Machining equipment yield prediction system
By real-time acquisition of three-color light signal data of machine tool, the comprehensive efficiency of the calculation equipment is realized, and the output prediction and resource optimization of machining equipment are solved, and the problems of poor real-time and low efficiency of output prediction in the existing technology are improved, and the production efficiency and resource allocation are improved. , reducing monitoring costs.
Patent Information
- Application Number
- CN202411159225.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-22
- Publication Date
- 2024-12-20
AI Technical Summary
The existing machining equipment output prediction methods have problems such as poor real-time, low efficiency and high monitoring costs, which leads to the inability to accurately predict output and schedule production, affecting production efficiency.
A production forecasting system for machining equipment is designed, including data acquisition module, OEE calculation module and output prediction and production schedule module. By collecting the signal data of three-color lamps of the machine tool in real time, calculating the comprehensive efficiency of the equipment, and combining other production information to make production forecasts and resource optimization to automatically generate production scheduling strategies.
Real-time and accurate production forecasts and production schedules are realized, production efficiency is improved, resource allocation is optimized, monitoring costs are reduced, contract extension is avoided, and customer needs are met.
Smart Images
Figure CN119168203A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the field of intelligence and informatization, and particularly relates to a production prediction system for machining equipment. Background Art
[0002] Production forecasting is very important in the machining production process, which helps factories to reasonably allocate resources to reduce the factory's production costs. At present, the machining industry usually uses manual inspections and the installation of complex and diverse sensors to obtain basic machine tool data, and then analyzes the impact of different factors on production, applies mathematical models and machine learning algorithms to analyze large amounts of data, and establishes models to predict future production. However, this production forecasting model has the problems of no real-time performance, low efficiency, and high monitoring costs. In addition, in the face of problems such as waste of manpower and material resources caused by workers' slacking during production, it is impossible to generate accurate data records, which affects the accuracy of the prediction model and makes it difficult to obtain scientific and accurate production and scheduling forecast results. Summary of the invention
[0003] The technical problem to be solved by the present invention is that the existing yield prediction method has poor real-time performance, low efficiency and high monitoring cost.
[0004] To this end, the present invention provides a machining equipment output prediction system.
[0005] The technical solution adopted by the present invention to solve its technical problem is:
[0006] A machining equipment output prediction system, comprising:
[0007] A data acquisition module, which is used to obtain and process the signal data of the three-color lamp of the machine tool;
[0008] An OEE calculation module, which obtains the real-time three-color light data processed by the data acquisition module and calculates the overall efficiency of the equipment;
[0009] The production forecasting and scheduling module calculates the measured production according to the calculation results of the OEE operation module, and automatically summarizes different machining strategies.
[0010] Furthermore, the data acquisition module is provided with a switch quantity acquisition module and a network communication module. The switch quantity acquisition module is used to detect signal changes on the production line and collect pulse signals generated when the CNC machine tool is working. The network communication module is used to establish communication with the three-color light of the machine tool to realize signal transmission.
[0011] Furthermore, the data acquisition module is provided with an algorithm business logic layer, which defines the meaning of the three-color light as a variable and establishes corresponding relationships with different digital pulse signal data. After receiving the digital pulse signal, it is compared and judged to obtain the current state of the three-color light and the operation of the machine tool, and the corresponding variables and receiving time data are stored in the database.
[0012] Furthermore, the comprehensive efficiency of the equipment is related to the total machining production time, the sum of each abnormal downtime, the time to process a product under ideal conditions, the total output and the number of unqualified products. The OEE calculation module takes a time interval, calls the collected three-color light data and the statistical total output and unqualified number data within the time interval, performs preliminary data processing according to the meaning of the lights, removes the normal shutdown time of the production line, and obtains the total machining production time; adds up each abnormal downtime to obtain the sum of each abnormal downtime; within the time interval, obtains the shortest time for processing a product, excluding the sum of each abnormal downtime for processing this product, as the time for the equipment to process a product under the current ideal condition.
[0013] Furthermore, the calculation formula of the equipment overall efficiency is as follows: OEE=F×K×P, wherein F is the equipment utilization rate, K is the performance utilization rate, and P is the qualified product rate.
[0014] Furthermore, the predicted output is related to the specified time t, OEE, and the time E for processing a product under ideal conditions.
[0015] Furthermore, it also includes a production line resource optimization module, which obtains the three-color light data processed by the data acquisition module, combines other production information to compare and analyze the proportion of various types of downtime reasons for each equipment within the total production time, and provides a reference for the formulation of processing strategies in the production forecast and scheduling module.
[0016] Furthermore, the production forecasting and scheduling module obtains various data of the production order, and obtains the production cycle or scheduling plan in combination with the predicted production calculation formula.
[0017] The beneficial effect of the present invention is that the system can be linked with the original workshop management system, with little modification to the original system. It only needs to install the corresponding module to collect the three-color light signal data in real time, and store the machine tool operation data in the processing process into the database for subsequent data analysis and calculation. The system makes deep use of the original three-color light data to improve the utilization rate of the three-color light data. This monitoring method is real-time, simple and easy to understand, and has detailed and complete data, which can improve the value of industrial production data.
[0018] Using three-color lights and data acquisition modules, through real-time data collection, data analysis and prediction algorithms are performed to improve the waste of human subjectivity, calculate accurate OEE (overall equipment efficiency) data to obtain production forecasts and complete production scheduling, and improve production efficiency.
[0019] The algorithm automatically gives suggestions for improving the production line after calculation, which improves production efficiency and optimizes the allocation of various resources. Compared with the traditional production forecasting method, which is time-consuming, costly and inaccurate, this algorithm has the advantages of simplicity and real-time. The more accurate three-category production forecast and production scheduling obtained by the algorithm can effectively help customers quickly formulate production plans, avoid delaying contract compensation and affecting reputation, and meet customer needs. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The present invention is further described below in conjunction with the accompanying drawings and embodiments.
[0021] Figure 1 It is a structural schematic diagram of the machining equipment output prediction system in the present invention. DETAILED DESCRIPTION
[0022] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.
[0023] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.
[0024] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.
[0025] A machining equipment output prediction system is interconnected with a workshop management system (MES). The system includes a data acquisition module, a database, a production line resource optimization module, an OEE calculation module, and an output prediction and scheduling module.
[0026] The data acquisition module is connected to the three-color light system installed in the machining production line through the MES system. The data acquisition module is connected to the database. The data acquisition module monitors the three-color light signal of the machine tool in real time, converts the collected three-color light signal data and stores it in the database as the data basis for subsequent production forecasts.
[0027] Specifically, the power supply end of the three-color lamp is connected to the positive voltage of the CNC machine tool, and there are three control lines corresponding to the three colors and connected to the three control signal lines of the CNC machine tool. The three-color lamp is controlled by controlling the output voltage to turn on and off, and the PLC outputs voltage at a fixed frequency to control the flashing of the lamp. The switch quantity acquisition module and the network communication module are used to detect the signal changes on the production line and collect the pulse signals generated when the CNC machine tool is working. The three-color lamp establishes communication with the data acquisition module in the upper computer software through the control signal line or other appropriate communication methods, and sends a digital pulse signal under the current lighting state of the three-color lamp.
[0028] The meaning of the three-color light is defined by the manufacturer, and the meaning of the light status of different manufacturers is different (for example, red is always on when the machine tool is shut down, red flashes when it is an abnormal alarm, yellow is always on when waiting for material to be put in, yellow flashes when waiting for the machine tool to be cleaned, green is always on when normal processing is in progress according to the set program, green flashes when the machine tool is reset, and the light is off when the production line is closed). The algorithm business logic layer defines the meaning of the three-color light as a variable and establishes a corresponding relationship with different digital pulse signal data. After receiving the digital pulse signal, it is compared and judged to obtain the current light status of the three-color light and the operation status of the machine tool, and the corresponding variables and receiving time data are stored in the database. In this way, the received signal is converted into a definable data type.
[0029] For example, when a machine tool stops during production time due to waiting for materials, changing materials, blocking materials, fault maintenance, power outages, etc., the three-color light signal changes from green to red. At this time, the signal changes. Real-time acquisition of signal data, when the signal light changes from red to green again, that is, the machine tool returns to normal, the signal change information is obtained again, so that the downtime of each section of the equipment during the total production time can be obtained. These data are stored in the database for subsequent calculations.
[0030] The production line resource optimization module uses a large amount of three-color light data in the database and combines it with other production information (such as materials, manpower data, etc.) to compare and analyze the proportion of various types of downtime reasons for each device during the total production time. This can accurately identify risk factors that may affect production output, summarize improvement plans, optimize resource allocation of production lines, solve the problem of worker idleness, and increase production output.
[0031] The OEE calculation module calculates OEE (overall equipment efficiency) by calling the data in the database in real time.
[0032] The calculation of OEE is related to the total machining production time, the sum of each abnormal downtime, the time to process a product under ideal conditions, the total output and the number of unqualified products. The algorithm takes a period of time, calls the collected three-color light data and the statistical total output and unqualified number data within the time interval. Preliminary data processing is performed based on the meaning of the lights, and the normal shutdown time of the production line is removed to obtain the total machining production time; each abnormal downtime is added to obtain the sum of each abnormal downtime; within this time interval, the shortest time to process a product is retrieved, and the sum of each abnormal downtime for processing this product is removed as the time for the equipment to process a product under the current ideal condition.
[0033] Equipment utilization rate is the proportion of time that equipment takes up to create value within the time it can provide. Performance utilization rate is the degree of effective production of equipment during production. The equipment utilization rate F, performance utilization rate K, qualified product rate P and OEE of a certain machining production line are expressed as follows:
[0034]
[0035]
[0036]
[0037] OEE=F×K×P (4)
[0038] In the formula, T is the total machining production time, C is the sum of the abnormal downtime of each section, E is the time to process a product under ideal conditions, D is the total output, and G is the number of unqualified products.
[0039] After the real-time OEE data of each device is accurately counted, it is automatically uploaded and stored in the database, serving as the data basis for equipment production trend analysis, production forecasting and production scheduling calculation.
[0040] The production forecasting and scheduling module automatically summarizes different machining strategies through algorithms for users to choose from, helping users plan production.
[0041] The predicted output is related to the specified time, OEE, and the time it takes to process a product under ideal conditions. The output forecast for a specified period of time, the predicted output N of a certain machining production line equipment is expressed as follows:
[0042]
[0043] Where t is the specified time, OEE is the overall equipment efficiency, and E is the time to process a product under ideal conditions.
[0044] Therefore, the OEE calculation module calculates the ideal output N (calculated according to the optimal OEE of each device in the recent period), the stable output (the output data predicted by the mathematical model fitted by the output of each device in the recent period is substituted into the OEE formula for calculation), and the worst output (the worst output data of each device in the recent period is substituted into the OEE calculation). Because OEE changes in real time, an OEE data set will be formed. The predicted output is to select the OEE data set of the most recent period for calculation to obtain different output prediction results (ideal, stable, and worst). Three production scheduling results are obtained according to the three situations, so that users can compare and select plans. You can choose a production scheduling plan based on stable output, or choose a production scheduling plan with the worst output for more security (but it is easy to cause equipment idle waste) to improve the accuracy of the production scheduling plan.
[0045] In response to new order scheduling requirements, the algorithm imports production order data from the MES system for calculations.
[0046] The user can specify a batch of equipment for this order production, and predict the delivery date through algorithm calculation. Input the unit production cycle (including the actual production and shutdown time, generally in days) and the actual production time within the cycle. Substitute the actual production time as the specified time t into formula (5) to obtain the predicted output of each equipment within the unit production cycle. The sum of multiple equipment is obtained to obtain the total output within the unit production cycle. The order demand divided by the total output within the unit production cycle can obtain the number of unit production cycles required for production, and the delivery date is predicted from this.
[0047] If the order includes a delivery date, it is possible to plan how many devices to arrange for production. Input the delivery date, required output, unit production cycle and actual production time within the cycle. Calculate the number of cycles until the delivery date and multiply it by the actual production time to get the total production time. Substitute the total production time as the specified time t into formula (5) to get the predicted output of each device until the delivery date. According to the production status of each device, list the permutations and combinations of multiple devices and sum them up, compare and judge the optimal solution that meets and is closest to the required output, and output it for selection. If the required output cannot be met, it will prompt to extend the delivery date or purchase more equipment.
[0048] Specific conditions are set according to user needs, so that users can understand the production status of production line equipment more clearly and accurately, so as to make corresponding contract decisions.
[0049] At the same time, the reasons for downtime due to faults can be analyzed by data, and their frequency of occurrence and duration of impact can be compared, so that users can perform preventive maintenance and improve the resolution rate, thereby reducing the loss of fault time and increasing the equipment utilization rate. The reasons for non-fault downtime (waiting for materials, loading, equipment cleaning, etc.) can be analyzed by data to determine the rationality of the time used, prompting users to make adequate preparations and improve personnel management to improve performance utilization. During the production process, OEE (overall equipment efficiency) changes in real time, from which production forecasts and subsequent production scheduling results can be continuously obtained, so that users can make timely adjustments based on the proposed solutions when they encounter the risk of delays caused by unexpected situations, improve the accuracy of production scheduling, and ensure the order completion rate.
[0050] In addition, a simple UI interface can be drawn to show users the equipment production status, OEE dashboard, equipment production output and fault trend, production status (quantity, completion rate, yield rate) and other information required by users, and includes functions such as output forecast and production scheduling input and output, which is convenient for users to observe and operate.
[0051] In summary, the system can be linked with the original workshop management system, with little change to the original system. It only needs to install the corresponding module to collect the three-color light signal data in real time, and store the machine tool operation data in the processing process into the database for subsequent data analysis and calculation. The system makes deep use of the original three-color light data to improve the utilization rate of the three-color light data. This monitoring method is real-time, simple and easy to understand, and has detailed and complete data, which can improve the value of industrial production data.
[0052] Using three-color lights and data acquisition modules, through real-time data collection, data analysis and prediction algorithms are performed to improve the waste of human subjectivity, calculate accurate OEE (overall equipment efficiency) data to obtain production forecasts and complete production scheduling, and improve production efficiency.
[0053] The algorithm automatically gives suggestions for improving the production line after calculation, which improves production efficiency and optimizes the allocation of various resources. Compared with the traditional production forecasting method, which is time-consuming, costly and inaccurate, this algorithm has the advantages of simplicity and real-time. The more accurate three-category production forecast and production scheduling obtained by the algorithm can effectively help customers quickly formulate production plans, avoid delaying contract compensation and affecting reputation, and meet customer needs.
[0054] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.
Claims
1. A machining equipment output prediction system, characterized in that: include A data acquisition module, which is used to obtain and process the signal data of the three-color lamp of the machine tool; An OEE calculation module, which obtains the real-time three-color light data processed by the data acquisition module and calculates the overall efficiency of the equipment; The production forecasting and scheduling module calculates the measured production according to the calculation results of the OEE operation module, and automatically summarizes different machining strategies.
2. The machining equipment output prediction system according to claim 1, characterized in that: The data acquisition module is provided with a switch quantity acquisition module and a network communication module. The switch quantity acquisition module is used to detect signal changes on the production line and collect pulse signals generated when the CNC machine tool is working. The network communication module is used to establish communication with the three-color light of the machine tool to realize signal transmission.
3. The machining equipment output prediction system according to claim 2, characterized in that: The data acquisition module is provided with an algorithm business logic layer, which defines the meaning of the three-color light as a variable and establishes corresponding relationships with different digital pulse signal data. After receiving the digital pulse signal, it is compared and judged to obtain the current state of the three-color light and the operation of the machine tool, and the corresponding variables and receiving time data are stored in the database.
4. The machining equipment output prediction system according to claim 1, characterized in that: The equipment comprehensive efficiency is related to the total machining production time, the sum of each section of abnormal downtime, the time to process a product under ideal conditions, the total output and the number of unqualified products. The OEE calculation module takes a time interval, calls the collected three-color light data and the total output and unqualified number data within the time interval, performs preliminary data processing according to the meaning of the light, removes the normal shutdown time of the production line, and obtains the total machining production time; each section of abnormal downtime is added to obtain the sum of each section of abnormal downtime; In this time interval, the shortest time required to process a product is obtained, excluding the sum of the abnormal downtimes of each section in processing this product, and this is taken as the time required for the equipment to process a product under the current ideal conditions.
5. The machining equipment output prediction system according to claim 4, characterized in that: The calculation formula of the equipment overall efficiency is as follows: OEE=F×K×P, where F is the equipment utilization rate, K is the performance utilization rate, and P is the qualified product rate.
6. The machining equipment output prediction system according to claim 1, characterized in that: The predicted output is related to the specified time t, OEE, and the time E for processing a product under ideal conditions.
7. The machining equipment output prediction system according to claim 6, characterized in that: It also includes a production line resource optimization module, which obtains the three-color light data processed by the data acquisition module, combines it with other production information to compare and analyze the proportion of various types of downtime reasons for each equipment within the total production time, and provides a reference for the formulation of processing strategies in the output forecast and scheduling module.
8. The machining equipment output prediction system according to claim 6, characterized in that: The output forecasting and production scheduling module obtains various data of the production order, and obtains the production cycle or production scheduling plan in combination with the predicted output calculation formula.