product quality parameter in the production process to be evaluated is, and the smaller eta is, the weaker the safety capability of the product quality parameter in the production process to be evaluated is;
j represents the type number of the production parameter or the corresponding process parameter; m represents all production parameters or the kinds and the numbers of the corresponding process parameters; epsilonjThe production parameter deviation index with the category number j and the influence capability index of the product quality parameter are expressed in the production process to be evaluated; lambda [ alpha ]jIs shown to be under evaluationIn the production process of (3), the deviation index of the production parameter with the category number j to the standard process parameter;
if the safety index eta of the product quality parameter in the production process to be evaluated is lower than a set threshold, the product quality safety problem exists in the production process; and if the safety indexes eta of all the product quality parameters in the production process to be evaluated exceed the set threshold, the product quality safety problem does not exist in the production process.
8. A production quality control system with a self-optimizing mechanism, characterized in that a production quality control method with a self-optimizing mechanism according to any one of claims 1 to 7 is applied:
the production quality control system with the self-optimization mechanism comprises: the system comprises a quality management platform (1) and a production recording terminal (8);
the production recording terminal (8) is connected with the quality management platform (1) and is used for uploading production recording data;
the quality management platform (1) comprises: a memory (2); a processor (3) connected to the memory (2); the production condition evaluation module (4) runs on the processor (3) and is used for evaluating the actual production condition according to a specified algorithm; the process deviation influence evaluation module (5) runs on the processor (3) and is used for evaluating the influence capacity of the process deviation condition on the product quality according to a specified algorithm; and the quality safety evaluation module (6) runs on the processor (3) and is used for evaluating the quality safety condition of the production process according to a specified algorithm.
9. The production quality control system with self-optimizing mechanism of claim 8, wherein:
further comprising: the production process parameter database (21) is arranged in the memory (2) and is used for storing production process parameter data; the production record database (22) is arranged in the memory (2), is connected with the production record terminal (8) and is used for receiving and storing production record data; a product quality parameter database (23) disposed in the memory (2) for storing product quality parameter data; and the process deviation influence database (24) is arranged in the memory (2), is connected with the process deviation influence evaluation module (5) and is used for receiving and storing the process deviation influence data.
10. The production quality control system with self-optimizing mechanism of claim 8, wherein:
further comprising: and the optimization direction screening module (7) runs on the processor (3), is respectively connected with the production condition evaluation module (4), the process deviation influence evaluation module (5) and the quality safety evaluation module (6), and is used for receiving evaluation data and screening the first-class and second-class process optimization directions.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115965166A (en) * | 2023-03-16 | 2023-04-14 | 昆山市恒达精密机械工业有限公司 | Optimization method and system for plastic product production process |
| CN116224949A (en) * | 2023-05-10 | 2023-06-06 | 山东一方制药有限公司 | Traditional Chinese medicine formula granule production process data processing system based on artificial intelligence |
| CN117522100A (en) * | 2023-11-16 | 2024-02-06 | 永久电力金具有限公司 | Optimization method for electric power fitting production process |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107578100A (en) * | 2017-08-31 | 2018-01-12 | 江苏康缘药业股份有限公司 | The result feedback method and device of a kind of Kernel-based methods parameter |
| CN111966061A (en) * | 2020-07-23 | 2020-11-20 | 珠海格力电器股份有限公司 | Method and device for optimizing product process parameters, electronic equipment and storage medium |
| CN113656456A (en) * | 2021-08-13 | 2021-11-16 | 上海铸世自动化科技有限公司 | Real-time acquisition control method for process parameter big data in die-casting production |
-
2022
- 2022-01-11 CN CN202210028360.7A patent/CN114330926A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107578100A (en) * | 2017-08-31 | 2018-01-12 | 江苏康缘药业股份有限公司 | The result feedback method and device of a kind of Kernel-based methods parameter |
| CN111966061A (en) * | 2020-07-23 | 2020-11-20 | 珠海格力电器股份有限公司 | Method and device for optimizing product process parameters, electronic equipment and storage medium |
| CN113656456A (en) * | 2021-08-13 | 2021-11-16 | 上海铸世自动化科技有限公司 | Real-time acquisition control method for process parameter big data in die-casting production |
Non-Patent Citations (1)
| Title |
|---|
| 陈鸿雁 等: "《统计学》", vol. 1, 30 June 2021, 北京理工大学出版社, pages: 211 - 213 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115965166A (en) * | 2023-03-16 | 2023-04-14 | 昆山市恒达精密机械工业有限公司 | Optimization method and system for plastic product production process |
| CN116224949A (en) * | 2023-05-10 | 2023-06-06 | 山东一方制药有限公司 | Traditional Chinese medicine formula granule production process data processing system based on artificial intelligence |
| CN117522100A (en) * | 2023-11-16 | 2024-02-06 | 永久电力金具有限公司 | Optimization method for electric power fitting production process |
| CN117522100B (en) * | 2023-11-16 | 2024-04-05 | 永久电力金具有限公司 | Optimization method for electric power fitting production process |
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