CN114398392B - Product data calling control system and method based on process tolerance library - Google Patents
Product data calling control system and method based on process tolerance library Download PDFInfo
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Abstract
The invention discloses a product data calling control system and method based on a process tolerance library, belonging to the technical field of production control and comprising a process tolerance library, an extraction module, an analysis module, a production control module, a detection feedback module and a server; the process tolerance library is used for storing tolerance data; the extraction module is used for extracting data from the process tolerance library and sending the extracted data to the analysis module; the analysis module analyzes the acquired process tolerance data and sends an analysis result to the production control module for process adjustment; the detection feedback module is used for detecting the production product and feeding back the detection result to the analysis module for analysis and correction; the data obtained from the process tolerance library is corrected by analyzing the obtained process tolerance data, so that the corrected data can be improved by the current production process.
Description
Technical Field
The invention belongs to the technical field of production control, and particularly relates to a product data calling control system and method based on a process tolerance library.
Background
In the process of machining any part, the geometric shape of the machined part inevitably generates errors due to the geometric errors of a process system (a machine tool, a cutter and a tool clamp) and the influences of stress, deformation caused by heating, vibration, abrasion and the like in the machining process; these errors mainly include: dimensional deviation, shape error, position error, surface roughness, and the like.
However, the process tolerance data in the current production process is not fully utilized, the process tolerance data is not well applied to production and manufacturing, and the current production process is not optimized according to the process tolerance data.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides a product data calling control system and method based on a process tolerance library.
The purpose of the invention can be realized by the following technical scheme:
the system comprises a product data calling control system based on a process tolerance library, the process tolerance library, an extraction module, an analysis module, a production control module, a detection feedback module and a server;
the process tolerance library is used for storing tolerance data;
the extraction module is used for extracting data from the process tolerance library and sending the extracted data to the analysis module;
the analysis module analyzes the acquired process tolerance data and sends an analysis result to the production control module for process adjustment;
the detection feedback module is used for detecting the production product and feeding back the detection result to the analysis module for analysis and correction.
And further, acquiring process tolerance data, performing duplication elimination on the acquired data, and storing the duplicated process tolerance data into a process tolerance library.
Further, the method for extracting data from the process tolerance base by the extraction module comprises the following steps:
step SA 1: acquiring an extracted keyword, setting a voice recognition node, and further describing the extracted keyword by using the voice recognition node;
step SA 2: establishing a calling model, inputting the extracted keywords and the voice information into the calling model, and obtaining corresponding process tolerance data; obtaining the purpose of extracting process tolerance data;
when the extracted process tolerance data is used for subsequent manufacturing production, the extraction of the process tolerance data is completed.
Further, when the extracted process tolerance data is used for data updating in the process tolerance library, step SA3 is entered;
step SA 3: comparing the process tolerance data for updating with the obtained process tolerance data;
when the comparison data are the same, updating is not carried out;
when the comparison data is different, the process tolerance data for updating is replaced with the original process tolerance data.
Further, the method for analyzing the acquired process tolerance data by the analysis module comprises the following steps:
step SB 1: dividing the acquired process tolerance data into standard exceeding tolerance data and standard not exceeding tolerance data according to the process specification;
step SB 2: acquiring analysis reasons of the overproof tolerance data, and analyzing the current production process according to the analysis reasons;
when the problems in the reason are not analyzed in the current production process, the operation is not carried out;
when the problems in the analysis reasons exist in the current production process, acquiring the corresponding analysis reasons and solutions;
step SB 3: the non-out-of-tolerance is labeled i, and the non-out-of-tolerance value is labeled P i ，
According to the formula
Obtaining the mean value of tolerance not exceeding standard according to the formula
Obtaining a stable value alpha of the tolerance which is not over-standard, and the corrected value of the tolerance which is not over-standard is
；
Further, the method for adjusting the production process by the production control module comprises the following steps:
adjusting the current production process according to the analysis reason and the solution of the obtained overproof tolerance data;
obtaining a tolerance correction value P c According to a tolerance correction value P c The current process size is adjusted.
Further, the method for detecting the production product by the detection feedback module comprises the following steps:
setting the number of inspection batches, producing the inspection batches according to the set number of the inspection batches, detecting the produced inspection batches, and calculating the qualification rate of the inspection batches;
when the qualified rate of the inspection batch is lower than X1, acquiring a tolerance value, and feeding the acquired tolerance value back to the analysis module for correction;
and when the qualification rate of the inspection lot is not lower than X1, performing subsequent production.
Further, randomly extracting a sample for detection in the subsequent production process, and counting the qualified rate of the sample with the time span of X2;
when the qualified rate of the inspection batch is lower than X1, acquiring a tolerance value, and feeding the acquired tolerance value back to the analysis module for correction;
when the qualification rate of the test lot is not less than X1, no operation is performed.
A product data calling control method based on a process tolerance library comprises the following specific steps:
the method comprises the following steps: establishing a process tolerance library;
step two: extracting data from the process tolerance library according to the extracted keywords and the voice information;
step three: analyzing and correcting the acquired process tolerance data;
step four: adjusting the current production process according to the analysis result and the correction data;
step five: and (4) detecting the produced product, and feeding back the detection result to the third step for analysis and correction.
Compared with the prior art, the invention has the beneficial effects that: by describing the extracted keywords, the retrieval range is limited, the problem that some ideas are easier to dictate but are particularly difficult to write is avoided, the purpose that the calling result is most suitable for a user is guaranteed to the greatest extent, the problem that calling by only extracting the keywords has great limitation is solved, and the calling accuracy is improved; the obtained process tolerance data are analyzed, the data obtained from the process tolerance library are corrected, so that the corrected data can be improved by the current production process, and the subsequent data updating of the process tolerance library is facilitated by setting a calling model; by setting the inspection lot, the optimized production process is verified in a small range, and when the process has problems, the loss can be reduced to the maximum extent; and the detection result is fed back to the analysis module for correction, so that the production process is optimized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described below clearly and completely in conjunction with the embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the product data calling control system based on the process tolerance library, the extraction module, the analysis module, the production control module, the detection feedback module and the server;
all modules can realize data intercommunication;
the process tolerance library is used for storing tolerance data, acquiring the process tolerance data from a database of a user and the Internet, and removing duplication of the acquired data, namely deleting duplicated data, and storing the duplicate removed process tolerance data into the process tolerance library;
the extraction module is used for extracting data from a process tolerance library, and the specific method comprises the following steps:
step SA 1: acquiring extracted keywords, setting a voice recognition node, wherein the voice recognition node is used for recognizing voice content of a user, further describing the extracted keywords by using the voice recognition node, and marking information described by voice as voice information; by further describing the extracted keywords, the retrieval range can be limited, and the problem that some ideas are easier to dictate but are particularly difficult to write can be avoided;
step SA 2: establishing a calling model, inputting the extracted keywords and the voice information into the calling model, and obtaining corresponding process tolerance data; obtaining the purpose of extracting process tolerance data;
when the extracted process tolerance data is used for subsequent manufacturing production, finishing the extraction of the process tolerance data;
when the extracted process tolerance data is used for updating data in the process tolerance library, entering the next step;
step SA 3: comparing the process tolerance data used for updating with the obtained process tolerance data, wherein the obtained process tolerance data are the process tolerance data obtained by calling the model;
when the process tolerance data for updating is the same as the obtained process tolerance data, not updating;
replacing the obtained process tolerance data with the updated process tolerance data when the process tolerance data for updating is different from the obtained process tolerance data;
the method for establishing the calling model in the step SA2 comprises the following steps:
acquiring historical extraction data, wherein the historical extraction data comprises extraction keywords and voice information, and constructing an artificial intelligence model, wherein the artificial intelligence model comprises an error reverse propagation neural network, an RBF neural network and a deep convolution neural network; setting corresponding process tolerance data for the historical extraction data; dividing the history extracted data and the corresponding process tolerance data into a training set, a test set and a check set according to a set proportion; the set proportion comprises 2: 2: 1. 3: 2: 1 and 3: 2: 1; training, testing and verifying the artificial intelligent model through a training set, a testing set and a verifying set; marking the trained artificial intelligence model as a calling model;
by describing the extracted keywords, the retrieval range is limited, the problem that some ideas are easier to dictate but are particularly difficult to write is avoided, the purpose that the retrieval result is most consistent with the user is guaranteed to the greatest extent, the problem that retrieval is greatly limited only by extracting the keywords is solved, and the retrieval accuracy is improved;
the analysis module is used for analyzing the acquired process tolerance data, and the specific method comprises the following steps:
step SB 1: acquiring a process tolerance limit value and process tolerance data of a process specification, wherein the process tolerance limit value of the process specification is a value which cannot be exceeded by a process tolerance specified in the process specification, and dividing the acquired process tolerance data into overproof tolerance data and overproof tolerance data according to the process tolerance limit value;
step SB 2: the analysis reason of the data of the standard exceeding tolerance is obtained, because when the problem of the standard exceeding occurs, the problem analysis and summary can be carried out, the analysis reason can be directly collected and obtained, and the analysis reason of the obtained data of the standard exceeding tolerance is compared with the current production process;
when the existing production process has no problem in the analysis reason of the overproof tolerance data, the operation is not carried out;
when the problem in the analysis reason with the overproof tolerance data exists in the current production process, acquiring the corresponding analysis reason and solution;
step SB 3: marking the non-standard tolerance as i, wherein i =1, 2, … …, n is the number of the non-standard tolerances; marking the value of the non-exceeding tolerance as P i ，
According to the formula
Obtaining the mean value of tolerance not exceeding standard according to the formula
Obtaining a stable value alpha of the tolerance which is not over-standard, and the corrected value of the tolerance which is not over-standard is
；
The obtained process tolerance data are analyzed, the data obtained from the process tolerance library are corrected, so that the corrected data can be improved by the current production process, and the subsequent data updating of the process tolerance library is facilitated by setting a calling model;
the production control module is used for adjusting the production process according to the analysis result of the analysis module, and the specific method comprises the following steps:
acquiring analysis reasons and solutions of the overproof tolerance data, and adjusting the current production process according to the analysis reasons and solutions of the overproof tolerance data;
obtaining a tolerance correction value P c According to a tolerance correction value P c Adjusting the size of the current process;
the detection feedback module is used for detecting a production product and feeding back a detection result, and the specific method comprises the following steps:
setting the number of inspection batches, producing the inspection batches according to the set number of the inspection batches, detecting the produced inspection batches, and calculating the qualification rate of the inspection batches;
when the qualified rate of the inspection lot is lower than X1, wherein X1 is a threshold value, acquiring a tolerance value, and feeding the acquired tolerance value back to the analysis module for correction;
when the qualification rate of the inspection lot is not lower than X1, performing subsequent production;
randomly extracting a sample for detection in the production process, and counting the qualified rate of the sample with the time span of X2, wherein X2 is a threshold value;
when the qualified rate of the inspection batch is lower than X1, wherein X1 is a threshold value, acquiring a tolerance value, and feeding the acquired tolerance value back to the analysis module for correction;
when the qualification rate of the inspection lot is not lower than X1, not performing operation;
by setting the inspection lot, the optimized production process is verified in a small range, and when the process has problems, the loss can be reduced to the maximum extent; and the detection result is fed back to the analysis module for correction, so that the production process is optimized.
A product data calling control method based on a process tolerance library comprises the following specific steps:
the method comprises the following steps: establishing a process tolerance library;
acquiring process tolerance data from a database of a user and the Internet, removing duplication of the acquired data, and storing the duplicate-removed process tolerance data into a process tolerance database;
step two: extracting data from the process tolerance library according to the extracted keywords and the voice information;
step SA 1: acquiring extracted keywords, setting a voice recognition node, further describing the extracted keywords by using the voice recognition node, and marking information described by the voice as voice information;
step SA 2: extracting keywords and voice information, establishing a calling model, and inputting the extracted keywords and the voice information into the calling model to obtain corresponding process tolerance data; obtaining the purpose of extracting process tolerance data;
when the extracted process tolerance data is used for subsequent manufacturing production, finishing the extraction of the process tolerance data;
when the extracted process tolerance data is used for updating data in the process tolerance library, entering the next step;
step SA 3: comparing the process tolerance data for updating with the obtained process tolerance data;
when the process tolerance data for updating is the same as the obtained process tolerance data, not updating;
replacing the obtained process tolerance data with the updated process tolerance data when the process tolerance data for updating is different from the obtained process tolerance data;
step three: analyzing and correcting the acquired process tolerance data;
step SB 1: acquiring a process tolerance limit value and process tolerance data of a process specification, and dividing the acquired process tolerance data into standard tolerance data and standard tolerance data according to the process tolerance limit value;
step SB 2: acquiring analysis reasons of the data of the standard deviation and tolerance, and comparing the analysis reasons of the data of the standard deviation and tolerance with the current production process;
when the existing production process has no problem in the analysis reason of the overproof tolerance data, the operation is not carried out;
when the problem in the analysis reason with the overproof tolerance data exists in the current production process, acquiring the corresponding analysis reason and solution;
step SB 3: marking the non-exceeding tolerance as i, wherein i =1, 2, … …, n is the number of non-exceeding tolerances; marking the value of the non-exceeding tolerance as P i ，
According to the formula
Obtaining the mean value of tolerance not exceeding standard according to the formula
Obtaining a stable value alpha of the tolerance which is not over-standard, and the corrected value of the tolerance which is not over-standard is
；
Step four: adjusting the current production process according to the analysis result and the correction data;
acquiring analysis reasons and solutions of the overproof tolerance data, and adjusting the current production process according to the analysis reasons and solutions of the overproof tolerance data;
obtaining a tolerance correction value P c According to a tolerance correction value P c Adjusting the size of the current process;
step five: detecting the product, and feeding the detection result back to the third step for analysis and correction;
setting the number of inspection batches, producing the inspection batches according to the set number of the inspection batches, detecting the produced inspection batches, and calculating the qualification rate of the inspection batches;
when the qualified rate of the inspection lot is lower than X1, wherein X1 is a threshold value, acquiring a tolerance value, and feeding the acquired tolerance value back to the analysis module for correction;
when the qualification rate of the inspection lot is not lower than X1, performing subsequent production;
randomly extracting a sample for detection in the production process, and counting the qualified rate of the sample with the time span of X2, wherein X2 is a threshold value;
when the qualified rate of the inspection lot is lower than X1, wherein X1 is a threshold value, acquiring a tolerance value, and feeding the acquired tolerance value back to the analysis module for correction;
when the qualification rate of the test lot is not less than X1, no operation is performed.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows: establishing a process tolerance library; acquiring process tolerance data from a database of a user and the Internet, removing duplication of the acquired data, and storing the duplicate-removed process tolerance data into a process tolerance database; extracting data from the process tolerance library according to the extracted keywords and the voice information; acquiring extracted keywords, setting a voice recognition node, further describing the extracted keywords by using the voice recognition node, and marking information described by the voice as voice information; extracting keywords and voice information, establishing a calling model, and inputting the extracted keywords and the voice information into the calling model to obtain corresponding process tolerance data; obtaining the purpose of extracting process tolerance data; when the extracted process tolerance data is used for subsequent manufacturing production, finishing the extraction of the process tolerance data; when the extracted process tolerance data is used for updating data in the process tolerance library, comparing the process tolerance data used for updating with the obtained process tolerance data; when the process tolerance data for updating is the same as the obtained process tolerance data, not updating; replacing the obtained process tolerance data with the updated process tolerance data when the process tolerance data for updating is different from the obtained process tolerance data;
analyzing and correcting the acquired process tolerance data; acquiring a process tolerance limit value and process tolerance data of a process specification, and dividing the acquired process tolerance data into standard tolerance data and standard tolerance data according to the process tolerance limit value; acquiring analysis reasons of the data of the standard deviation and tolerance, and comparing the analysis reasons of the data of the standard deviation and tolerance with the current production process; when the existing production process has no problem in the analysis reason of the overproof tolerance data, the operation is not carried out;
when the problem in the analysis reason with the overproof tolerance data exists in the current production process, acquiring the corresponding analysis reason and solution; the non-out-of-tolerance is labeled i, and the non-out-of-tolerance value is labeled P i According to the formula
Obtaining the mean value of tolerance not exceeding standard according to the formula
Obtaining a stable value alpha of the tolerance which is not over-standard, and the corrected value of the tolerance which is not over-standard is
；
Adjusting the current production process according to the analysis result and the correction data; acquiring analysis reasons and solutions of the overproof tolerance data, and adjusting the current production process according to the analysis reasons and solutions of the overproof tolerance data; obtaining a tolerance correction value P c According to a tolerance correction value P c Adjusting the size of the current process; detecting the product, and feeding the detection result back to the third step for analysis and correction; setting the number of inspection batches, producing the inspection batches according to the set number of the inspection batches, detecting the produced inspection batches, and calculating the qualification rate of the inspection batches; when the qualified rate of the inspection batch is lower than X1, acquiring a tolerance value, and feeding the acquired tolerance value back to the analysis module for correction; when the qualification rate of the inspection lot is not lower than X1, performing subsequent production; randomly extracting samples for detection in the production process, counting the qualified rate of the samples with the time span of X2, obtaining a tolerance value when the qualified rate of a test batch is lower than X1, wherein X1 is a threshold value, and feeding the obtained tolerance value back to an analysis module for correction; when the qualification rate of the test lot is not less than X1, no operation is performed.
In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and there may be other divisions when the actual implementation is performed; the modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of this embodiment.
It will also be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above examples are only intended to illustrate the technical process of the present invention and not to limit the same, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical process of the present invention without departing from the spirit and scope of the technical process of the present invention.
Claims (7)
1. The product data calling control system based on the process tolerance library is characterized by comprising the process tolerance library, an extraction module, an analysis module, a production control module, a detection feedback module and a server;
the process tolerance library is used for storing tolerance data;
the extraction module is used for extracting data from the process tolerance library and sending the extracted data to the analysis module;
the analysis module analyzes the acquired process tolerance data and sends an analysis result to the production control module for process adjustment;
the method for analyzing the acquired process tolerance data comprises the following steps:
step SB 1: dividing the acquired process tolerance data into standard exceeding tolerance data and standard not exceeding tolerance data according to the process specification;
step SB 2: acquiring analysis reasons of the overproof tolerance data, and analyzing the current production process according to the analysis reasons;
when the problems in the reason are not analyzed in the current production process, the operation is not carried out;
when the problems in analysis reasons exist in the current production process, corresponding analysis reasons and solutions are obtained;
step SB 3: the non-out-of-tolerance is labeled i, and the non-out-of-tolerance value is labeled P i ，
According to the formula
Obtaining the mean value of tolerance not exceeding standard according to the formula
Obtaining a stable value alpha of the tolerance which is not over-standard, and the corrected value of the tolerance which is not over-standard is
；
The method for adjusting the production process by the production control module comprises the following steps:
adjusting the current production process according to the analysis reason and the solution of the obtained overproof tolerance data;
obtaining a tolerance correction value P c According to a tolerance correction value P c Adjusting the size of the current process;
the detection feedback module is used for detecting the production product and feeding back the detection result to the analysis module for analysis and correction.
2. The product data calling control system based on the process tolerance library according to claim 1, wherein the process tolerance data is acquired, the acquired data is de-duplicated, and the de-duplicated process tolerance data is stored in the process tolerance library.
3. The process tolerance library-based product data retrieval control system of claim 1, wherein the method of extracting data from the process tolerance library by the extraction module comprises:
step SA 1: obtaining extracted keywords, setting a voice recognition node, and further describing the extracted keywords by using the voice recognition node;
step SA 2: establishing a calling model, inputting the extracted keywords and the voice information into the calling model, and obtaining corresponding process tolerance data; obtaining the purpose of extracting process tolerance data;
the extraction of the process tolerance data is completed when the extracted process tolerance data is used for subsequent manufacturing production.
4. The product data calling control system based on the process tolerance library according to claim 3, wherein when the extracted process tolerance data is used for data updating in the process tolerance library, step SA3 is entered;
step SA 3: comparing the process tolerance data for updating with the obtained process tolerance data;
when the comparison data are the same, updating is not carried out;
when the comparison data is different, the process tolerance data for updating is replaced with the original process tolerance data.
5. The process tolerance library-based product data retrieval control system of claim 1, wherein the method of detecting the production product by the detection feedback module comprises:
setting the number of inspection batches, producing the inspection batches according to the set number of the inspection batches, detecting the produced inspection batches, and calculating the qualification rate of the inspection batches;
when the qualified rate of the inspection batch is lower than X1, acquiring a tolerance value, and feeding the acquired tolerance value back to the analysis module for correction;
and when the qualification rate of the inspection lot is not lower than X1, performing subsequent production.
6. The product data calling control system based on the process tolerance library of claim 5, wherein samples are randomly extracted for detection in the subsequent production process, and the sample qualification rate with the time span of X2 is counted;
when the qualified rate of the inspection batch is lower than X1, acquiring a tolerance value, and feeding the acquired tolerance value back to the analysis module for correction;
when the pass rate of the inspection lot is not less than X1, no operation is performed.
7. The product data calling control method based on the process tolerance library is characterized by comprising the following specific steps:
the method comprises the following steps: establishing a process tolerance library;
step two: extracting data from the process tolerance library according to the extracted keywords and the voice information;
step three: analyzing and correcting the acquired process tolerance data;
step four: adjusting the current production process according to the analysis result and the correction data;
step five: detecting the produced product, and feeding back the detection result to the third step for analysis and correction;
the method for analyzing the acquired process tolerance data comprises the following steps:
step SB 1: dividing the acquired process tolerance data into standard exceeding tolerance data and standard not exceeding tolerance data according to the process specification;
step SB 2: acquiring analysis reasons of the overproof tolerance data, and analyzing the current production process according to the analysis reasons;
when the problems in the reason are not analyzed in the current production process, the operation is not carried out;
when the problems in the analysis reasons exist in the current production process, acquiring the corresponding analysis reasons and solutions;
step SB 3: marking the tolerance not exceeding the standard as i, and marking the tolerance not exceeding the standardTolerance value is marked P i ，
According to the formula
Obtaining the mean value of tolerance not exceeding standard according to the formula
Obtaining a stable value alpha of the tolerance which is not over-standard, and the corrected value of the tolerance which is not over-standard is
；
The method for adjusting the production process comprises the following steps:
adjusting the current production process according to the analysis reason and the solution of the obtained overproof tolerance data;
obtaining a tolerance correction value P c According to a tolerance correction value P c The current process size is adjusted.
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