CN116911734A - Stamping die management method based on big data - Google Patents
Stamping die management method based on big data Download PDFInfo
- Publication number
- CN116911734A CN116911734A CN202310873643.6A CN202310873643A CN116911734A CN 116911734 A CN116911734 A CN 116911734A CN 202310873643 A CN202310873643 A CN 202310873643A CN 116911734 A CN116911734 A CN 116911734A
- Authority
- CN
- China
- Prior art keywords
- die
- production
- dies
- group
- inventory
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007726 management method Methods 0.000 title claims abstract description 45
- 238000004519 manufacturing process Methods 0.000 claims abstract description 279
- 238000012795 verification Methods 0.000 claims abstract description 48
- 238000005457 optimization Methods 0.000 claims abstract description 26
- 238000012216 screening Methods 0.000 claims abstract description 19
- 238000012544 monitoring process Methods 0.000 claims abstract description 14
- 238000013461 design Methods 0.000 claims abstract description 13
- 238000012163 sequencing technique Methods 0.000 claims abstract description 7
- 238000012423 maintenance Methods 0.000 claims description 44
- 238000000034 method Methods 0.000 claims description 25
- 238000004364 calculation method Methods 0.000 claims description 24
- 238000009434 installation Methods 0.000 claims description 16
- 238000004806 packaging method and process Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 10
- 238000004140 cleaning Methods 0.000 claims description 6
- 238000003860 storage Methods 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 3
- 238000012797 qualification Methods 0.000 claims description 3
- 230000001105 regulatory effect Effects 0.000 claims description 3
- 230000001502 supplementing effect Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 10
- 238000011161 development Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000013075 data extraction Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 238000012827 research and development Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 238000005299 abrasion Methods 0.000 description 1
- 238000010924 continuous production Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012856 packing Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Game Theory and Decision Science (AREA)
- Manufacturing & Machinery (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- General Factory Administration (AREA)
Abstract
The application relates to a stamping die management method based on big data, which comprises the following steps: establishing a mould management database, monitoring the working state of the existing stock mould, and maintaining the existing stock mould at regular time to generate stock mould working record information; acquiring a design scheme of a novel die in real time, sending the design scheme to a die manufacturing department for die trial production, sending the trial-produced novel die to a corresponding part production line for verification production, and acquiring verification production data of the novel die; and periodically collecting inventory mold work record information and verification production data of novel molds, calculating and determining the production efficiency A and the production cost B of each group of molds, sequencing and screening each group of molds to obtain each type of mold required by a user, and generating a mold inventory optimization scheme according to a preset mold inventory proportion. The application has the effects of intelligently optimizing the die stock and improving the stamping production efficiency.
Description
Technical Field
The application relates to the field of die management, in particular to a stamping die management method based on big data.
Background
An automobile accessory is a product that serves an automobile as well as individual units that make up the automobile as a whole. Automobile accessories are various, and with the improvement of the living standard of people, people consume automobiles more and more, and the market of the automobile accessories is becoming larger and larger. Automobile parts manufacturers have also evolved rapidly in recent years. The automobile parts are taken as the basis of the automobile industry and are necessary factors for supporting the continuous healthy development of the automobile industry. Particularly, the automobile industry is currently developing independent development and innovation, and a strong part system is needed to support. The requirement of the universal automobile parts rises year by year, and how to improve the production efficiency of the universal parts and the standard parts and save the production cost is a problem which needs to be solved by various manufacturing enterprises.
The stamping equipment always needs continuous production, the failure stop rate of the stamping equipment is an important factor for restricting the production and development of stamping parts, and the main reason is that the dies and vulnerable parts of the stamping equipment are not maintained or replaced in time, if maintenance work can greatly reduce the stop rate, however, the maintenance work in the stamping production process cannot play the due role due to the reasons of high mobility of technicians, forgetting to implement, manually recording maintenance content, maintaining by master and the like, so that the stop rate of the stamping equipment is high. In addition, when the automobile parts are produced, along with the continuous progress of design work, stamping equipment is continuously updated, so that stamping dies adopted in a certain stamping processing procedure of the same automobile part are of various types, and each group of dies are different in design and different in advantages, and therefore, on the basis of monitoring the use of the dies, optimizing the die stock is also a technical problem to be solved.
Aiming at the related technology, the use condition of the die is tracked and managed in a manual management mode at present, and the die stock cannot be optimized according to the specific use condition of the die, so that the die management efficiency is low, the stamping production cost is overhigh, and the production efficiency is low.
Disclosure of Invention
The application provides a stamping die management method based on big data, which aims to solve the problems that the use condition of a die is tracked and managed in a manual management mode, and the inventory of the die cannot be optimized according to the specific use condition of the die, so that the die management efficiency is low, the stamping production cost is too high and the production efficiency is too low.
In a first aspect, the present application provides a method for managing a stamping die based on big data, which adopts the following technical scheme:
a stamping die management method based on big data comprises the following steps:
establishing a mould management database, monitoring the working state of the existing stock mould, and maintaining the existing stock mould at regular time to generate stock mould working record information;
acquiring a design scheme of a novel die in real time, sending the design scheme to a die manufacturing department for die trial production, sending the trial-produced novel die to a corresponding part production line for verification production, and acquiring verification production data of the novel die;
and periodically collecting inventory mold work record information and verification production data of novel molds, calculating and determining the production efficiency A and the production cost B of each group of molds, sequencing and screening each group of molds to obtain each type of mold required by a user, and generating a mold inventory optimization scheme according to a preset mold inventory proportion.
Preferably, the establishing a mold management database specifically includes the following steps:
establishing a database to input characteristic information of an existing inventory mold, wherein the characteristic information comprises production date information, coding information, service life information, processed part information and work record information;
the method comprises the steps of supplementing inventory mold work record information generated after production work of each inventory mold in stock into characteristic information of the inventory mold in stock in real time;
packaging and storing the characteristic information of the scrapped die;
the design scheme of the novel die is received in real time, the characteristic information of the novel die is recorded after the novel die is manufactured in trial mode, and verification production data generated during verification production of the novel die are supplemented into the characteristic information of the novel die in real time.
Preferably, the monitoring the working state of the existing inventory mold, maintaining the existing inventory mold at regular time, and generating the working record information of the inventory mold specifically includes the following steps:
acquiring working states of an existing inventory mold in real time, wherein the working states comprise three states of idle state, working state and maintenance state;
acquiring the use times, the use duration and the product yield information of any die in real time when the die is in operation;
when any one of the using times, the using time and the product yield information of the die exceeds a preset using times threshold, a using time threshold or a product yield threshold, generating maintenance prompt information of the die and sending the maintenance prompt information to a manager for maintenance of the die;
after each mold use or maintenance, collecting mold work record data and/or maintenance information to generate inventory mold work record information.
Preferably, the step of sending the tested novel mold into a corresponding part production line for verification production, and the step of acquiring verification production data of the novel mold specifically comprises the following steps:
the tested novel die is sent to a corresponding part production line for preliminary installation and debugging;
after the installation and debugging are finished, product trial production is carried out, the novel die is installed and adjusted based on the trial production result until the yield of the trial production products of the part production line meets the preset trial production qualification threshold, and installation and adjustment data are recorded;
after the novel die is installed and regulated, starting a part production line for verification production;
monitoring the working state of a novel die, and stopping a part production line from generating maintenance prompt information of the die to a manager when any one of the using time length or the using time number of the novel die exceeds a preset using time number threshold or a using time length threshold of the novel die, so as to maintain the novel die;
repeating the steps until the novel die is scrapped after the novel die is maintained;
and collecting installation and adjustment data, work record data and maintenance information of the novel die, and packaging to generate verification production data.
Preferably, the periodic collection of the work record information of the stock mold and the verification production data of the novel mold, the calculation and determination of the production efficiency A and the production cost B of each group of molds specifically comprise the following steps:
periodically collecting inventory mold work record information and verification production data of the novel mold, wherein the collection period is set by a manager;
cleaning the collected stock mold work record information and verification production data of the novel mold, and extracting and obtaining various use data in the average service life cycle of each group of molds;
calculating the production efficiency A of each group of dies according to a preset production efficiency calculation formula;
and calculating the production cost B of each group of dies according to a preset production cost calculation formula.
Preferably, the production efficiency calculation formula is calculated as:
wherein C is the total number of good products produced in the average service life period of the group of dies, D is the average service life of the group of dies, C i For producing good product quantity when the group of dies is used for the ith time, d i The use time length is the i-th time of using the group of dies.
Preferably, the production cost calculation formula is:
wherein, C is the total number of good products produced in the average service life period of the group of the dies, K is the total number of products produced in the average service life period of the group of the dies, E is the material consumption cost in the average service life period of the group of the dies, F is the production cost of the group of the dies, G is the single maintenance cost of the group of the dies, and H is the maintenance frequency in the average service life period of the group of the dies; j is the equipment loss cost of single processing of the production equipment matched with the group of the dies, and the equipment loss cost is set by a manager.
Preferably, the generating the mould inventory optimization scheme according to the preset mould inventory proportion specifically includes the following steps:
sorting and screening the groups of dies according to the production efficiency A and the production cost B of the groups of dies to obtain a high-efficiency die, a low-cost die and a full-function die which correspond to the production of each part;
calculating the required quantity of various moulds according to the preset mould inventory proportion;
acquiring the existing inventory quantity of various types of moulds through a database, generating supplementary demand information based on moulds of which the inventory quantity does not meet the demand quantity according to the demand quantity of various types of moulds, generating maintenance demand information according to moulds of which the inventory quantity meets the demand quantity, and packaging the supplementary demand information and the maintenance demand information corresponding to various parts to generate a mould inventory optimization scheme.
Preferably, the sorting and screening are performed on the groups of dies according to the production efficiency a and the production cost B of each group of dies, so as to obtain the high-efficiency dies, the low-cost dies and the all-purpose dies corresponding to the production of each part, which specifically comprise the following steps:
according to the production efficiency A and the production cost B of each group of dies, sequencing each group of dies to generate a production efficiency sequence and a production cost sequence;
selecting a die with highest production efficiency as a high-efficiency die according to the production efficiency sequence;
selecting a die with the lowest production cost as a low-cost die according to the production cost sequence;
generating efficiency scores and cost scores for various dies on the production efficiency sequence and the production cost sequence based on the sorting scoring table according to a preset sorting scoring table, adding the efficiency scores and the cost scores of the dies in each group to obtain a comprehensive score, and sorting the dies in each group based on the comprehensive score to generate a comprehensive sequence;
and selecting the die with the highest comprehensive score as a full-function die according to the comprehensive sequence.
In a second aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
a computer readable storage medium storing a computer program capable of being loaded by a processor and performing any one of the methods described above.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the production efficiency and the production cost of each group of dies are calculated, so that classification screening is carried out on each group of dies, various die type labels are given, a die inventory optimization scheme is generated according to a preset die inventory proportion based on the types of each group of dies, intelligent tracking management is carried out on die inventory and the use condition of the dies, and the die management efficiency is improved;
2. on the basis of regularly optimizing the existing die stock, the verification production data of the novel die can be collected when the novel die is ground, an inventory optimization scheme aiming at the existing die and the novel die is generated, the intelligentization of die management is further improved, the die research and development and the inventory management are promoted to form linkage, the die group suitable for production can be matched according to the actual condition of an order during the part production, the stamping production efficiency is effectively improved, the stamping production cost is reduced on the premise of meeting the construction period of the order, and the effects of intelligently optimizing the die stock and improving the stamping production efficiency are achieved;
3. according to the requirements of users, the production data are verified by periodically collecting working record information of the inventory molds, the collected data information is cleaned in a data cleaning mode, the data are preprocessed, the accuracy of each item of use data in the average service life period of each group of molds is facilitated to be extracted, the data analysis and extraction difficulty is reduced, the accurate calculation of the production efficiency A and the production cost B of each group of molds is ensured, and further, the mold inventory optimization scheme is intelligently and efficiently planned and generated, so that the mold inventory optimization scheme is more fit with the actual production requirements of users;
4. the production cost and the production efficiency of each group of dies are used for generating a production efficiency sequence and a production cost sequence, so that a high-efficiency die and a low-cost die are determined, the efficiency score and the cost score of each group of dies are measured and calculated by introducing a scoring mechanism, the comprehensive score of each group of dies is determined, and a universal die is selected, so that a die inventory optimization scheme is determined according to a classification screening result, a user can determine a die set meeting the requirements of the user based on the matching of the working hour requirements of orders in different production time periods, the production efficiency of parts is improved as much as possible, the production cost of the parts is reduced, the die inventory scheme is optimized, the obsolete and the moderate die is eliminated, and the effects of intelligently optimizing the die inventory and improving the stamping production efficiency are achieved by effectively connecting the novel die to the ground when the existing die inventory structure is regularly optimized.
Drawings
FIG. 1 is a flow chart of a method of stamping die management in an embodiment of the application;
FIG. 2 is a flow chart of a method of creating a mold management database in an embodiment of the application;
FIG. 3 is a flow chart of a method for monitoring the operational status of an inventory mold in accordance with an embodiment of the application;
FIG. 4 is a flow chart of a method of collecting validated production data for a new mold in an embodiment of the present application;
FIG. 5 is a flow chart of a method for calculating the production efficiency and production cost of each set of dies in an embodiment of the application;
FIG. 6 is a flow chart of a method of generating a mold inventory optimization scheme in an embodiment of the application;
FIG. 7 is a flowchart of a method for sorting and screening groups of modules in an embodiment of the present application.
Detailed Description
The application is described in further detail below with reference to fig. 1-7.
The embodiment of the application discloses a stamping die management method based on big data. Referring to fig. 1, a stamping die management method based on big data includes the steps of:
s1, generating stock mould work record information: establishing a mould management database, monitoring the working state of the existing stock mould, and maintaining the existing stock mould at regular time to generate stock mould working record information;
s2, collecting verification production data of the novel die: acquiring a design scheme of a novel die in real time, sending the design scheme to a die manufacturing department for die trial production, sending the trial-produced novel die to a corresponding part production line for verification production, and acquiring verification production data of the novel die;
s3, generating a die inventory optimization scheme: and periodically collecting inventory mold work record information and verification production data of novel molds, calculating and determining the production efficiency A and the production cost B of each group of molds, sequencing and screening each group of molds to obtain each type of mold required by a user, and generating a mold inventory optimization scheme according to a preset mold inventory proportion. Through establishing a die management database, the stock die work record information of the existing stock die and the verification production data of the novel die are continuously collected and stored, the production efficiency and the production cost of each group of dies are calculated, the classification screening of each group of dies is further realized, the types labels corresponding to the groups of dies are endowed, a die stock optimization scheme is generated according to the preset die stock proportion based on the types of the groups of dies, intelligent tracking management of the die stock and the die use condition is realized, and the die management efficiency is improved.
On the basis of regularly optimizing the existing die stock, when a novel die is ground, verification production data of the novel die are collected, an inventory optimization scheme aiming at the existing die and the novel die is generated, the intelligent of die management is further improved, the die research and development and the inventory management are promoted to form linkage, a die set suitable for production can be matched according to the actual condition of an order during part production, the stamping production cost is effectively improved on the premise that the construction period of the order is met, and the effects of intelligently optimizing the die stock and improving the stamping production efficiency are achieved.
Referring to fig. 2, the above-mentioned creation of the mold management database specifically includes the following steps:
a1, establishing a database to input characteristic information of an existing inventory mold, wherein the characteristic information comprises production date information, coding information, service life information, processed part information and work record information;
a2, the characteristic information of the existing inventory mould is updated in a supplementary mode: the method comprises the steps of supplementing inventory mold work record information generated after production work of each inventory mold in stock into characteristic information of the inventory mold in stock in real time;
a3, packing and storing the characteristic information of the scrapped die;
a4, inputting characteristic information of the novel die: the design scheme of the novel die is received in real time, the characteristic information of the novel die is recorded after the novel die is manufactured in trial mode, and verification production data generated during verification production of the novel die are supplemented into the characteristic information of the novel die in real time. By establishing the mold management database, the method and the device can continuously collect, update and store the characteristic information of the existing inventory mold and the novel mold, and package and store the characteristic information of the scrapped mold, thereby being beneficial to improving the data accuracy of calculating the production cost and the production efficiency of various molds and being beneficial to accurately and efficiently generating a mold inventory optimization scheme.
Referring to fig. 3, the above-mentioned monitoring of the working state of the existing inventory mold, the maintenance of the existing inventory mold at regular time, and the generation of the inventory mold working record information specifically includes the following steps:
b1, acquiring working states of an existing inventory mold in real time, wherein the working states comprise three states of idle state, working state and maintenance state;
b2, acquiring the use times, the use duration and the product yield information of any die in real time when the die is in operation;
b3, maintaining the die: when any one of the using times, the using time and the product yield information of the die exceeds a preset using times threshold, a using time threshold or a product yield threshold, generating maintenance prompt information of the die and sending the maintenance prompt information to a manager for maintenance of the die;
and B4, generating stock mould work record information: after each mold use or maintenance, collecting mold work record data and/or maintenance information to generate inventory mold work record information. The work record data comprises data information such as using times information, using time length information, the number of produced products, the number of produced qualified products, the production yield of the products, material consumption data and the like. Through the steps, intelligent monitoring of inventory molds is realized, mold management efficiency is improved, and the effect of intelligent tracking management of the molds is achieved.
Referring to fig. 4, the above-mentioned method for feeding the new mold into the corresponding part production line for verification production, and collecting verification production data of the new mold specifically includes the following steps:
c1, performing preliminary installation and debugging on the novel die: the tested novel die is sent to a corresponding part production line for preliminary installation and debugging;
c2, product trial production is carried out, and installation and adjustment are carried out on the novel die: after the installation and debugging are finished, product trial production is carried out, the novel die is installed and adjusted based on the trial production result until the yield of the trial production products of the part production line meets the preset trial production qualification threshold, and installation and adjustment data are recorded;
and C3, starting a part production line to verify production: after the novel die is installed and regulated, starting a part production line for verification production;
and C4, monitoring the working state of the novel die and maintaining: monitoring the working state of a novel die, and stopping a part production line from generating maintenance prompt information of the die to a manager when any one of the using time length or the using time number of the novel die exceeds a preset using time number threshold or a using time length threshold of the novel die, so as to maintain the novel die;
c5, repeating the steps until the novel die is scrapped after the novel die is maintained;
and C6, packaging to generate verification production data: and collecting installation and adjustment data, work record data and maintenance information of the novel die, and packaging to generate verification production data. After the quality detection is finished, the novel mold trial production is installed on the corresponding part production line, installation debugging and trial production verification are carried out, verification production is carried out until the novel mold is scrapped, in the verification production process, installation adjustment data, work record data and maintenance information of the novel mold are collected, verification production data are generated in a packaging mode, the verification production test dimension of the novel mold is prolonged, the accuracy of the verification production data is improved, the phenomenon that the production cost and the production efficiency calculation error of the novel mold are caused to be larger due to the fact that data samples are single is avoided, the production performance of the novel mold of the group can be accurately obtained after the novel mold trial production of a certain automobile part is finished, the follow-up accurate measurement and calculation of the production cost and the production efficiency of the novel mold are facilitated, the data calculation accuracy is improved, the intelligent mold management is improved, the linkage of mold development and inventory management is promoted, and the effect that the production cost is saved due to intelligent optimization of mold inventory and the improvement of stamping production efficiency is achieved.
Referring to fig. 5, the above-mentioned periodic collection of stock mold work record information and verification production data of new mold, and calculation and determination of production efficiency a and production cost B of each group of mold specifically includes the following steps:
d1, periodically collecting inventory mold work record information and verification production data of a novel mold, wherein the collection period is set by a manager;
it should be noted that the feature information of the scrapped sealing and storing die with the same model is also in the extraction range; the more the number of the molds of the same model is, the more accurate the production efficiency A and the production cost B are calculated from each item of use data extracted from the characteristic information of the molds;
and D2, cleaning the acquired data to acquire various usage data of each group of dies: cleaning the collected stock mold work record information and verification production data of the novel mold, and extracting and obtaining various use data in the average service life cycle of each group of molds;
d3, calculating the production efficiency A of each group of dies: calculating the production efficiency A of each group of dies according to a preset production efficiency calculation formula;
and D4, calculating the production cost B of each group of dies: and calculating the production cost B of each group of dies according to a preset production cost calculation formula. According to the requirements of users, working records of inventory molds are periodically collected, production data are verified, collected data information is cleaned in a data cleaning mode, pretreatment of the data is achieved, accuracy of all usage data in average service life periods of all groups of molds is facilitated to be extracted, data analysis and extraction difficulty is reduced, accurate calculation of production efficiency A and production cost B of all groups of molds is guaranteed, and further an intelligent efficient planning generation mold inventory optimization scheme is achieved, so that the mold inventory optimization scheme is more fit with actual production requirements of users.
The production efficiency calculation formula is calculated as follows:
wherein C is the total number of good products produced in the average service life period of the group of dies, D is the average service life of the group of dies, C i For producing good product quantity when the group of dies is used for the ith time, d i The use time length is the i-th time of using the group of dies. The overall production efficiency in the average service life period of the die is calculated, and then the production efficiency of each group of dies is calculated by introducing the production efficiency of each time based on the work record data during each production time to carry out summation averaging, so that the production efficiency of each group of dies is calculated, the calculation accuracy of the production efficiency is improved, the calculated production efficiency is more attached to the actual production condition of the dies, the subsequent sorting screening of each group of dies is facilitated, and the effect of intelligently optimizing the die stock is achieved.
The production cost calculation formula is as follows:
wherein, C is the total number of good products produced in the average service life period of the group of the dies, K is the total number of products produced in the average service life period of the group of the dies, E is the material consumption cost in the average service life period of the group of the dies, F is the production cost of the group of the dies, G is the single maintenance cost of the group of the dies, and H is the maintenance frequency in the average service life period of the group of the dies; j is the equipment loss cost of single processing of the production equipment matched with the group of the dies, and the equipment loss cost is set by a manager. The actual production cost of each group of dies is calculated from multiple dimensions such as material loss, die cost, maintenance cost, adapter equipment loss and the like, so that the data calculation accuracy is improved, the calculated production cost is more attached to the actual production condition of the dies, the subsequent sorting and screening of each group of dies are facilitated, and the effect of intelligently optimizing die inventory is achieved.
In addition, the application introduces the equipment loss cost of single processing of the production equipment with the die adaptation, which is based on the different production equipment with the die adaptation of different groups, and the manufacturing cost also has larger difference, so that the calculation of the production cost of each group of dies is more reasonable and is close to actual production by introducing the equipment loss cost. For example, a certain group of dies is high in manufacturing cost for adapting to a certain production device, but the performance of the dies such as material loss, processing precision, processing efficiency and the like is quite excellent in the part processing process, but the production loss speed is high, the theoretical effective processing times are short in service life, and if the phenomena of high production efficiency and low production cost of the group of dies are easy to appear in calculation from the material loss, the die manufacturing cost and the maintenance cost, the data calculation is inaccurate, so that the die inventory optimization scheme is influenced.
Referring to fig. 6, generating a mold inventory optimization scheme according to a preset mold inventory ratio specifically includes the following steps:
and E1, sorting and screening the die groups: sorting and screening the groups of dies according to the production efficiency A and the production cost B of the groups of dies to obtain a high-efficiency die, a low-cost die and a full-function die which correspond to the production of each part;
e2, calculating the required quantity of various moulds: calculating the required quantity of various moulds according to a preset mould inventory proportion, wherein the mould inventory proportion is set by a manager;
e3, generating a die inventory optimization scheme: acquiring the existing inventory quantity of various types of moulds through a database, generating supplementary demand information based on moulds of which the inventory quantity does not meet the demand quantity according to the demand quantity of various types of moulds, generating maintenance demand information according to moulds of which the inventory quantity meets the demand quantity, and packaging the supplementary demand information and the maintenance demand information corresponding to various parts to generate a mould inventory optimization scheme. The screening classification of the various groups of dies is realized based on the production efficiency A and the production cost B of the various groups of dies, and the high-efficiency dies, the low-cost dies and the all-round dies corresponding to the production of the various parts are obtained, so that a user can match the order to a proper production die set under different application situations, and the stamping production efficiency is effectively improved and the stamping production cost is reduced on the premise of meeting the construction period of the order. The maintenance requirement information is to maintain the excessive number of the dies, namely, the dies are not replenished when the dies are scrapped due to abrasion, and the dies are replenished when the number of the die sets is lower than the required number.
Referring to fig. 7, the steps of sorting and screening the sets of molds according to the production efficiency a and the production cost B of the sets of molds to obtain the efficient mold, the low-cost mold and the universal mold corresponding to the production of each part specifically include the following steps:
f1, generating a production efficiency sequence and a production cost sequence: according to the production efficiency A and the production cost B of each group of dies, sequencing each group of dies to generate a production efficiency sequence and a production cost sequence;
f2, selecting a high-efficiency die: selecting a die with highest production efficiency as a high-efficiency die according to the production efficiency sequence;
f3, selecting a low-cost die: selecting a die with the lowest production cost as a low-cost die according to the production cost sequence;
f4, generating a comprehensive sequence: generating efficiency scores and cost scores for various dies on the production efficiency sequence and the production cost sequence based on the sorting scoring table according to a preset sorting scoring table, adding the efficiency scores and the cost scores of the dies in each group to obtain a comprehensive score, and sorting the dies in each group based on the comprehensive score to generate a comprehensive sequence;
f5, selecting a full-function die: and selecting the die with the highest comprehensive score as a full-function die according to the comprehensive sequence. It should be noted that the score corresponding to each sequence number is recorded in the ranking score table, for example, in this embodiment, the sequence 1 is 10 and the sequence 2 is 9. The number of the die sets of each type of the same part can be set according to the actual scale of a factory, and the number of the efficient die, the low-cost die and the full-function die of the same part in the embodiment of the application is 1. When all types of moulds for producing a certain part are a group of moulds, the selection quantity of all types of moulds can be increased according to the requirements for the stock diversity of the moulds. The production cost and the production efficiency of each group of dies are used for generating a production efficiency sequence and a production cost sequence, so that a high-efficiency die and a low-cost die are determined, the efficiency score and the cost score of each group of dies are measured and calculated by introducing a scoring mechanism, the comprehensive score of each group of dies is determined, and a universal die is selected, so that a die inventory optimization scheme is determined according to a classification screening result, a user can determine a die set meeting the requirements of the user based on the matching of the working hour requirements of orders in different production time periods, the production efficiency of parts is improved as much as possible, the production cost of the parts is reduced, the die inventory scheme is optimized, the obsolete and the moderate die is eliminated, and the effects of intelligently optimizing the die inventory and improving the stamping production efficiency are achieved by effectively connecting the novel die to the ground when the existing die inventory structure is regularly optimized.
The above-mentioned manager presets the mould stock proportion and still includes: the method comprises the steps of collecting historical order information and various types of die history extraction and use information for producing various parts, importing the information into a machine learning model for training to obtain a light-duty die proportion recommendation model, and further extracting current time information and part information by the light-duty die proportion recommendation model when an administrator sets the inventory proportion of various types of dies corresponding to various parts, matching and generating various types of die inventory proportion setting reference values corresponding to the current time of the parts, wherein the administrator can refer to the die inventory proportion setting of various parts. The method has the advantages that the generation requirement conditions of various parts in different seasons can be determined by collecting historical order information, the calling conditions of various types of dies in various seasons can be extracted by extracting use information through various types of dies for producing various parts, and then a light-duty die proportion recommendation model is formed through training, a die inventory proportion setting reference value can be generated based on matching of part types and current seasons, a manager can conveniently and scientifically and accurately set and adjust the die inventory proportion, and the inventory die can adapt to the processing production requirements of the parts in the current seasons.
The embodiment of the present application also discloses a computer readable storage medium storing a computer program capable of being loaded by a processor and executing the method as described above, the computer readable storage medium for example comprising: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the scope of the present application. It will be apparent that the described embodiments are merely some, but not all, embodiments of the application. Based on these embodiments, all other embodiments that may be obtained by one of ordinary skill in the art without inventive effort are within the scope of the application. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art may still combine, add or delete features of the embodiments of the present application or make other adjustments according to circumstances without any conflict, so as to obtain different technical solutions without substantially departing from the spirit of the present application, which also falls within the scope of the present application.
Claims (10)
1. The stamping die management method based on big data is characterized by comprising the following steps of:
establishing a mould management database, monitoring the working state of the existing stock mould, and maintaining the existing stock mould at regular time to generate stock mould working record information;
acquiring a design scheme of a novel die in real time, sending the design scheme to a die manufacturing department for die trial production, sending the trial-produced novel die to a corresponding part production line for verification production, and acquiring verification production data of the novel die;
and periodically collecting inventory mold work record information and verification production data of novel molds, calculating and determining the production efficiency A and the production cost B of each group of molds, sequencing and screening each group of molds to obtain each type of mold required by a user, and generating a mold inventory optimization scheme according to a preset mold inventory proportion.
2. The stamping die management method based on big data as claimed in claim 1, wherein the creating a die management database specifically comprises the steps of:
establishing a database to input characteristic information of an existing inventory mold, wherein the characteristic information comprises production date information, coding information, service life information, processed part information and work record information;
the method comprises the steps of supplementing inventory mold work record information generated after production work of each inventory mold in stock into characteristic information of the inventory mold in stock in real time;
packaging and storing the characteristic information of the scrapped die;
the design scheme of the novel die is received in real time, the characteristic information of the novel die is recorded after the novel die is manufactured in trial mode, and verification production data generated during verification production of the novel die are supplemented into the characteristic information of the novel die in real time.
3. The big data based stamping die management method as claimed in claim 1, wherein: the method for monitoring the working state of the existing inventory mould, and maintaining the existing inventory mould at regular time, and generating the inventory mould working record information specifically comprises the following steps:
acquiring working states of an existing inventory mold in real time, wherein the working states comprise three states of idle state, working state and maintenance state;
acquiring the use times, the use duration and the product yield information of any die in real time when the die is in operation;
when any one of the using times, the using time and the product yield information of the die exceeds a preset using times threshold, a using time threshold or a product yield threshold, generating maintenance prompt information of the die and sending the maintenance prompt information to a manager for maintenance of the die;
after each mold use or maintenance, collecting mold work record data and/or maintenance information to generate inventory mold work record information.
4. The big data based stamping die management method as claimed in claim 1, wherein: the method for verifying and producing the novel mould comprises the following steps of:
the tested novel die is sent to a corresponding part production line for preliminary installation and debugging;
after the installation and debugging are finished, product trial production is carried out, the novel die is installed and adjusted based on the trial production result until the yield of the trial production products of the part production line meets the preset trial production qualification threshold, and installation and adjustment data are recorded;
after the novel die is installed and regulated, starting a part production line for verification production;
monitoring the working state of a novel die, and stopping a part production line from generating maintenance prompt information of the die to a manager when any one of the using time length or the using time number of the novel die exceeds a preset using time number threshold or a using time length threshold of the novel die, so as to maintain the novel die;
repeating the steps until the novel die is scrapped after the novel die is maintained;
and collecting installation and adjustment data, work record data and maintenance information of the novel die, and packaging to generate verification production data.
5. The big data based stamping die management method as claimed in claim 1, wherein: the method comprises the steps of periodically collecting inventory mold work record information and verification production data of novel molds, calculating and determining production efficiency A and production cost B of each group of molds, and specifically comprising the following steps of:
periodically collecting inventory mold work record information and verification production data of the novel mold, wherein the collection period is set by a manager;
cleaning the collected stock mold work record information and verification production data of the novel mold, and extracting and obtaining various use data in the average service life cycle of each group of molds;
calculating the production efficiency A of each group of dies according to a preset production efficiency calculation formula;
and calculating the production cost B of each group of dies according to a preset production cost calculation formula.
6. The big data based stamping die management method as claimed in claim 1, wherein: the production efficiency calculation formula is calculated as follows:
wherein C is the total number of good products produced in the average service life period of the group of dies, D is the average service life of the group of dies, C i For producing good product quantity when the group of dies is used for the ith time, d i The use time length is the i-th time of using the group of dies.
7. The big data based stamping die management method as claimed in claim 1, wherein: the production cost calculation formula is as follows:
wherein, C is the total number of good products produced in the average service life period of the group of the dies, K is the total number of products produced in the average service life period of the group of the dies, E is the material consumption cost in the average service life period of the group of the dies, F is the production cost of the group of the dies, G is the single maintenance cost of the group of the dies, and H is the maintenance frequency in the average service life period of the group of the dies; j is the equipment loss cost of single processing of the production equipment matched with the group of the dies, and the equipment loss cost is set by a manager.
8. The big data based stamping die management method as claimed in claim 1, wherein: the method for generating the die stock optimization scheme according to the preset die stock proportion specifically comprises the following steps of:
sorting and screening the groups of dies according to the production efficiency A and the production cost B of the groups of dies to obtain a high-efficiency die, a low-cost die and a full-function die which correspond to the production of each part;
calculating the required quantity of various moulds according to the preset mould inventory proportion;
acquiring the existing inventory quantity of various types of moulds through a database, generating supplementary demand information based on moulds of which the inventory quantity does not meet the demand quantity according to the demand quantity of various types of moulds, generating maintenance demand information according to moulds of which the inventory quantity meets the demand quantity, and packaging the supplementary demand information and the maintenance demand information corresponding to various parts to generate a mould inventory optimization scheme.
9. The big data based stamping die management method as claimed in claim 8, wherein: according to the production efficiency A and the production cost B of each group of dies, sorting and screening are carried out on each group of dies to obtain a high-efficiency die, a low-cost die and a full-function die corresponding to the production of each part, wherein the method specifically comprises the following steps:
according to the production efficiency A and the production cost B of each group of dies, sequencing each group of dies to generate a production efficiency sequence and a production cost sequence;
selecting a die with highest production efficiency as a high-efficiency die according to the production efficiency sequence;
selecting a die with the lowest production cost as a low-cost die according to the production cost sequence;
generating efficiency scores and cost scores for various dies on the production efficiency sequence and the production cost sequence based on the sorting scoring table according to a preset sorting scoring table, adding the efficiency scores and the cost scores of the dies in each group to obtain a comprehensive score, and sorting the dies in each group based on the comprehensive score to generate a comprehensive sequence;
and selecting the die with the highest comprehensive score as a full-function die according to the comprehensive sequence.
10. A computer-readable storage medium, characterized by: a computer program stored which can be loaded by a processor and which performs the method according to any one of claims 1-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310873643.6A CN116911734A (en) | 2023-07-17 | 2023-07-17 | Stamping die management method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310873643.6A CN116911734A (en) | 2023-07-17 | 2023-07-17 | Stamping die management method based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116911734A true CN116911734A (en) | 2023-10-20 |
Family
ID=88362439
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310873643.6A Pending CN116911734A (en) | 2023-07-17 | 2023-07-17 | Stamping die management method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116911734A (en) |
-
2023
- 2023-07-17 CN CN202310873643.6A patent/CN116911734A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111369070B (en) | Multimode fusion photovoltaic power prediction method based on envelope clustering | |
| CN109376924A (en) | A kind of method, apparatus, equipment and the readable storage medium storing program for executing of material requirements prediction | |
| CN112749840B (en) | Method for acquiring energy efficiency characteristic index reference value of thermal power generating unit | |
| CN108389069A (en) | Top-tier customer recognition methods based on random forest and logistic regression and device | |
| CN113515512A (en) | Quality control and improvement method for industrial internet platform data | |
| CN110109431B (en) | Intelligent acquiring system for OEE information of die casting machine | |
| CN114638476A (en) | Water conservancy integrated operation and maintenance management method and system | |
| CN108364191A (en) | Top-tier customer Optimum Identification Method and device based on random forest and logistic regression | |
| CN111539845A (en) | Enterprise environment-friendly management and control response studying and judging method based on power consumption mode membership grade | |
| CN112488507A (en) | Expert classification portrait method and device based on clustering and storage medium | |
| CN112686491A (en) | Enterprise power data analysis method based on power consumption behavior | |
| CN113627860A (en) | Warehouse and warehousing integrated intelligent management system and management method based on artificial intelligence | |
| CN117576823B (en) | Queuing and calling system terminal | |
| CN116911734A (en) | Stamping die management method based on big data | |
| CN116562599A (en) | Efficient intelligent factory scheduling system based on deep learning | |
| CN106779245A (en) | Civil aviaton's needing forecasting method and device based on event | |
| CN110738423B (en) | Comprehensive efficiency evaluation method for rolling and connecting equipment | |
| CN114676931A (en) | Electric quantity prediction system based on data relay technology | |
| CN114155072A (en) | Financial prediction model construction method and system based on big data analysis | |
| CN112308340A (en) | Power data processing method and device | |
| CN112529712A (en) | Modeling method and system for user operation analysis RFM | |
| CN111563665A (en) | Voltage quality management method | |
| JP2001243401A (en) | Order receipt prediction system | |
| CN111833127A (en) | Product cost quotation management system | |
| CN110689161A (en) | Method for realizing photovoltaic power generation power prediction model with reusability |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |