Multi-angle mold production and manufacturing management system
By designing a multi-angle mold production and manufacturing management system, the problem that the existing system cannot reasonably plan and effectively control the mold production quality is solved, real-time control and quality analysis of the mold production process is achieved, and production efficiency and information transparency are improved.
Patent Information
- Application Number
- CN202411734759.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-28
- Publication Date
- 2025-03-14
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing mold production management system lacks overall planning and system design, cannot reasonably plan the mold production process, and is difficult to effectively control the production quality.
Design a multi-angle mold production and manufacturing management system, including data acquisition module, production planning management module, production scheduling management module, material management module and quality control management module, and realize quality control of the mold production process through real-time data acquisition and production data analysis.
Real-time control and quality analysis of the mold production process is achieved, manual intervention is reduced, and the transparency and traceability of production information is improved, and the standardization and efficiency of the production process is promoted.
Smart Images
Figure CN119624239A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of manufacturing management of mold production, and in particular to a multi-angle mold production and manufacturing management system. Background Art
[0002] There is an urgent need to start a new engine driven by transformation and innovation, comprehensively promote the upgrading of industrial structure, promote high-quality development of manufacturing industry and build a strong manufacturing country. However, in the existing mold production companies, there is still a long way to go to achieve the goal of "first-class management". The production and manufacturing execution system is not a simple information project, but a reconstruction of enterprise management. It is necessary to better integrate information and management. The existing mold production management system lacks overall planning and system design for information construction, cannot reasonably plan mold production, and cannot effectively control mold production quality. Summary of the invention
[0003] The purpose of the present invention is to provide a multi-angle mold production and manufacturing management system to control the specific process of mold production and conduct real-time quality analysis in combination with actual production data, thereby achieving quality control of the mold production process.
[0004] To achieve the above object, the present invention provides a multi-angle mold production and manufacturing management system, comprising the following steps:
[0005] Data acquisition module, production planning management module, production scheduling management module, material management module, quality control management module;
[0006] The data acquisition module collects real-time data of the production process, including production data, equipment information, production information and energy consumption data;
[0007] The production plan management module is responsible for setting the deadline for the production process, including long-term production plan management, monthly production plan, and weekly production plan;
[0008] The production scheduling management module adjusts the weekly production plan management according to the actual production volume to meet the needs of the monthly production plan;
[0009] The material management module includes the management of raw materials, auxiliary materials, tooling, spare parts, as well as the storage of daily production output and data recording;
[0010] The quality control management module includes quality analysis and quality management.
[0011] Preferably, the specific contents collected by the data collection module are as follows:
[0012] Production data collection includes equipment information collection and production information collection. Equipment information collection includes equipment operating status, machine startup information, start-stop fault information, and effective operation rate; production information collection mainly includes material consumption data and product data; energy consumption data collection includes energy consumption information.
[0013] Preferably, the specific components of the production plan management module are as follows:
[0014] The long-term production plan includes order product information, order quantity and completion date. The long-term plan is broken down to obtain the monthly production plan, which is divided according to different production departments. Each department sets a weekly production plan on a monthly basis. The weekly production plan includes production days, production teams and production working hours.
[0015] Preferably, the specific composition of the production scheduling management module is as follows: after the end of daily production, each production department provides the material management module with corresponding new production data, and adjusts the weekly production plan based on the new production data.
[0016] Preferably, the specific process of the quality management module is as follows:
[0017] Quality analysis: The types of waste products caused by molds include sticking, cracking, aging, and falling off of mold coating. The factors affecting the mold printing volume include the stamping force, stamping time, equipment trouble-free operation time, and mold batch. The above factors are selected as the characteristic quantities affecting the mold printing volume to calculate the quality;
[0018] Select samples to form a row matrix u (0) , the formula for forming non-negative historical time series data is as follows:
[0019]
[0020] where u (0) is a non-negative sequence, n is data, and the cumulative generation operation is used to obtain a monotonically increasing sequence u (1) :
[0021] u (1) = {u (1) (1),u (1) (2), ..., u (1) (n)};
[0022]
[0023] Generate sequence u (1) The mean sequence z (1) , the formula is as follows:
[0024] z (1) ={z (1) (1), z(1) (2),...,z (1) (n)};
[0025] In the above formula, z (1) (k) is the average value of adjacent data, and the formula is as follows:
[0026] z (1) (k) = {0.5u (1) (k), 0.5u (1) (k-1), ..., k = 2, 3, ... n.}; the formula of the least squares estimation sequence of the grey differential equation is as follows:
[0027] u (0) (k)+az (1) (k) = b;
[0028] In the above formula, a is the development coefficient, b is the endogenous control gray number;
[0029] The whitening equation is as follows:
[0030]
[0031] In the above formula, [a, b] T is the calculation parameter, the specific formula is as follows:
[0032] [a, b] T =(B T B) -1 B T Y;
[0033] in
[0034]
[0035] Substituting the above into the whitening equation, the formula at time k is as follows:
[0036]
[0037] Establish the grey model formula:
[0038]
[0039] The predicted value of the original data at time k+H is as follows:
[0040]
[0041] The formula for calculating the difference is as follows:
[0042]
[0043] The total error in the weight matrix of the hidden layer and the output layer is calculated, and the formula is as follows:
[0044]
[0045] The simplified formula is as follows:
[0046]
[0047] Comparing the matrix on the right with the who matrix in BP, it is the torque of who, and the formula is as follows:
[0048] e who =who T e;
[0049] Calculate the error of the activation function network, the formula is as follows:
[0050]
[0051] All output nodes O covered by function E i(i＝1....n) The specific formula is as follows
[0052]
[0053] In the derivation, the formula is inserted as follows:
[0054]
[0055] Replace t i -o i The formula is as follows:
[0056]
[0057] t k The target value is set to a constant, and the formula is as follows:
[0058]
[0059] Output node o k , calculate the sum of the sigmoid function English weighted input signal, replacing o k , the formula is as follows:
[0060]
[0061] In the above formula, m is the number of hidden nodes, and the formula is as follows:
[0062]
[0063]
[0064] Get w kj The difference characteristic formula is as follows:
[0065]
[0066] In the above formula, w ki are the difference features corresponding to different hidden nodes;
[0067] Quality management: When the actual production results do not correspond to the results of quality analysis, an early warning will be issued to prompt management personnel to conduct corresponding inspections and solve problems in production equipment.
[0068] Therefore, the present invention adopts the above-mentioned multi-angle mold production and manufacturing management system, which has the following advantages:
[0069] In the present invention, the system provides real-time data collection, reduces manual intervention, makes production information more transparent, and makes the production process traceable. The manufacturing execution system can trace the entire process of finished products, materials, energy and other information, realizes a closed-loop management, combines the gray system theoretical model for prediction, realizes the control of production quality, promotes the standardization of personnel management in the production process, reduces duplication of work, and also plays a certain supervisory role in the work of personnel. Production data is more timely and accurate. Through production
[0070] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS
[0071] Figure 1 This is a system structure diagram of a multi-angle mold production and manufacturing management system of the present invention;
[0072] Figure 2 This is a flow chart of quality analysis in a system of a multi-angle mold production and manufacturing management system of the present invention. DETAILED DESCRIPTION
[0073] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations. The specific model specifications need to be selected and determined according to the actual specifications of the device, and the specific selection calculation method adopts the existing technology in the field, so it will not be described in detail.
[0074] Example
[0075] like Figure 1-Figure 2As shown, the present invention provides a multi-angle mold production and manufacturing management system, which specifically includes: a data acquisition module, a production planning management module, a production scheduling management module, a material management module, and a quality control management module;
[0076] The data acquisition module collects real-time data of the production process, including production data, equipment information, production information and energy consumption data;
[0077] The production plan management module is responsible for setting the deadline for the production process, including long-term production plan management, monthly production plan, and weekly production plan;
[0078] The production scheduling management module adjusts the weekly production plan management according to the actual production volume to meet the needs of the monthly production plan;
[0079] The material management module includes the management of raw materials, auxiliary materials, tooling, spare parts, as well as the storage of daily production output and data recording.
[0080] The quality control management module includes quality analysis and quality management.
[0081] The specific contents collected by the data collection module are as follows:
[0082] Production data collection includes equipment information collection and production information collection. Equipment information collection mainly refers to equipment operating status, machine startup information, start-stop fault information, and effective operation rate; production information collection mainly includes material consumption data and finished product data; energy consumption data includes energy consumption information, specifically water, electricity, steam, etc.
[0083] The specific components of the production planning management module are as follows:
[0084] The long-term production plan includes order product information, order quantity and completion date. The long-term plan is decomposed to obtain the monthly production plan, which is divided according to different production departments. Each department sets a weekly production plan on a monthly basis. The weekly production plan includes production days, production teams and production hours.
[0085] The specific composition of the production scheduling management module is as follows: After the daily production is completed, each production department provides the corresponding new production data to the material management module and adjusts the weekly production plan based on the new production data.
[0086] Quality analysis: The types of waste products caused by molds include sticking, cracking, aging, and falling off of mold coating. The factors affecting the mold printing volume include the stamping force, stamping time, equipment trouble-free operation time, and mold batch. The above factors are selected as the characteristic quantities affecting the mold printing volume. The quality is calculated and the selected samples are formed into a row matrix u (0) , the formula for forming non-negative historical time series data is as follows:
[0087]
[0088] where u (0) is a non-negative sequence, n is data, and the cumulative generation operation is used to obtain a monotonically increasing sequence u (1) :
[0089] u (1) = {u (1) (1),u (1) (2), ..., u (1) (n)}
[0090]
[0091] Generate sequence u (1) The mean sequence z (1) ,
[0092] z (1) ={z (1) (1), z (1) (2), ..., z (1) (n)}
[0093] In the above formula, z (1) (k) is the average value of adjacent data, and the formula is as follows:
[0094] z (1) (k) = {0.5u (1) (k), 0.5u (1) (k-1),...,k=2,3,...n.}
[0095] The formula of the least squares estimation sequence of the grey-grey differential equation is as follows:
[0096] u (0) (k)+az (1) (k) = b
[0097] In the above formula, a is the development coefficient, b is the endogenous control gray number;
[0098] The whitening equation is as follows:
[0099]
[0100] In the above formula, [a, b] T is the calculation parameter, the specific formula is as follows:
[0101] [a, b] T =(B T B) -1 B T Y
[0102] in
[0103] Y=[u (0) (2),u (0) (3),...,u (0) (n)] T
[0104]
[0105] Substituting the above into the whitening equation, the formula at time k is as follows:
[0106]
[0107] Establish the grey model formula:
[0108]
[0109] The predicted value of the original data at time k+H is as follows:
[0110]
[0111] The formula for calculating the difference is as follows:
[0112]
[0113] The total error in the weight matrix of the hidden layer and the output layer is calculated, and the formula is as follows:
[0114]
[0115] The simplified formula is as follows:
[0116]
[0117] Comparing the matrix on the right with the who matrix in BP, it is the torque of who, and the formula is as follows:
[0118] e who =who T ·e
[0119] Calculate the error of the activation function network, the formula is as follows:
[0120]
[0121] All output nodes O covered by function E i(i＝1....n) The specific formula is as follows
[0122]
[0123] In the derivation, the formula is inserted as follows:
[0124]
[0125] Replace t i -o i The formula is as follows:
[0126]
[0127] t k The target value is set to a constant, and the formula is as follows:
[0128]
[0129] Output node o k , calculate the sum of the sigmoid function English weighted input signal, replacing o k , the formula is as follows:
[0130]
[0131] In the above formula, m is the number of hidden nodes, and the formula is as follows:
[0132]
[0133]
[0134] Get w kj The difference characteristic formula is as follows:
[0135]
[0136] In the above formula, w ki are the difference features corresponding to different hidden nodes;
[0137] Quality management: The integrated data collection module will issue an early warning when the actual production results do not correspond to the results obtained from the quality analysis, prompting management personnel to conduct corresponding inspections and resolve problems in the production equipment.
[0138] Therefore, the present invention adopts a multi-angle mold production and manufacturing management system to control the specific process of mold production, and combines actual production data to perform real-time quality analysis, thereby achieving quality control of the mold production process.
[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.
Claims
1. A multi-angle mold production and manufacturing management system, characterized in that: Specifically include: Data acquisition module, production planning management module, production scheduling management module, material management module, quality control management module; The data acquisition module collects real-time data of the production process, including production data, equipment information, production information and energy consumption data; The production plan management module is responsible for setting the deadline for the production process, including long-term production plan management, monthly production plan, and weekly production plan; The production scheduling management module adjusts the weekly production plan management according to the actual production volume to meet the needs of the monthly production plan; The material management module includes the management of raw materials, auxiliary materials, tooling, spare parts, as well as the storage of daily production output and data recording; The quality control management module includes quality analysis and quality management.
2. A multi-angle mold production and manufacturing management system according to claim 1, characterized in that: The specific contents collected by the data collection module are as follows: Production data collection includes equipment information collection and production information collection. Equipment information collection includes equipment operating status, machine startup information, start-stop fault information, and effective operation rate; production information collection mainly includes material consumption data and product data; energy consumption data collection includes energy consumption information.
3. The multi-angle mold production and manufacturing management system according to claim 2 is characterized by: The specific components of the production plan management module are as follows: The long-term production plan includes order product information, order quantity and completion date. The long-term plan is broken down to obtain the monthly production plan, which is divided according to different production departments. Each department sets a weekly production plan on a monthly basis. The weekly production plan includes production days, production teams and production working hours.
4. The multi-angle mold production and manufacturing management system according to claim 3 is characterized by: The specific composition of the production scheduling management module is as follows: after the daily production is completed, each production department provides the corresponding new production data to the material management module, and adjusts the weekly production plan according to the new production data.
5. The multi-angle mold production and manufacturing management system according to claim 1, characterized in that: The specific process of the quality management module is as follows: Quality analysis: The types of waste products caused by molds include sticking, cracking, aging, and falling off of mold coating. The factors affecting the mold printing volume include the stamping force, stamping time, equipment trouble-free operation time, and mold batch. The above factors are selected as the characteristic quantities affecting the mold printing volume to calculate the quality; Select samples to form a row matrix u (0) , the formula for forming non-negative historical time series data is as follows: where u (0) is a non-negative sequence, n is data, and the cumulative generation operation is used to obtain a monotonically increasing sequence u (1) : u (1) ={u (1) (1),u (1) (2),...,u (1) (n)}; Generate sequence u( 1) The mean sequence z (1) , the formula is as follows: With (1) ={z (1) (1),with (1) (2),...,of (1) (n)}; In the above formula, z (1) (k) is the average value of adjacent data, and the formula is as follows: z (1) (k)={0.5u (1) (k),0.5u (1) (k-1),...,k=2,3,...n.}; The formula of the least squares estimation sequence of the grey-grey differential equation is as follows: you (0) (k)+az (1) (k)=b; In the above formula, a is the development coefficient, b is the endogenous control gray number; The whitening equation is as follows: In the above formula, [a, b] T is the calculation parameter, the specific formula is as follows: [a,b] T =(B T B) -1 B T Y; in Y=[u (0) (2),u (0) (3),...,u (0) (n)] T Substituting the above into the whitening equation, the formula at time k is as follows: Establish the grey model formula: The predicted value of the original data at time k+H is as follows: The formula for calculating the difference is as follows: The total error in the weight matrix of the hidden layer and the output layer is calculated, and the formula is as follows: The simplified formula is as follows: Comparing the matrix on the right with the who matrix in BP, it is the torque of who, and the formula is as follows: e who =who T ·e; Calculate the error of the activation function network, the formula is as follows: All output nodes O covered by function E i(i＝1....n) The specific formula is as follows In the derivation, the formula is inserted as follows: Replace t i -o i The formula is as follows: t k The target value is set to a constant, and the formula is as follows: Output node o k , calculate the sum of the sigmoid function English weighted input signal, replacing o k , the formula is as follows: In the above formula, m is the number of hidden nodes, and the formula is as follows: Get w kj The difference characteristic formula is as follows: In the above formula, w ki are the difference features corresponding to different hidden nodes; Quality management: When the actual production results do not correspond to the results of quality analysis, an early warning will be issued to prompt management personnel to conduct corresponding inspections and solve problems in production equipment.