An intelligent expansion control module compatible with Siemens Sinumerik ONE system

Abstract

This invention discloses an intelligent extended control module compatible with the Siemens Sinumerik ONE system. Integrated into the Siemens Sinumerik ONE CNC system, the intelligent extended control module analyzes historical and real-time data generated during machining based on machine learning algorithms. By adjusting machining parameters, it optimizes the CNC machining process. The intelligent extended control module acquires system data in real time through the Sinumerik ONE communication interface and utilizes Siemens' edge computing devices or cloud platform for data processing and analysis to achieve dynamic parameter adjustment. This invention is not only applicable to the optimization of single devices but also enables global optimization and predictive maintenance in multi-factory, multi-device collaborative environments, thereby greatly enhancing the flexibility, stability, and production efficiency of the production system.

Description

Technical Field

[0001] The invention belongs to the technical field of numerical control, and in particular relates to an intelligent expansion control module compatible with the Siemens Sinumerik ONE system. Background Art

[0002] With the rapid development of Industry 4.0 and intelligent manufacturing, CNC machine tools have become crucial equipment in the manufacturing industry, used to achieve high-precision, automated and efficient processing operations. However, traditional CNC systems still have some shortcomings when facing complex processing tasks, especially in improving processing efficiency, optimizing tool life, improving processing quality and reducing equipment downtime. In order to solve these problems, intelligent control technology, machine learning and big data analysis have gradually been introduced into CNC systems to achieve automatic optimization and intelligent management of the processing process.

[0003] Currently, many CNC systems on the market provide some automation functions, but most systems still rely on manual operation and debugging, especially in the setting and optimization of processing parameters, operators need to manually adjust them based on experience. This approach is not only inefficient, but also prone to unstable processing or poor processing quality due to human errors. In addition, traditional CNC systems also face challenges in dealing with tool wear management, predictive maintenance and multi-machine collaborative control, resulting in premature tool damage, long equipment downtime, and high maintenance costs, which cannot meet the needs of efficient operation of large-scale production lines in modern manufacturing.

[0004] The Sinumerik ONE CNC system launched by Siemens is a high-performance integrated CNC system that is widely used in turning, milling and grinding, and supports multi-axis linkage and complex processing tasks. However, with the continuous increase in the demand for manufacturing, how to further improve the intelligence level of the Sinumerik ONE system so that it can handle complex processing tasks more efficiently, optimize the processing process, extend the service life of tools, reduce downtime, and improve overall production efficiency has become an urgent problem to be solved.

[0005] Therefore, it is of great significance to develop an intelligent expansion control module compatible with the Siemens Sinumerik ONE system to solve the problems of insufficient automation, imperfect tool management, lack of predictive maintenance and low efficiency of multi-device collaborative control in traditional CNC systems. Summary of the invention

[0006] Therefore, the purpose of the present invention is to provide an intelligent expansion control module compatible with the Siemens Sinumerik ONE system. The present invention is not only suitable for the optimization of a single device, but also can achieve global optimization and predictive maintenance in a multi-factory and multi-device collaborative working environment, thereby greatly enhancing the flexibility, stability and production efficiency of the production system.

[0007] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:

[0008] An intelligent expansion control module compatible with the Siemens Sinumerik ONE system, the intelligent expansion control module is integrated in the Siemens Sinumerik ONE numerical control system, analyzes historical data and real-time data generated during the processing based on a machine learning algorithm, and optimizes the numerical control processing by adjusting processing parameters; the intelligent expansion control module obtains system data in real time through the communication interface of Sinumerik ONE, uses Siemens' edge computing equipment or cloud platform to process and analyze data, and realizes dynamic parameter adjustment;

[0009] The intelligent expansion control module includes:

[0010] A data acquisition and preprocessing module, wherein the data acquisition and preprocessing module is used to collect one or more real-time data including cutting force, spindle load, tool wear, temperature, vibration and processing time from the Sinumerik ONE system;

[0011] The virtual simulation integration module provides virtual debugging and optimization functions by integrating with the Run MyVirtual Machine module of the Sinumerik ONE system;

[0012] The computing module is integrated into Siemens' Sinumerik Edge edge computing device, which uses its computing power to locally process and analyze processing data;

[0013] Adaptive machining optimization and NC program dynamic adjustment module, dynamically adjust key parameters in NC programs based on real-time data and historical experience;

[0014] Tool management and predictive maintenance module, integrated with Sinumerik ONE's tool management system, monitors tool wear in real time, predicts tool life during processing and adjusts processing parameters in advance;

[0015] The cloud data analysis and optimization module uploads the processing data to the cloud and uses the cloud data analysis capabilities to perform global optimization and big data analysis.

[0016] The data acquisition and preprocessing module is seamlessly connected to the real-time data stream of SinumerikONE through the Profinet or OPC UA communication protocol.

[0017] The virtual simulation integration module is combined with the Run MyVirtual Machine virtual simulation module of the Sinumerik ONE system to pre-debug and verify the NC code and processing parameters through virtual simulation technology, simulate the processing process in a virtual environment, and adjust the NC program or process parameters according to the simulation results, thereby optimizing the processing strategy before physical processing.

[0018] The computing module is integrated into Siemens' Sinumerik Edge edge computing platform, and local processing and analysis of large amounts of real-time data are achieved through Sinumerik Edge devices. Edge computing is used to infer and optimize machine learning algorithms, reduce data transmission delays, and achieve real-time adaptive adjustment of the processing process.

[0019] Among them, the adaptive processing optimization and NC program dynamic adjustment module has a dynamic parameter optimization function. It automatically adjusts the processing parameters including cutting speed, feed rate and coolant flow rate through real-time analysis of the collected data and the machine learning model trained with historical data. It also directly modifies and optimizes the NC code through the Sinumerik ONE system, so that various parameters in the processing process change in real time according to actual needs, ensuring the optimization effect.

[0020] The tool management and predictive maintenance module is deeply integrated with the tool management system of Sinumerik ONE. It monitors the wear of the tool, predicts the tool life, and automatically adjusts the processing parameters or reminds the operator to replace the tool according to the wear status of the tool.

[0021] The cloud data analysis and optimization module is integrated with Siemens' industrial cloud platform MindSphere to upload real-time processing data to the cloud for big data analysis and global optimization. It uses the MindSphere platform to perform collaborative optimization of multiple devices and multiple factories, analyze processing data across factories, further optimize processing strategies, and synchronize cloud optimization results to local machine tools in the Sinumerik ONE system to achieve remote management and global optimization.

[0022] The intelligent expansion control module includes a user interface and an operation feedback module. The user interface and the operation feedback module display the real-time optimization data, historical processing records and dynamic parameter adjustment suggestions of the intelligent expansion control module through the HMI Pro interface of Sinumerik ONE. The operator can monitor, adjust and intervene in the processing process in real time through this interface to ensure that the operator's optimization adjustment of the processing process is transparent and controllable.

[0023] Among them, the intelligent expansion control module further integrates a multi-machine collaborative control module, which can work in coordination with multiple machine tools or equipment in the SinumerikONE system. Based on the optimization algorithm of multi-device collaboration, it intelligently adjusts the working rhythm and load distribution of each machine tool to improve the overall production efficiency and the collaborative production capacity of the factory; the multi-machine collaborative control module realizes intelligent scheduling and load balancing across devices by working in coordination with the Siemens Simatic PLC system.

[0024] Among them, the intelligent extension control module has a predictive maintenance function. By real-time monitoring of key operating parameters of machine tools including spindle load, cutting force and vibration data, combined with historical data and machine learning models, it can predict the occurrence time of equipment failure in advance and provide preventive maintenance suggestions; the predictive maintenance function ensures that the equipment is always in the best operating state and reduces unplanned downtime through the diagnostic function of the Sinumerik ONE system and the data analysis of the MindSphere cloud platform.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The intelligent expansion control module compatible with the Siemens Sinumerik ONE system of the present invention realizes intelligent optimization and automated management in the CNC machining process by highly integrating advanced machine learning algorithms, virtual simulation, edge computing, and cloud data analysis technologies. The module dynamically adjusts key machining parameters based on real-time collection and analysis of a large amount of data generated during the machining process, thereby optimizing the machining process, improving production efficiency, and reducing equipment maintenance costs. The following is a comprehensive description of the main beneficial effects of the module and its various technical functions:

[0027] The intelligent expansion control module relies on machine learning algorithms to analyze historical and real-time data from the processing process in real time and automatically adjust parameters. This adaptive learning ability enables the system to quickly adapt to different processing conditions, thereby improving processing accuracy and reducing errors. This process does not require a lot of manual intervention, reducing the workload of operators and improving the automation level of the system.

[0028] Through the integration of Run MyVirtual Machine virtual simulation with the Sinumerik ONE system, processing simulation and optimization in a virtual environment are realized. Through digital twin technology, any parameter adjustment can be tested and verified in a virtual environment to ensure that the adjusted parameters can effectively reduce trial and error costs and risks in actual processing. This not only reduces material waste, but also improves the safety and efficiency of the equipment, so that every optimization adjustment is fully verified.

[0029] Through the Advanced CNC function of Sinumerik ONE, the intelligent extension control module can directly optimize and dynamically adjust the key parameters in the NC program. The module monitors the data in real time during processing and continuously adjusts the cutting speed, feed rate and other parameters in the program based on the feedback. In this way, the system can quickly adapt to the process during processing to ensure the best processing effect while reducing processing defects caused by parameter errors.

[0030] The intelligent expansion control module of the present invention is combined with the tool management system of Sinumerik ONE to monitor the tool wear status in real time and predict the tool life. Through predictive maintenance, the module can prompt the replacement of the tool before it reaches its limit, and adjust the processing parameters to extend the tool life and reduce the risk of tool damage. This kind of tool management automation improves production efficiency, reduces errors caused by manual intervention, and reduces downtime.

[0031] The intelligent expansion control module is integrated with the Siemens MindSphere industrial cloud platform, and performs cross-device and cross-factory data analysis and global optimization through the cloud platform. The module can perform large-scale analysis of processing data of multiple devices in the cloud and generate global optimization strategies. This global optimization is particularly suitable for multi-factory collaborative production or complex production processes, and helps to improve the intelligence level and production efficiency of the entire production system.

[0032] Through deep integration with the Simatic PLC system of the Sinumerik ONE system, the intelligent expansion control module can achieve collaborative control and load balancing among multiple devices, and dynamically adjust the work tasks and rhythm of each machine tool. The module monitors the load status of the equipment in real time to ensure balanced distribution of workloads, avoid equipment overload or idleness, and improve the operating efficiency of the entire production line. It is particularly suitable for multi-machine collaborative scenarios in large-scale production lines.

[0033] Through the HMI Pro interface of Sinumerik ONE, operators can intuitively monitor the real-time data and optimization suggestions of the system. The visual interface provided by the module displays the processing status, parameter adjustment and historical data. The operator can manually intervene to adjust the parameters according to the actual situation to ensure the transparency and controllability of the entire processing process. The interface is simple and easy to use, which helps operators quickly understand and perform processing optimization.

[0034] Combined with Siemens' process library, the intelligent expansion control module can automatically optimize relevant process parameters according to different processing technologies. The module dynamically adjusts process parameters according to different workpiece materials and real-time processing conditions to ensure processing quality and efficiency under different material and process scenarios. This capability makes the module extremely flexible and adaptable in production.

[0035] Through deep integration with the Siemens Sinumerik ONE system, the intelligent expansion control module can achieve intelligent optimization of the processing process, real-time adaptive adjustment, and collaborative control of multiple devices. With the help of machine learning algorithms, virtual simulation, edge computing, and cloud data analysis, the module significantly improves the automation level, production efficiency, and intelligence of CNC machining. The module is not only suitable for the optimization of a single device, but also can achieve global optimization and predictive maintenance in a multi-factory, multi-device collaborative working environment, thereby greatly enhancing the flexibility, stability, and production efficiency of the production system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below in combination with the accompanying drawings and detailed embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0037] Figure 1 Schematic diagram of the working process of the intelligent expansion control module of the present invention. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0039] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0040] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0041] See also Figure 1 , an intelligent expansion control module compatible with the Siemens Sinumerik ONE system. The intelligent expansion control module is integrated in the Siemens Sinumerik ONE CNC system. Based on the machine learning algorithm, it analyzes the historical data and real-time data generated during the processing process, and optimizes the CNC processing process by adjusting the processing parameters; the intelligent expansion control module obtains system data in real time through the communication interface of Sinumerik ONE, and uses Siemens' edge computing devices or cloud platforms for data processing and analysis to achieve dynamic parameter adjustment.

[0042] The intelligent expansion control module includes a data acquisition and integration module, which uses Sinumerik ONE's open communication interfaces, such as Profinet, OPC UA or Industrial Ethernet, to seamlessly connect with sensors of machine tools and peripheral equipment. Through the diagnostic and monitoring functions embedded in the Sinumerik ONE system, all relevant data during the machining process are automatically collected, including cutting force, spindle load, tool wear, vibration, temperature, machining time, etc.

[0043] This module is directly integrated into the PLC control layer of Sinumerik ONE, and uses Sinumerik ONE's powerful real-time data acquisition capabilities to ensure accurate machine tool operating status and processing parameters are collected every second, ensuring optimized real-time performance. Using Sinumerik ONE's native communication interface, compatibility issues are eliminated, ensuring synchronization and accuracy of data acquisition, and is suitable for complex multi-axis linkage control scenarios.

[0044] Furthermore, it also includes a virtual simulation integration module, which provides virtual debugging and optimization functions by integrating with the RunMyVirtual Machine module of the Sinumerik ONE system. The intelligent extension control module can simulate the machining process in a virtual environment and optimize the machining parameters based on the simulation results. In this way, any parameter adjustment can be pre-tested and verified in the digital twin environment.

[0045] Sinumerik ONE supports Run MyVirtual Machine digital twin technology, which enables virtual debugging before actual processing. The intelligent extension control module can be repeatedly optimized in the digital twin model to ensure more accurate adjustments in the physical world. Virtual debugging can reduce the number of parameter adjustments during processing, reduce the risk and trial and error costs in physical processing, and significantly improve production efficiency.

[0046] Furthermore, it also includes a computing module, which is integrated into Siemens' Sinumerik Edge edge computing device, using its high-performance computing capabilities to locally process and analyze processing data. The intelligent expansion control module can send data to Sinumerik Edge in real time for preprocessing and local modeling, and the optimization results are directly applied to the processing process through the Sinumerik ONE system.

[0047] Using the powerful computing power of Sinumerik Edge, the intelligent expansion control module can process large amounts of data in real time during on-site processing, and even perform machine learning reasoning on the edge, reducing latency and the need to rely on remote servers. Fast response is achieved through edge computing, which is suitable for processing tasks with high-frequency adjustment requirements and ensures the real-time performance of parameter optimization.

[0048] Furthermore, it also includes adaptive machining optimization and NC program dynamic adjustment modules. Through the Advanced CNC function of the Sinumerik ONE system, the intelligent extension control module can directly modify and optimize the NC code. The module dynamically adjusts key parameters in the NC program, such as cutting speed, feed rate, acceleration / deceleration curve, etc., based on real-time data and historical experience, to achieve real-time adaptive optimization of the entire machining process.

[0049] By utilizing the powerful control capabilities of the Sinumerik ONE system over NC programs, the optimization strategies in the programs are automatically generated and dynamically modified, and integrated with the system's CNC machining libraries, the optimization strategies of the intelligent extension control modules can be directly fed back to the controller level to ensure a high degree of synchronization in the operation of the machine tools. By utilizing the open NC control architecture of the Siemens system, the intelligent extension control module not only optimizes the machining strategy, but also dynamically affects the actual NC code execution, ensuring that real-time adjustments do not affect machining accuracy.

[0050] Furthermore, it also includes a tool management and predictive maintenance module, which integrates the Sinumerik ONE tool management system. By real-time monitoring of tool wear, the intelligent extension control module can predict tool life during processing and adjust processing parameters in advance, thereby extending tool use time or prompting replacement in a timely manner.

[0051] Sinumerik ONE's tool management system is deeply integrated with the intelligent expansion control module. Based on real-time data on tool wear, it predicts tool life and maintenance time, and realizes automated and intelligent adjustment of tool management. Through local integration of the tool management system, tool life can be automatically predicted, reducing human errors and tool damage risks, and improving production efficiency.

[0052] Furthermore, it also includes cloud data analysis and optimization modules based on MindSphere. The intelligent expansion control module uploads a large amount of data in the processing process to the cloud through seamless integration with the Siemens MindSphere industrial cloud platform. The powerful data analysis capabilities of the cloud are used to perform global optimization and big data analysis. The cloud analysis results are fed back to the local machine tool through the Sinumerik ONE system to adjust the processing strategy.

[0053] Through the MindSphere cloud platform, the intelligent expansion control module can realize cross-device and cross-factory data analysis and optimization, provide remote management and large-scale data optimization for various types of equipment, and is particularly suitable for multi-factory and multi-device collaborative production environments. It can achieve large-scale machine learning model training and cross-factory optimization, improve the intelligence level of the entire production system, and support global optimization and predictive maintenance.

[0054] Furthermore, it also includes an adaptive process parameter adjustment module based on the process library. Siemens' process library (Machining Technology Libraries) provides a large number of preset process parameters for specific processes such as turning, milling, grinding, etc. The intelligent expansion control module can combine these process libraries to dynamically optimize parameters such as cutting speed, feed rate, coolant flow, etc., and adaptively adjust the parameters in the process library based on real-time feedback.

[0055] The intelligent expansion control module uses the process library function of Sinumerik ONE to optimize the process, and automatically adjusts the parameters in the library based on historical data to improve the processing accuracy and efficiency of specific processes. Through the combination of process library and adaptive learning, the module can continuously optimize parameters during the processing process, adapt to the changes of different workpieces and materials, and ensure processing quality.

[0056] Furthermore, it also includes a multi-machine collaborative control module based on Simatic PLC integration. Through deep integration with the Siemens Simatic PLC system, the intelligent expansion control module can not only optimize a single machine tool, but also collaboratively control multiple devices, coordinate their processing rhythm, and realize intelligent multi-machine collaborative workflow.

[0057] The Sinumerik ONE system can communicate seamlessly with Simatic PLC. The intelligent expansion control module optimizes the workload distribution and production rhythm of each device in real time through integration with PLC. It is suitable for large-scale production lines or multiple machine tools to improve production efficiency by coordinating equipment operation.

[0058] Furthermore, it also includes HMI Pro interface integration and adaptive learning visualization modules. Through the HMI Pro interface of the SinumerikONE system, the intelligent expansion control module can display the optimization suggestions and parameter adjustment results of the processing process in real time in the operator's interface, and provide visual feedback and data display.

[0059] Sinumerik ONE's HMI Pro interface allows the intelligent expansion control module to interact with the operator through a customized interface, displaying the optimized processing status and historical data, and the operator can manually intervene and adjust parameters. Providing an intuitive user interface helps the operator understand the optimization process and make adjustments when necessary, ensuring user transparency of the system's operations.

[0060] Furthermore, it also includes NC code optimization and simulation modules. Through the NC simulation function of the Sinumerik ONE system, the intelligent extension control module can simulate the execution effect of the NC code before adjusting the processing parameters to ensure that the NC program after parameter optimization will not cause processing errors or safety hazards.

[0061] Utilize the NC simulation function of Sinumerik ONE to ensure that the optimization and modification of NC code by the intelligent extension control module is fully verified in a virtual environment. Reduce the risks brought by parameter adjustment and ensure that each modification is rigorously simulated and tested to avoid potential processing problems.

[0062] The intelligent expansion control module is combined with the Siemens Sinumerik ONE system, and its operation method is as follows:

[0063] Step 1: Data collection and initialization

[0064] System connection: Connect the intelligent expansion control module to the Sinumerik ONE system through the industrial communication interface. Ensure that all processing equipment, sensors and peripherals (such as tool management system, servo drive, spindle motor) can exchange data with the intelligent expansion control module.

[0065] Data acquisition initialization: After the intelligent expansion control module is started, the Sinumerik ONE system begins to collect real-time processing data, including cutting speed, feed rate, spindle load, tool wear, temperature, vibration and processing time, etc. These data will be transmitted to the data acquisition and preprocessing module in the module.

[0066] Data preprocessing: Clean and format the collected raw data, including noise removal, abnormal data processing and data normalization. After preprocessing, the data enters the machine learning analysis stage.

[0067] Step 2: Machine Learning Model Selection and Training

[0068] Feature extraction: Based on the preprocessed data, the intelligent expansion control module extracts key features, such as tool wear rate, spindle load fluctuation, temperature change trend, etc. These features will serve as input to the machine learning model.

[0069] Model selection: Choose the appropriate machine learning model based on the actual application scenario (such as high-precision machining, mass production). You can use regression models for cutting parameter optimization, reinforcement learning models for dynamic adjustment of machining strategies, or neural network models for optimization of complex multi-axis machining.

[0070] Model training: Model training is performed based on historical data from the Sinumerik ONE system. The training process can be performed locally (using the Sinumerik Edge edge computing platform) or through a cloud platform (such as MindSphere). After the model is initially trained, an initial optimization strategy is generated.

[0071] Step 3: Virtual simulation and optimization strategy verification

[0072] Virtual environment simulation: The intelligent extension control module is integrated with the Run MyVirtualMachine module of the Sinumerik ONE system to simulate the machining process in a virtual simulation environment. The virtual environment will be pre-debugged according to the NC code and current workpiece data to verify the optimization strategy generated by the intelligent extension control module in advance.

[0073] Strategy verification and adjustment: Through virtual simulation, multiple processing cycles are simulated to verify the generated optimization parameters (such as cutting speed, feed rate, acceleration curve, etc.). If the strategy is found to be inappropriate or potential risks arise, the module will further optimize the model and perform simulation tests again until the optimal processing parameters are determined.

[0074] Step 4: Adaptive adjustment of parameters in real-time processing

[0075] Processing start: After virtual simulation confirmation, the actual processing process starts. The intelligent expansion control module transmits the real-time data in the processing to the adaptive parameter optimization module, which combines historical data, machine learning models and real-time data to optimize the processing parameters in real time.

[0076] Dynamic parameter adjustment: Based on real-time monitoring of cutting force, spindle load, tool wear and other data, the intelligent expansion control module dynamically adjusts processing parameters (such as cutting speed, feed rate, coolant flow, etc.). Changes in these parameters are directly reflected in the execution of NC code through the Sinumerik ONE system.

[0077] Real-time feedback and adaptive learning: During the processing, the module continuously adjusts parameters and continues to optimize the model based on real-time feedback. This closed-loop control method ensures adaptive adjustment throughout the entire processing cycle, reduces processing errors and improves processing efficiency.

[0078] Step 5: Tool Management and Maintenance

[0079] Tool status monitoring: The intelligent extended control module monitors tool wear in real time by integrating with the tool management system of the Sinumerik ONE system. By analyzing tool wear data, the module can predict tool life in advance and remind the operator to replace the tool or automatically adjust the processing parameters to extend tool life when necessary.

[0080] Tool replacement suggestion: When the module predicts that the tool is about to reach its life limit, the system will automatically issue an alarm to remind the operator to replace the tool. At the same time, the module will adjust the processing parameters according to the current tool status to ensure that the best processing quality is maintained before the end of the tool life.

[0081] Step 6: Predictive maintenance and equipment optimization

[0082] Equipment status monitoring: The intelligent expansion control module monitors the operating status of the machine tool in real time through the diagnostic function of the Sinumerik ONE system, including key parameters such as spindle vibration, load, and temperature.

[0083] Fault prediction and maintenance recommendations: Based on the collected historical data and real-time monitoring data, the intelligent expansion control module uses machine learning models to predict possible equipment failure times and provide preventive maintenance recommendations. When equipment is abnormal, the module will issue an alarm in advance, and the operator can perform preventive maintenance according to the system recommendations to reduce unplanned downtime.

[0084] Step 7: Multi-machine collaboration and workflow optimization

[0085] Multi-device integration and collaborative work: The intelligent expansion control module is integrated with multiple devices in the factory through the Sinumerik ONE system to achieve multi-machine collaborative work. Through the Simatic PLC system of Sinumerik ONE, the module can coordinate the processing rhythm of each device and optimize the load distribution in the process.

[0086] Workflow optimization: The module analyzes the operating status and production load of each device and dynamically adjusts the task arrangement of each device to ensure the maximum operating efficiency of the entire production line. For large-scale production lines, the module can optimize production scheduling and avoid bottleneck problems.

[0087] Step 8: Cloud data analysis and global optimization

[0088] Data upload to the cloud platform: During the production process, the intelligent expansion control module uploads the processing data to Siemens' MindSphere cloud platform through the Sinumerik ONE system. The cloud can perform large-scale analysis of data from different equipment and factories.

[0089] Global optimization and feedback: MindSphere platform conducts global optimization analysis based on a large amount of data and provides optimization suggestions for each factory and production line. The intelligent expansion control module downloads the latest optimization strategy through the cloud platform and applies it to local equipment to achieve global optimization across factories.

[0090] Step 9: User Interface and Operation Feedback

[0091] HMI Pro interface display: Through the HMI Pro interface of Sinumerik ONE, the optimization suggestions and real-time processing data of the intelligent expansion control module are presented to the operator in a visual way. The operator can view real-time data (such as cutting speed, feed rate, spindle load, etc.) and manually adjust parameters as needed.

[0092] Operational feedback and manual intervention: During the processing, the operator can intervene in the intelligent expansion control module through the HMI Pro interface and input feedback data (such as processing anomalies and tool changes). This data will be used by the module to further optimize the machine learning model and enhance the system's learning ability.

[0093] Step 10: Continuous learning and model updating

[0094] Data accumulation and model update: The intelligent expansion control module will continuously accumulate processing data and regularly update the machine learning model based on the new data to ensure that the system can adapt to changing processing conditions (such as equipment aging, tool wear, environmental changes, etc.).

[0095] Long-term optimization: Through continuous model updates, the system can gradually improve its adaptability to complex processing tasks and achieve higher processing efficiency, lower maintenance costs and longer equipment life.

[0096] Through the above operation method, the intelligent expansion control module can be deeply integrated with the Siemens Sinumerik ONE system, automatically optimize the parameters in the processing process, predict equipment failures, coordinate multiple devices to work together, and achieve global optimization through the cloud platform. Operators can monitor the system operation status in real time through the visual interface, improve production efficiency and reduce equipment maintenance costs.

[0097] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced by equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed in the present invention may be used in combination with each other in any manner, and the fact that these combinations are not exhaustively described in this specification is only for the sake of omitting space and saving resources. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. An intelligent expansion control module compatible with Siemens Sinumerik ONE system, characterized by: The intelligent expansion control module is integrated into the Siemens Sinumerik ONE CNC system. Based on the machine learning algorithm, it analyzes the historical data and real-time data generated during the processing, and optimizes the CNC processing process by adjusting the processing parameters. The intelligent expansion control module obtains system data in real time through the communication interface of Sinumerik ONE, and uses Siemens' edge computing devices or cloud platforms to process and analyze data to achieve dynamic parameter adjustment. The intelligent expansion control module includes: A data acquisition and preprocessing module, wherein the data acquisition and preprocessing module is used to collect one or more real-time data including cutting force, spindle load, tool wear, temperature, vibration and processing time from the Sinumerik ONE system; The virtual simulation integration module provides virtual debugging and optimization functions by integrating with the Run MyVirtual Machine module of the Sinumerik ONE system; The computing module is integrated into Siemens' Sinumerik Edge edge computing device, which uses its computing power to locally process and analyze processing data; Adaptive machining optimization and NC program dynamic adjustment module, dynamically adjust key parameters in NC programs based on real-time data and historical experience; Tool management and predictive maintenance module, integrated with Sinumerik ONE's tool management system, monitors tool wear in real time, predicts tool life during processing and adjusts processing parameters in advance; The cloud data analysis and optimization module uploads the processing data to the cloud and uses the cloud data analysis capabilities to perform global optimization and big data analysis.

2. The intelligent expansion control module compatible with Siemens Sinumerik ONE system according to claim 1, characterized in that: The data acquisition and preprocessing module is seamlessly connected to the real-time data stream of Sinumerik ONE through the Profinet or OPC UA communication protocol.

3. The intelligent expansion control module compatible with Siemens Sinumerik ONE system according to claim 1, characterized in that: The virtual simulation integration module is combined with the Run MyVirtual Machine virtual simulation module of the Sinumerik ONE system to pre-debug and verify the NC code and processing parameters through virtual simulation technology, simulate the processing process in a virtual environment, adjust the NC program or process parameters according to the simulation results, and thus optimize the processing strategy before physical processing.

4. The intelligent expansion control module compatible with Siemens Sinumerik ONE system according to claim 1, characterized in that: The computing module is integrated into Siemens' Sinumerik Edge edge computing platform, and local processing and analysis of large amounts of real-time data are achieved through Sinumerik Edge devices. Edge computing is used to infer and optimize machine learning algorithms, reduce data transmission delays, and achieve real-time adaptive adjustment of the processing process.

5. An intelligent expansion control module compatible with Siemens Sinumerik ONE system according to any one of claims 1 to 4, characterized in that: The adaptive machining optimization and NC program dynamic adjustment module has a dynamic parameter optimization function. It automatically adjusts machining parameters including cutting speed, feed rate and coolant flow rate by real-time analysis of collected data and machine learning models trained with historical data. It also directly modifies and optimizes the NC code through the Sinumerik ONE system, so that various parameters in the machining process change in real time according to actual needs, ensuring the optimization effect.

6. An intelligent expansion control module compatible with Siemens Sinumerik ONE system according to any one of claims 1 to 4, characterized in that: The tool management and predictive maintenance module is deeply integrated with the tool management system of Sinumerik ONE. It monitors the wear of the tool, predicts the tool life, and automatically adjusts the processing parameters or reminds the operator to replace the tool according to the wear status of the tool.

7. An intelligent expansion control module compatible with Siemens Sinumerik ONE system according to any one of claims 1 to 4, characterized in that: The cloud data analysis and optimization module is integrated with Siemens' industrial cloud platform MindSphere to upload real-time processing data to the cloud for big data analysis and global optimization. It uses the MindSphere platform to perform collaborative optimization of multiple devices and multiple factories, analyze processing data across factories, further optimize processing strategies, and synchronize cloud optimization results to local machine tools in the Sinumerik ONE system to achieve remote management and global optimization.

8. An intelligent expansion control module compatible with Siemens Sinumerik ONE system according to any one of claims 1 to 4, characterized in that: The intelligent expansion control module includes a user interface and an operation feedback module. The user interface and the operation feedback module display the real-time optimization data, historical processing records and dynamic parameter adjustment suggestions of the intelligent expansion control module through the HMI Pro interface of Sinumerik ONE. The operator can monitor, adjust and intervene in the processing process in real time through this interface, ensuring that the operator's optimization adjustment of the processing process is transparent and controllable.

9. The intelligent expansion control module compatible with Siemens Sinumerik ONE system according to claim 7, characterized in that: The intelligent expansion control module further integrates a multi-machine collaborative control module, which can work in collaboration with multiple machine tools or equipment in the Sinumerik ONE system. Based on the optimization algorithm for multi-device collaboration, it intelligently adjusts the working rhythm and load distribution of each machine tool to improve the overall production efficiency and the collaborative production capacity of the factory; the multi-machine collaborative control module works in collaboration with the Siemens Simatic PLC system to achieve intelligent scheduling and load balancing across devices.

10. The intelligent expansion control module compatible with Siemens Sinumerik ONE system according to claim 9, characterized in that: The intelligent extended control module has a predictive maintenance function. By real-time monitoring of key operating parameters of the machine tool including spindle load, cutting force and vibration data, combined with historical data and machine learning models, it can predict the time of equipment failure in advance and provide preventive maintenance suggestions. The predictive maintenance function ensures that the equipment is always in the best operating state and reduces unplanned downtime through the diagnostic function of the Sinumerik ONE system and the data analysis of the MindSphere cloud platform.

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