Machine tool digital twin simulation optimization method and system

Abstract

The invention discloses a machine tool digital twin simulation optimization method and system and an optimization control system. The optimization control system comprises a data input unit, a system optimization unit, a twin model construction unit, a data analysis unit, a decision support unit and an implementation feedback unit. Through real-time data acquisition and analysis, machine tool parameters can be adjusted in time, the production efficiency is improved, and the production interruption and shutdown time is shortened; an optimization algorithm is used for improving machining parameters, the machining speed and precision are improved, and the production yield is increased; the digital twinning technology can accurately simulate the operation and environment of a machine tool, predict potential quality problems and ensure that the product quality meets the standard; by optimizing machining parameters and processes, the defect rate of products is reduced, and the consistency and reliability of the products are improved; by predicting the maintenance demand and monitoring the state of the machine tool in real time, maintenance can be carried out in advance, sudden faults are reduced, and the maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of machine tool digital twin simulation optimization systems, and specifically to a machine tool digital twin simulation optimization method and system. Background Art

[0002] The application of digital twin technology in the field of machine tools has gradually attracted attention. A digital twin-driven machine tool refers to a machine tool that synchronizes its physical entity with its digital model in real time to achieve more efficient management, monitoring and optimization. Digital twin technology can closely integrate the physical operation of machine tools with the virtual world, bringing a higher level of intelligence and efficiency improvement to the manufacturing industry.

[0003] Machine tool digital twin simulation optimization is to use digital twin technology to virtually model and simulate machine tools to optimize their performance and processing. This optimization method can significantly improve production efficiency, processing quality and reliability, but the existing machine tool digital twin simulation optimization methods and systems have low production efficiency, repeated operation parameters and high resource waste when used. Summary of the invention

[0004] The purpose of the present invention is to provide a machine tool digital twin simulation optimization method and system to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A machine tool digital twin simulation optimization method and system, an optimization control system, the optimization control system includes a data input unit, a system optimization unit, a twin model construction unit, a data analysis unit, a decision support unit and an implementation feedback unit, the output ends of the data input unit, the twin model construction unit and the data analysis unit are all connected to the input end of the system optimization unit, the output end of the system optimization unit is connected to the input end of the decision support unit, and the output end of the decision support unit is connected to the input end of the implementation feedback unit.

[0007] As a further solution of the present invention: the data input unit includes a data acquisition module and an actual acquisition module, the data acquisition module includes a data forwarding module, a temperature sensor, a vibration sensor and a pressure sensor, and the actual acquisition module includes an ERP system and an MES system.

[0008] As a further solution of the present invention: the system optimization unit includes an optimization algorithm module and a target setting module, which includes genetic algorithm, particle swarm optimization and simulated annealing, and the target setting module includes a performance evaluation tool and a cost analysis tool.

[0009] As a further solution of the present invention: the decision support unit includes a visualization tool module and a result evaluation module, which includes a monitoring dashboard and VR or AR equipment, and the result evaluation module includes a report generation tool and a data analysis tool.

[0010] As a further solution of the present invention: the implementation feedback unit includes an optimization execution module and a monitoring module, the optimization execution module includes a PLC controller and a servo driver, and the monitoring module includes a monitor and an alarm module.

[0011] As a further solution of the present invention: the machine tool digital twin simulation optimization method and system steps are as follows:

[0012] S1: First, various sensors are installed on the machine tool by installing data forwarding modules, temperature sensors, vibration sensors and pressure sensors, and connected to the IoT gateway to ensure that sensor data can be transmitted in real time. Sensor data is collected through the IoT network and stored in the central database. Actual production data such as process parameters and production efficiency are collected regularly and integrated with sensor data;

[0013] S2: The performance evaluation tool in the system optimization unit is used to specify production efficiency and quality targets, the cost analysis tool is used to calculate production costs and expected profits, the genetic algorithm is used to select the best processing parameters, the particle swarm optimization is used to find the optimal solution through group search, and the simulated annealing is used to avoid the local optimum and obtain the global optimum to optimize the algorithm;

[0014] S3: Use modeling software to create a 3D digital model of the machine tool through the twin model building unit, define the machine tool material, geometric features and its kinematic characteristics, and use physical property simulation, finite element analysis to predict the performance of the machine tool structure under different loads, and multi-body dynamics analysis to analyze the interaction between the moving parts of the machine tool;

[0015] S4: The data analysis unit processes the data, removes noise and abnormal data through data cleaning tools such as OpenRefine, and stores and manages historical data through databases such as MySQL and MongoDB;

[0016] S5: The monitoring dashboard displays the machine status and key performance indicators, VR or AR devices are used for virtual training and real-time operation guidance, the report generation tool automatically generates optimization reports, and the data analysis tool evaluates the effects before and after optimization;

[0017] S6: The PLC controller executes the optimized control strategy, the servo drive adjusts the machine tool motion according to the optimization results, the monitor monitors the machine tool performance in real time and collects feedback data, and the alarm module detects abnormalities and notifies the operator.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1. In the present invention, through real-time data collection and analysis, machine tool parameters can be adjusted in time to improve production efficiency, reduce production interruptions and downtime; optimization algorithms are used to improve processing parameters, increase processing speed and accuracy, and increase production output; digital twin technology can accurately simulate the operation and environment of machine tools, predict potential quality problems, and ensure that product quality meets standards; by optimizing processing parameters and processes, product defect rates can be reduced, and product consistency and reliability can be improved; by predicting maintenance needs and real-time monitoring of machine tool status, maintenance can be carried out in advance, sudden failures can be reduced, and maintenance costs can be reduced;

[0020] 2. The present invention optimizes machine tool operating parameters, reduces energy waste, and reduces energy consumption and related costs. The digital twin model can quickly adapt to changes in production requirements, such as adjusting production plans or switching production lines, to enhance production flexibility. Different production plans are simulated in a digital twin environment to support large-scale customized production. Real-time data analysis and visualization tools provide strong decision-making support to help management make more scientific production decisions. Through automated data analysis and optimization algorithms, errors and deviations caused by human intervention are reduced. Through real-time monitoring and predictive models, potential faults and safety hazards can be discovered early to reduce safety risks. Operating procedures and safety measures are adjusted according to system feedback to ensure the safety of operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the overall structure of the machine tool digital twin simulation optimization method and system.

[0022] Figure 2 This is a structural diagram of the data input unit in the machine tool digital twin simulation optimization method and system.

[0023] Figure 3 It is a structural schematic diagram of the machine tool digital twin simulation optimization method and the system optimization unit in the system.

[0024] Figure 4 This is a schematic diagram of the structure of the machine tool digital twin simulation optimization method and the decision support unit in the system.

[0025] Figure 5 This is a schematic diagram of the structure of the feedback unit implemented in the digital twin simulation optimization method and system of machine tools.

[0026] In the figure: 100, optimization control system; 1, data input unit; 2, system optimization unit; 3, twin model building unit; 4, data analysis unit; 5, decision support unit; 6, implementation feedback unit; 10, data acquisition module; 11, actual acquisition module; 101, data forwarding module; 102, temperature sensor; 103, vibration sensor; 104, pressure sensor; 105, ERP system; 106, MES system; 20, system optimization unit; 21, target setting module; 201. Genetic algorithm; 202. Particle swarm optimization; 203. Simulated annealing; 204. Performance evaluation tool; 205. Cost analysis tool; 50. Visualization tool module; 51. Result evaluation module; 501. Monitoring dashboard; 502. VR or AR device; 503. Report generation tool; 504. Data analysis tool; 60. Optimization execution module; 61. Monitoring module; 601. PLC controller; 602. Servo drive; 603. Monitor; 604. Alarm module. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] See also Figure 1 In an embodiment of the present invention, a digital twin simulation optimization method and system for a machine tool, an optimization control system 100, the optimization control system 100 includes a data input unit 1, a system optimization unit 2, a twin model construction unit 3, a data analysis unit 4, a decision support unit 5 and an implementation feedback unit 6, the output ends of the data input unit 1, the twin model construction unit 3 and the data analysis unit 4 are all connected to the input end of the system optimization unit 2, the output end of the system optimization unit 2 is connected to the input end of the decision support unit 5, and the output end of the decision support unit 5 is connected to the input end of the implementation feedback unit 6. The twin model construction unit 3 uses modeling software to create a 3D digital model of the machine tool, define the material, geometric features and kinematic characteristics of the machine tool, and simulate physical properties. Finite element analysis predicts the performance of the machine tool structure under different loads, and multi-body dynamics analyzes the interaction between the moving parts of the machine tool. The data analysis unit 4 processes the data, removes noise and abnormal data through data cleaning tools such as OpenRefine, and stores and manages historical data through databases such as MySQL and MongoDB.

[0029] See also Figure 2The data input unit 1 includes a data acquisition module 10 and an actual acquisition module 11. The data acquisition module 10 includes a data forwarding module 101, a temperature sensor 102, a vibration sensor 103 and a pressure sensor 104. The actual acquisition module 11 includes an ERP system 105 and an MES system 106. By installing the data forwarding module 101, the temperature sensor 102, the vibration sensor 103 and the pressure sensor 104, various sensors are installed on the machine tool, and the IoT gateway is connected to ensure that the sensor data can be transmitted in real time. The sensor data is collected through the IoT network and stored in the central database. The actual production data such as process parameters, production efficiency, etc. are regularly collected and integrated with the sensor data.

[0030] See also Figure 3 The system optimization unit 2 includes an optimization algorithm module 20 and a target setting module 21, wherein the module 20 includes a genetic algorithm 201, a particle swarm optimization 202 and a simulated annealing 203, and the target setting module 21 includes a performance evaluation tool 204 and a cost analysis tool 205. The performance evaluation tool in the system optimization unit 2 is used to specify production efficiency and quality targets, and the production cost and expected profit are calculated by the cost analysis tool. The genetic algorithm 201 is used to select the best processing parameters, the particle swarm optimization 202 finds the optimal solution through group search, and the simulated annealing 203 is used to avoid the local optimum and obtain the global optimum to optimize the algorithm.

[0031] See also Figure 4 The decision support unit 5 includes a visualization tool module 50 and a result evaluation module 51. The module 50 includes a monitoring dashboard 501 and a VR or AR device 502. The result evaluation module 51 includes a report generation tool 503 and a data analysis tool 504. The monitoring dashboard 501 displays the machine tool status and key performance indicators. The VR or AR device 502 is used for virtual training and real-time operation guidance. The report generation tool 503 automatically generates an optimization report. The data analysis tool 504 evaluates the effects before and after optimization.

[0032] See also Figure 5 The implementation feedback unit 6 includes an optimization execution module 60 and a monitoring module 61. The optimization execution module 60 includes a PLC controller 601 and a servo driver 602. The monitoring module 61 includes a monitor 603 and an alarm module 604. The PLC controller 601 executes the optimized control strategy, the servo driver 602 adjusts the machine tool movement according to the optimization result, the monitor 603 monitors the machine tool performance in real time and collects feedback data, and the alarm module 604 detects abnormalities and notifies the operator.

[0033] The working principle of the present invention is: first, by installing a data forwarding module 101, a temperature sensor 102, a vibration sensor 103 and a pressure sensor 104, various sensors are installed on the machine tool, and the IoT gateway is connected to ensure that the sensor data can be transmitted in real time, and the sensor data is collected through the IoT network and stored in the central database. The actual production data such as process parameters, production efficiency, etc. are regularly collected and integrated with the sensor data. Through the performance evaluation tool in the system optimization unit 2, the production efficiency and quality goals are specified, and the production cost and expected profit are calculated through the cost analysis tool. The genetic algorithm 201 is used to select the best processing parameters, the particle swarm optimization 202 finds the optimal solution through group search, and the simulated annealing 203 is used to avoid the local optimum and obtain the global optimum to optimize the algorithm, and the twin model construction unit 3 uses the modeling software to create a 3D digital model of the machine tool, and defines the material and geometry of the machine tool. Features and their kinematic characteristics, and through physical property simulation, finite element analysis predicts the performance of the machine tool structure under different loads, and multi-body dynamics analysis the interaction between the moving parts of the machine tool. The data analysis unit 4 processes the data, removes noise and abnormal data through data cleaning tools such as OpenRefine, and stores and manages historical data through databases such as MySQL and MongoDB. The monitoring dashboard 501 displays the machine tool status and key performance indicators. VR or AR equipment 502 is used for virtual training and real-time operation guidance. The report generation tool 503 automatically generates optimization reports. The data analysis tool 504 evaluates the effects before and after optimization. The PLC controller 601 executes the optimized control strategy. The servo drive 602 adjusts the machine tool motion according to the optimization results. The monitor 603 monitors the machine tool performance in real time and collects feedback data. The alarm module 604 detects abnormalities and notifies the operator.

[0034] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0035] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. The machine tool digital twin simulation optimization system is characterized by: include: An optimization control system (100), the optimization control system (100) comprising a data input unit (1), a system optimization unit (2), a twin model construction unit (3), a data analysis unit (4), a decision support unit (5) and an implementation feedback unit (6), the output ends of the data input unit (1), the twin model construction unit (3) and the data analysis unit (4) are all connected to the input end of the system optimization unit (2), the output end of the system optimization unit (2) is connected to the input end of the decision support unit (5), and the output end of the decision support unit (5) is connected to the input end of the implementation feedback unit (6).

2. The machine tool digital twin simulation optimization system according to claim 1, characterized in that: The data input unit (1) comprises a data acquisition module (10) and an actual acquisition module (11); the data acquisition module (10) comprises a data forwarding module (101), a temperature sensor (102), a vibration sensor (103) and a pressure sensor (104); and the actual acquisition module (11) comprises an ERP system (105) and an MES system (106).

3. The machine tool digital twin simulation optimization system according to claim 1, characterized in that: The system optimization unit (2) includes an optimization algorithm module (20) and a target setting module (21), wherein the (20) includes a genetic algorithm (201), a particle swarm optimization (202) and simulated annealing (203), and the target setting module (21) includes a performance evaluation tool (204) and a cost analysis tool (205).

4. The machine tool digital twin simulation optimization system according to claim 1, characterized in that: The decision support unit (5) includes a visualization tool module (50) and a result evaluation module (51), wherein the (50) includes a monitoring dashboard (501) and a VR or AR device (502), and the result evaluation module (51) includes a report generation tool (503) and a data analysis tool (504).

5. The machine tool digital twin simulation optimization system according to claim 1, characterized in that: The implementation feedback unit (6) includes an optimization execution module (60) and a monitoring module (61), wherein the optimization execution module (60) includes a PLC controller (601) and a servo drive (602), and the monitoring module (61) includes a monitor (603) and an alarm module (604).

6. The machine tool digital twin simulation optimization method according to any one of claims 1 to 5, characterized in that: The machine tool digital twin simulation optimization method and system steps are as follows: S1: First, various sensors are installed on the machine tool by installing a data forwarding module (101), a temperature sensor (102), a vibration sensor (103) and a pressure sensor (104), and connected to an IoT gateway to ensure that sensor data can be transmitted in real time. The sensor data is collected through the IoT network and stored in a central database. Actual production data (such as process parameters, production efficiency, etc.) is collected regularly and integrated with the sensor data; S2: by using the performance evaluation tool in the system optimization unit (2), specifying the production efficiency and quality targets, calculating the production cost and expected profit through the cost analysis tool, optimizing the algorithm through the genetic algorithm (201) for selecting the best processing parameters, the particle swarm optimization (202) for finding the optimal solution through swarm search, and the simulated annealing (203) for avoiding the local optimum and obtaining the global optimum; S3: Building a twin model through unit (3) Use modeling software to create a 3D digital model of the machine tool, define the material, geometric features and kinematic characteristics of the machine tool, and use physical property simulation, finite element analysis to predict the performance of the machine tool structure under different loads, and multi-body dynamics analysis to analyze the interaction between the moving parts of the machine tool; S4: Data analysis unit (4) processes the data, removes noise and abnormal data through data cleaning tools such as OpenRefine, and stores and manages historical data through databases such as MySQL and MongoDB; S5: The monitoring dashboard (501) displays the machine tool status and key performance indicators, the VR or AR device (502) is used for virtual training and real-time operation guidance, the report generation tool (503) automatically generates optimization reports, and the data analysis tool (504) evaluates the effects before and after optimization; S6: The PLC controller (601) executes the optimized control strategy, the servo driver (602) adjusts the machine tool movement according to the optimization result, the monitor (603) monitors the machine tool performance in real time and collects feedback data, and the alarm module (604) detects abnormalities and notifies the operator.

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