Digital cutter management system with intelligent production scheduling function

Abstract

The invention relates to a digital cutter management system with intelligent production scheduling, and aims to optimize cutter use and management in industrial production. The system comprises four main modules: a cutter basic management module, an inventory management module, an interface module and a production management module. The tool basic management module is responsible for managing 2D and 3D graphic information of a tool and monitoring the service life of the tool in real time. The inventory management module uses a bar code or a two-dimensional code to track in-out of a tool, automatically calculates a purchase demand, reminds a minimum inventory level, and supports full-process management of a tool list. And the interface module realizes data exchange with various numerical control programming software and enterprise-level management systems. The production management module analyzes the data of the production control system, extracts a production task and a tool demand list, analyzes tool consumption, generates a purchase plan, and adjusts a production plan according to tool life data and inventory conditions. According to the system, the production efficiency and the cutter utilization rate are greatly improved through integrated management and intelligent characteristics.

Description

Technical Field

[0001] The present invention relates to the field of management of numerical control machining tools, and in particular to a digital tool management system with intelligent production scheduling. Background Art

[0002] Tools are the core tools of CNC machine tool processing companies. In the prior art, tool management systems mainly rely on basic inventory tracking and simple tool maintenance procedures. These systems usually integrate basic inventory management functions, such as recording the entry and exit of tools and basic usage records. In some advanced embodiments, tool management may also include basic tool performance monitoring, as well as preliminary interface integration with CNC machine tools and computer-aided design software, thereby providing limited data synchronization and workflow automation.

[0003] However, existing tool management systems have significant deficiencies in several key aspects. First, these systems often fail to provide comprehensive and dynamic management of tool usage, especially in large-scale production environments, where real-time tool status monitoring, life prediction, and efficient production scheduling capabilities are often insufficient. In addition, the integration and optimization of tool data in existing systems are usually not in-depth enough, resulting in inaccurate or slow-response tool management and scheduling decisions, which limits the improvement of production efficiency and the reduction of costs.

[0004] Given these limitations of existing technologies, there is an urgent need to develop a new digital tool management system to overcome these challenges. Summary of the invention

[0005] The present application provides a digital tool management system with intelligent production scheduling to improve the production efficiency of digital manufacturing.

[0006] The digital tool management system comprises:

[0007] The basic tool management module is used to manage the 2D and 3D graphics of tool units and components, and supports editing and external data import; provides tool assembly management functions; manages cutting parameters for different materials; and monitors and manages tool life in real time;

[0008] Inventory management module, which uses barcodes or QR codes to manage tool in and out of the warehouse; tracks tool usage, including order number, product number, tool list number, tool assembly number and cost center; automatically calculates purchase requisitions and provides automatic purchase reminders for minimum inventory tools; supports the creation, approval, editing, publishing, locking and archiving of tool lists;

[0009] Interface module, used to realize data interaction with CNC programming software, including importing and exporting tool data; supporting data transmission with computer-aided manufacturing software; realizing data exchange with enterprise-level management systems including enterprise resource planning system, manufacturing execution system, product lifecycle management system, distributed CNC system and warehouse management system;

[0010] The production management module is used to parse the production control system data, extract the production task list and the tool requirement list; establish the tool BOM record and the types of products processed by the tool; check whether the tool list is complete according to the process document; analyze the tool consumption and generate a procurement plan; adjust the production plan according to the production task list and tool requirement list, the analysis results of tool consumption, the tool life data from the tool basic management module, and the current tool inventory status and location information from the inventory management module.

[0011] Furthermore, the tool base management module is also used to record a detailed log of each assembly activity, including time, operator, components used and abnormal conditions, for future quality control and auditing.

[0012] Furthermore, the tool basic management module provides a user interface that allows operators to manually adjust cutting parameters according to specific production tasks and feed back the optimization results to the central database to improve future automatic settings.

[0013] Furthermore, the tool base management module includes an automatic alarm unit for alerting the maintenance team when the tool is approaching the end of its predetermined life or a potential failure is detected.

[0014] Furthermore, the inventory management module is also used to automatically track the specific location and status of each tool in the inventory using IoT-based sensors, thereby achieving dynamic visualization of the inventory and optimizing the inventory layout.

[0015] Furthermore, the inventory management module includes a supplier automatic replenishment unit, which automatically sends purchase or replenishment requests to suppliers based on preset inventory thresholds and real-time usage data to keep inventory levels at an optimal state.

[0016] Furthermore, the interface module is used to achieve direct communication with various sensor devices on the production line, collect data generated during the use of the tool, including cutting force, temperature and vibration data, for real-time monitoring and analysis of the tool status.

[0017] Furthermore, the interface module adopts data encryption and security protocols to ensure that all data exchanged through the module are fully protected to prevent unauthorized access and data leakage.

[0018] Furthermore, the production management module is specifically used for:

[0019] Get the predicted remaining life L of tool i i (t);

[0020] According to the following formula 1, calculate the priority score S of production task k k :

[0021]

[0022] Among them, D k is the delivery urgency of production task k; C k is the customer importance corresponding to production task k; R k is the resource utilization rate corresponding to production task k; P k The expected profit corresponding to production task k; w1, w2, w3 and w4 are weight coefficients and satisfy the following formula 2:

[0023] w1+w2+w3+w4=1 (2)

[0024] Construct the tool demand matrix U and the tool inventory matrix N, where the element U in the tool demand matrix U ik represents the number of tools i required for production task k; the element N in the tool inventory matrix N ij represents the quantity of tool i in inventory location j;

[0025] The production plan is optimized by a mixed integer linear programming model to determine the optimized production plan; wherein the objective function Z of the mixed integer linear programming model is as shown in the following formula 3:

[0026]

[0027] Among them, S k is the priority score S of production task k k ;X k is a binary decision variable indicating whether production task k is executed. A decision variable of 0 indicates that production task k is not executed, and a decision variable of 1 indicates that production task k is executed; C ik U represents the unit time cost of tool i when completing production task k; ik is the usage time of tool i required for production task k; λ and μ are weight coefficients;

[0028] The constraints of the mixed integer linear programming model include:

[0029] The tool inventory constraint is shown in Equation 4 below:

[0030] I i +Yi ≥∑ k U ik ·X k (4)

[0031] Among them, I i represents the number of tool i in the initial inventory; Y i Indicates the number of additional tools i that need to be purchased; U ik represents the number of tools i required to produce task k;

[0032] Tool life constraint, as shown in Equation 5 below:

[0033] ∑ k T k ·X k ≤L i (t) (5)

[0034] Among them, T k represents the usage time of tool i required to complete production task k;

[0035] The machine tool time constraint is shown in Equation 6 below:

[0036] ∑ k T k ·X k ≤T max (6)

[0037] Among them, T max Indicates the maximum available time of the machine tool.

[0038] Furthermore, the predicted remaining life L of the tool i is i (t) is obtained through a prediction model, the formula of the prediction model is shown in the following formula 7:

[0039]

[0040] Where t represents the time α that has passed since tool i was used. i , β i and δ i are model parameters obtained by fitting the data.

[0041] The beneficial effects of the present application mainly include: (1) By real-time monitoring and management of tool life, and dynamically adjusting production plans based on tool usage and inventory data, the system can significantly improve the efficiency of the production process and the efficiency of tool use. This not only reduces machine downtime, but also reduces production delays caused by improper use or excessive wear of tools. (2) The inventory management module uses barcode or QR code technology to track the entry and exit of tools, automatically calculates and reminds to purchase the minimum inventory tools, which helps to maintain the optimal level of inventory, reduce capital occupation, and avoid additional costs caused by excessive or insufficient inventory. (3) The interface module enables the system to interact with CNC programming software, computer-aided manufacturing software, and enterprise resource planning (ERP), manufacturing execution system (MES) and other management systems to achieve seamless data integration. This integration not only improves the accuracy of the data, but also strengthens the function of the decision support system, enabling managers to make more informed decisions based on the latest and most complete information. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of a digital tool management system with intelligent production scheduling provided in the first embodiment of the present application. DETAILED DESCRIPTION

[0043] Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0044] The first embodiment of the present application provides a digital tool management system with intelligent production scheduling. Figure 1 , which is a schematic diagram of the first embodiment of the present application. Figure 1 A first embodiment of the present application provides a digital tool management system with intelligent production scheduling for detailed description.

[0045] The digital tool management system with intelligent production scheduling includes a basic tool management module 101 , an inventory management module 102 , an interface module 103 and a production management module 104 .

[0046] The basic tool management module 101 is used to manage the 2D and 3D graphics of tool units and components, and supports editing and external data import; provides tool assembly management functions; manages cutting parameters for different materials; and monitors and manages tool life in real time.

[0047] The tool basic management module 101 is intended to provide a comprehensive and detailed way to manage the basic data of the tool and support comprehensive control and monitoring of the tool.

[0048] First, the tool basic management module 101 has the ability to manage 2D and 3D graphics of tool units and components. This includes creating and storing digital graphics of each tool, whether by manual drawing or by importing from external data sources. The module supports various graphic file formats, such as DXF, DWG, etc., so that the visual information of the tool can be accurately captured and reproduced. In addition, these graphics can be edited and modified within the module to ensure that the update and maintenance of tool information can be carried out at any time.

[0049] This function supports the creation, storage and editing of 2D and 3D graphics of tools. To achieve this function, the module first integrates a graphics processing engine that can support a variety of industrial standard graphics formats (such as AutoCAD's DXF and DWG formats). By using a graphical user interface (GUI), operators can upload new tool graphics or modify existing graphics. Graphic editing tools allow users to adjust dimensions, add annotations, or make other necessary graphic modifications to ensure the accuracy and up-to-dateness of the graphics.

[0050] Next, the module provides tool assembly management functions, allowing users to systematically classify and record tools and their components. This includes integrating relevant information about the tool and its components, such as part number, specification, assembly sequence, etc., to facilitate tracking and replacement. This function ensures the efficiency and accuracy of the tool system when repairing or replacing it.

[0051] This function is realized through a database management system that stores and manages detailed information of all tools and their components. Each record in the database contains the tool identification number, component list, assembly sequence and specific specifications of each component. Through the integrated management interface, users can easily add new component information or update existing data. In addition, the system also supports barcode or RFID technology to automatically identify and track each tool and component.

[0052] In addition, the tool basic management module 101 also manages cutting parameters for different materials, which involves the setting of key operating parameters such as tool cutting speed, feed rate, cutting depth, etc. By inputting different material types in the module, the system can automatically recommend or adjust the corresponding cutting parameters. This process is based on pre-set algorithms and historical data to optimize tool usage efficiency and processing quality.

[0053] The cutting parameter management of the tool is achieved through a parametric model. The system presets a database containing cutting parameters for different materials, such as cutting speed, feed rate and cutting depth for steel, aluminum or composite materials. When setting a new processing task, the system automatically provides recommended cutting parameters based on the selected material type. These parameters can be optimized by algorithms based on historical processing data to adapt to specific processing conditions.

[0054] Finally, the module enables real-time monitoring and management of tool life. This is done by continuously tracking the use and wear of the tool in actual operation, including calculating the tool usage time, monitoring cutting force and heat output, etc. This information is essential for predicting the remaining life of the tool, planning maintenance time points, and timely tool replacement.

[0055] Tool life monitoring is achieved through sensors and data analysis technology. The system integrates a sensor data acquisition module that can receive various data from the use of tools on the production line in real time, such as temperature, vibration, and load. After analysis and processing, these data are used to calculate the remaining life of the tool and remind the user to replace or maintain the tool when it is close to its life limit. This function is achieved through advanced data analysis and machine learning technology to ensure the optimal use of tools and reduce the risk of unexpected downtime.

[0056] In summary, the basic tool management module 101 not only improves the efficiency and accuracy of tool management through these comprehensive functions, but also provides strong technical support for factory operations, ensuring the continuity and economy of the production process.

[0057] Furthermore, the tool base management module is also used to record a detailed log of each assembly activity, including time, operator, components used and abnormal conditions, for future quality control and auditing.

[0058] In the digital tool management system, the tool basic management module has expanded its functionality to include a key recording mechanism. The core purpose of this mechanism is to record detailed logs of each assembly activity, which not only cover basic operation details such as time and operator, but also the components used and any abnormal situations that occurred. The implementation of this function is crucial to ensure the transparency, traceability of the production process and its ultimate quality control and audit.

[0059] The system is operated through an integrated software platform that can be seamlessly connected to the factory's production line and its information system. When implemented, the system automatically triggers the recording process whenever an assembly activity starts. The first step involved is that the system records the start time of the activity, which is achieved through the system's built-in clock or synchronized to a network time service to ensure the accuracy of time recording.

[0060] Next, authentication of the operator is required, which is usually done by scanning an employee ID card or entering login credentials. The system records this information as the responsible person for that assembly operation, which is critical for tracking operator performance and ensuring operational accountability.

[0061] During the assembly process, operators will follow the preset assembly guidelines and use specific components for assembly. The system automatically records the detailed information of each component used, including model, batch number, and location of use, by scanning the barcode or RFID tag of the component. This tracking method not only strengthens the accuracy of inventory management, but also provides detailed data for quality control.

[0062] In addition, any abnormal situation that occurs during the assembly process must be recorded by the operator in the system. The abnormality can be an assembly error, equipment failure or component defect. The operator needs to describe the nature of the problem in detail in the system and, if possible, attach the corresponding error code or problem snapshot. The system saves this information together with the assembly log, which will become an important basis for handling quality issues and making future improvements.

[0063] All of these data collection and recording processes are designed to be as automated as possible to reduce human error and improve the efficiency of data entry. These log data are stored on a secure server and can be accessed by audit and quality control personnel in a permission-controlled manner.

[0064] Through the implementation of this method, the digital tool management system can not only provide real-time production monitoring, but also support complex data analysis and historical backtracking, bringing significant quality assurance and operational efficiency improvements to manufacturing companies.

[0065] Furthermore, the tool basic management module provides a user interface that allows operators to manually adjust cutting parameters according to specific production tasks and feed back the optimization results to the central database to improve future automatic settings.

[0066] In the digital tool management system, a core function of the tool basic management module is to provide a highly customized user interface. This interface is designed to allow operators to manually adjust the cutting parameters of the tool for specific production tasks, such as cutting speed, feed rate, cutting depth and cutting path. The design and implementation of this function ensures the flexibility and optimization of tool use, while also continuously improving the automatic setting of cutting parameters through a data-driven approach.

[0067] The user interface should be intuitive and easy to use, allowing the operator to quickly select and modify the cutting parameters of the tool. The interface should include the following elements:

[0068] Parameter Selection Tool: The operator can select the cutting parameters that need to be adjusted through a drop-down menu or input field.

[0069] Real-time feedback: To aid the operator in making decisions, the interface should display relevant data and suggested values ​​for the current cutting parameter settings, which may be based on historical performance data or preset process standards.

[0070] Save and apply options: Once parameters have been selected and adjusted, the operator should be able to save the changes and immediately apply them to the corresponding tools on the production line.

[0071] In order to achieve continuous optimization of cutting parameters, all parameter adjustment data needs to be recorded and fed back to the system's central database. This process includes the following key steps:

[0072] Data Capture: Whenever an operator adjusts and saves a tool’s cutting parameters, the system automatically records details of the change, including the parameter adjusted, the magnitude of the adjustment, the time of the adjustment, and operator information.

[0073] Performance tracking: The system should continue to track the performance of the adjusted tool in production, including indicators such as cutting efficiency, tool wear rate and product quality.

[0074] Data analysis and learning: The system uses machine learning algorithms to identify optimal parameter settings by analyzing the collected performance data and parameter changes before and after adjustments. This information is used to update the system's parameter recommendation logic and improve the accuracy of future automatic settings.

[0075] Through this implementation plan, the tool basic management module not only provides operational flexibility, but also improves the intelligence and efficiency of the entire production system through continuous learning and optimization. Such system design ensures that technicians can implement and maintain this system, while also bringing significant improvements in production efficiency and quality control to the manufacturing industry.

[0076] Furthermore, the tool base management module includes an automatic alarm unit for alerting the maintenance team when the tool is approaching the end of its predetermined life or a potential failure is detected.

[0077] In the digital tool management system, the tool basic management module includes a key functional unit: the automatic alarm unit. The main function of this unit is to monitor the use and life of the tool and promptly warn the maintenance team when the tool is approaching the end of its scheduled life or when a potential failure is detected.

[0078] The core function of the automatic alarm unit is to ensure the optimal use and timely maintenance of the tool. The unit continuously collects key performance indicators about each tool by communicating in real time with the sensors equipped with the tool (such as vibration sensors, temperature sensors and wear detection sensors). These data include but are not limited to the tool's usage time, cutting force, temperature rise and wear degree.

[0079] The system regularly analyzes the collected data and uses preset thresholds and algorithms to assess the status of each tool. For example, if the tool wears faster than a safe threshold or temperature anomalies persist, these will be identified by the algorithm as early indicators of failure.

[0080] When the system analysis results show that a tool is about to reach the end of its service life, or detects abnormal performance parameters of the tool, the automatic alarm unit will trigger an alarm. These alarms can be visual and audible, such as displaying a warning message on the display screen of the operator's console and sending an audible alarm through the factory's internal communication system.

[0081] The alert information includes the specific location of the tool, its current status, the specific problem detected, and the recommended maintenance or replacement measures. This information ensures that the maintenance team can respond quickly and accurately and perform necessary maintenance operations.

[0082] In order to effectively implement this automatic alarm unit, the following steps need to be implemented:

[0083] Sensor integration and data collection: All tools should be equipped with necessary sensors, and the system should be able to seamlessly access these sensors and collect data in real time.

[0084] Data storage and analysis platform: There needs to be sufficient data storage capacity and powerful data processing capabilities to handle real-time data from a large number of tools. In addition, data analysis algorithms need to be updated regularly to adapt to new monitoring requirements and technological advances.

[0085] Communication system: The transmission of alarm information needs to rely on a stable and reliable communication system to ensure that the alarm can be conveyed to the maintenance team in a timely manner regardless of the working environment.

[0086] User Interface: The operator interface needs to be designed to be intuitive and easy to use so that the operator can quickly understand the alarm information and take action. Sufficient information should be provided on the interface, such as the specific location of the tool, a description of the problem, and recommended action steps.

[0087] By implementing the above functions, the automatic alarm unit will greatly improve production efficiency and tool safety, and reduce production delays and cost increases caused by tool failures. The design of this system takes into account both technical feasibility and actual production needs, ensuring that it can be widely deployed and effectively used.

[0088] The inventory management module 102 is used to manage the entry and exit of tools using barcodes or QR codes; track tool usage, including order number, product number, tool list number, tool assembly number and cost center; automatically calculate purchase requisitions and provide automatic purchase reminders for the minimum inventory tools; support the creation, approval, editing, publishing, locking and archiving management of tool lists.

[0089] The inventory management module 102 plays a core role in the digital tool management system provided, responsible for efficiently managing the in and out operations of tools, inventory monitoring, and inventory-related data flows. This module has multiple functions aimed at optimizing inventory control, reducing excess inventory, and ensuring sufficient supply of tools on the production line.

[0090] The module first manages the entry and exit of tools by using barcode or QR code technology. Each tool and its components are given a unique identification code when they are manufactured or purchased, which is used to record relevant inventory data, including the time, location and status of entry. By scanning these codes, the system can quickly retrieve relevant information about the tool, including its usage history and current status. This automated tracking method not only improves the accuracy of the data, but also greatly improves the efficiency of operations.

[0091] Furthermore, the module also has the ability to track tool usage, including order number, product number, tool list number, tool assembly number and cost center. This information is collected and stored through an integrated database management system, so that inventory management is not limited to quantity control, but also extends to cost and usage efficiency management. The system allows users to view the frequency of use and maintenance records of each tool, so as to better plan maintenance cycles and budget allocation.

[0092] In addition, the inventory management module can automatically calculate purchase requests and provide automatic purchase reminders when the minimum inventory level is reached. This function is achieved by analyzing the frequency of use and consumption rate of tools through algorithms, ensuring the continuous operation of the production line will not be interrupted by the lack of necessary tools. This algorithm dynamically adjusts the purchase time and quantity based on past usage data and forecast models to optimize inventory levels and reduce excessive capital occupation.

[0093] Finally, the inventory management module also supports the creation, approval, editing, publishing, locking and archiving of tool lists. This process is executed through the document management system in the module, and each step requires users with corresponding permissions to perform. For example, a new tool list must go through the approval process to ensure that all data is accurate before it can be officially entered into the system. Once the tool list is locked, its information is no longer allowed to be changed at will, thus ensuring the integrity and consistency of the data.

[0094] Through the implementation of these functions, the inventory management module (102) ensures the effective management of tools, reduces the risk of production interruption, and helps enterprises improve economic benefits through precise inventory control and cost management.

[0095] Furthermore, the inventory management module is also used to automatically track the specific location and status of each tool in the inventory using IoT-based sensors, thereby achieving dynamic visualization of the inventory and optimizing the inventory layout.

[0096] In the digital tool management system, the inventory management module has been expanded to include a key enhancement: the use of IoT-based sensors to automatically track the specific location and status of each tool in the inventory. The design and implementation of this function ensures a high degree of automation and efficiency in inventory management, significantly improving operational flexibility and responsiveness through dynamic visualization and optimized inventory layout.

[0097] Features that enable automatic tracking and dynamic visualization include:

[0098] 1. Integrated IoT sensors:

[0099] The inventory management module integrates multiple types of IoT sensors, including RFID (radio frequency identification) tags, BLE (Bluetooth Low Energy) beacons and other location sensors. These sensors are attached to each tool and tool box and can send data about the tool location and status in real time. Each sensor is designed to consume very little power, ensuring long-term maintenance-free operation.

[0100] 2. Automatic data collection and processing:

[0101] The data collected by the sensors is automatically transmitted to a central database system. This system uses advanced data processing algorithms to analyze the location information and usage status of the tools in real time, such as in use, standby or requiring maintenance. In addition, the system can detect whether the tools are placed in the wrong location, thereby helping to maintain the organization of inventory.

[0102] 3. Dynamic visual interface:

[0103] The inventory management module includes a dynamic visual interface that displays the floor plan of the warehouse and the real-time location of each tool. Through this interface, warehouse managers can easily view the location and status of all tools and optimize inventory layout efficiently. The interface also supports custom views and filters, such as displaying tools by tool type, frequency of use, or maintenance requirements.

[0104] 4. Optimize inventory layout:

[0105] The system uses the collected data to analyze the usage patterns and movement frequency of tools and automatically makes suggestions for optimizing inventory layout. For example, frequently used tools can be recommended to be placed in more convenient locations, while infrequently used tools can be stored in more remote areas. These suggestions are designed to reduce the time employees spend looking for tools in inventory and improve work efficiency.

[0106] To ensure the effective implementation of the above functions, the following technical and operational requirements must be met:

[0107] Network infrastructure: ensure stable and fast data transmission between sensors and central database.

[0108] High-performance database system: capable of processing large amounts of real-time data and supporting complex data query and analysis.

[0109] User-friendly interface design: ensures that managers and operators can easily use the system without in-depth technical background.

[0110] By implementing these technical solutions, the inventory management module of the digital tool management system with intelligent scheduling can not only improve the management efficiency of inventory, but also greatly improve the fluency and response speed of the entire production process by dynamically optimizing the inventory layout. The design of this system takes into account the latest developments in technology and is closely integrated with the actual needs of the manufacturing industry, ensuring its wide applicability and feasibility of implementation.

[0111] Furthermore, the inventory management module includes a supplier automatic replenishment unit, which automatically sends purchase or replenishment requests to suppliers based on preset inventory thresholds and real-time usage data to keep inventory levels at an optimal state.

[0112] In the digital tool management system, a core extension function of the inventory management module is the integration of the supplier automatic replenishment unit. The design and implementation of this unit is to automate the inventory replenishment process and ensure that the inventory level is always maintained at an optimal state to support continuous production needs without excessive inventory backlog.

[0113] The core function of the supplier automatic replenishment unit is to automatically monitor and adjust inventory levels using advanced inventory management technology. This unit monitors inventory status in real time and automatically initiates replenishment requests based on the set inventory management strategy, reducing manual intervention and improving the efficiency and accuracy of inventory management.

[0114] The implementation process of this unit includes:

[0115] 1. Inventory threshold setting:

[0116] The system allows inventory managers to set minimum and maximum inventory thresholds for each tool. These thresholds are set based on historical usage data, expected production schedules, and potential supply chain delays. Inventory thresholds are set to ensure that sufficient inventory is available for uninterrupted production while avoiding capital constraints caused by excessive inventory.

[0117] 2. Real-time inventory monitoring and data analysis:

[0118] Through integrated sensors and inventory tracking systems, the inventory management module continuously monitors the usage status and remaining quantity of each tool. The system analyzes this data to determine when an item of inventory reaches its preset minimum threshold, triggering the replenishment process.

[0119] 3. Automatically trigger replenishment requests:

[0120] Once it detects that the inventory of a certain tool falls below a set minimum threshold, the supplier automatic replenishment unit automatically sends a purchase or replenishment request to the associated supplier. These requests can be made via electronic data interchange (EDI), automated email systems or directly integrated into the supplier's order processing system.

[0121] 4. Order confirmation and tracking:

[0122] After receiving the supplier's confirmation, the system updates the order status and monitors the supplier's delivery progress. This ensures that the order is processed correctly and the replenishment arrives in time.

[0123] 5. Feedback and continuous optimization:

[0124] The system collects and analyzes the efficiency and effectiveness of the replenishment process, including supplier response time, delivery time and its impact on the production process. Through this data, the system continuously optimizes inventory thresholds and replenishment strategies to cope with changes in production demand and fluctuations in the supply chain.

[0125] To ensure the effective implementation of the above functions, the following technical and operational requirements must be met:

[0126] Inventory management software: The software needs to be highly configurable and scalable to handle complex inventory models and diverse supply chain scenarios.

[0127] Stable data communication system: ensure that data communication with suppliers is fast, secure and error-free.

[0128] User-friendly interface: The interface needs to be simple and intuitive, making it easy for inventory managers to set inventory thresholds and monitor replenishment status and inventory levels.

[0129] Data analysis capabilities: The system should have strong data analysis and machine learning capabilities to support automatic optimization of inventory strategies and decision support.

[0130] Through the implementation of these functions and technologies, the inventory management module of the digital tool management system with intelligent scheduling can achieve efficient automatic inventory replenishment, greatly improve production continuity and economic efficiency, while reducing resource waste and operational errors.

[0131] Interface module 103 is used to realize data interaction with CNC programming software, including importing and exporting tool data; supporting data transmission with computer-aided manufacturing software; and realizing data exchange with enterprise-level management systems including enterprise resource planning systems, manufacturing execution systems, product lifecycle management systems, distributed CNC systems and warehouse management systems.

[0132] The interface module 103 is a critical component of the digital tool management system. Its main function is to ensure that the system can exchange data efficiently with various external systems. The design of this module allows seamless integration and communication with NC programming software, computer-aided manufacturing software, and various enterprise-level management systems such as enterprise resource planning system (ERP), manufacturing execution system (MES), product lifecycle management system (PLM), distributed numerical control system (DNC) and warehouse management system (WMS).

[0133] Specifically, the implementation of the interface module includes several key aspects. First, the module includes data interaction with numerical control (NC) programming software. This function is implemented through standard application programming interfaces (APIs), which can import and export tool data to achieve information consistency and real-time update between tool programming and actual operation. For example, the tool geometry data and cutting parameters can be directly transmitted from the system to the CNC machine tool, and vice versa, and the actual use data of the machine tool such as tool wear can be transmitted back to the system for analysis.

[0134] Secondly, the interface module supports data transfer with computer-aided manufacturing (CAM) software. This includes not only the basic import and export of tool data, but also access to tool paths and process parameters in CAM software. In this way, pre-manufacturing planning and post-manufacturing analysis can share the same data source, ensuring optimization and efficiency improvement of the manufacturing process.

[0135] In addition, the interface module also enables data exchange with a wide range of enterprise-level management systems. For example, integration with the ERP system allows the tool management system to access the enterprise's resource planning and inventory status, thereby making more accurate purchasing and inventory decisions. Connection with the MES system ensures that real-time data from the production line can be used to dynamically adjust the production plan. At the same time, integration with the PLM system helps manage the entire life cycle of the tool from design to scrapping.

[0136] The implementation of the interface module relies on standardized data exchange formats and protocols, such as XML and JSON, as well as industrial communication standards such as OPC UA (Open Platform Communications Unified Architecture). These technologies ensure the security, reliability and scalability of data exchange. In addition, the module also needs to be highly configurable and adaptable so that it can be adjusted according to the specific requirements of different external systems.

[0137] The interface module 103 not only supports the core functions of the system, but also provides the necessary flexibility and scalability, so that the digital tool management system can adapt to various manufacturing environments and technical requirements.

[0138] Furthermore, the interface module is used to achieve direct communication with various sensor devices on the production line, collect data generated during the use of the tool, including cutting force, temperature and vibration data, for real-time monitoring and analysis of the tool status.

[0139] In the digital tool management system, the interface module plays a key role, enabling the system to communicate directly with various sensor devices on the production line. This module is designed to achieve efficient data acquisition and processing in order to collect and analyze key performance data generated by the tool during use, such as cutting force, temperature and vibration. This data is essential for real-time monitoring of tool status, predicting maintenance needs, and optimizing tool use.

[0140] The interface module has the ability to directly connect to a variety of sensing devices on the production line. This includes but is not limited to sensors that are directly connected to or deployed in close proximity to the tool, such as vibration sensors, temperature sensors, and force sensors. These sensors are carefully designed to withstand the harsh environments and extreme conditions of tool use.

[0141] Key performance data collected include:

[0142] 1. Cutting force sensor:

[0143] Cutting force sensors measure and record the forces applied by the tool during the cutting process. This data helps technicians understand how the tool performs when working on different materials and can be used to adjust cutting parameters to optimize performance and extend tool life.

[0144] 2. Temperature sensor:

[0145] Temperature sensors monitor the temperature of the tool during operation. Excessive temperatures may be a sign of insufficient lubrication or excessive tool wear, and timely temperature monitoring can prevent premature tool failure.

[0146] 3. Vibration sensor:

[0147] Vibration data provides important information about the stability of tool operation. Abnormal vibration patterns often indicate tool assembly problems or potential failure.

[0148] The interface module not only collects data, but is also responsible for transmitting this data in real time to the central processing system. Here, specialized analysis software processes and interprets the data, using advanced algorithms to assess the condition of the tool and predict future maintenance needs. These analysis results can be displayed in real time on the monitoring interface and can be used to automatically adjust production parameters or trigger maintenance alarms.

[0149] By implementing these technical solutions, the digital tool management system with intelligent production scheduling can effectively monitor and maintain the tools on the production line in real time, ensuring that the tools operate in the best condition, thereby improving production efficiency and product quality, and reducing downtime and maintenance costs. The design of this system not only reflects the cutting-edge development of tool management technology, but also meets the needs of modern manufacturing for intelligent and automated solutions.

[0150] Furthermore, the interface module adopts data encryption and security protocols to ensure that all data exchanged through the module are fully protected to prevent unauthorized access and data leakage.

[0151] In the digital tool management system, the interface module plays a key data exchange node, processing sensitive data from various sensor devices and systems. To ensure the security of this data, the module uses advanced data encryption and security protocols. This design is to prevent any unauthorized access and data leakage, and ensure the security and integrity of all data exchanged through the module during transmission and storage.

[0152] The design of the interface module takes into account the security of the entire data processing process, from data collection, transmission to final storage. To this end, the following key technical measures are implemented:

[0153] 1. End-to-end encryption:

[0154] Data is encrypted end-to-end throughout the entire transmission process from the source sensor to the central system. This means that once the data is collected from the source device, it is immediately encrypted and remains encrypted until it reaches the destination safely. This process ensures that the data cannot be read or tampered with even if it is intercepted during transmission.

[0155] The encryption algorithms used should comply with internationally recognized security standards, such as AES (Advanced Encryption Standard) 256-bit encryption. These algorithms provide a high level of security that is sufficient to resist most types of network attacks.

[0156] The system implements strict data access control to ensure that only authorized users and systems can access sensitive data. This is achieved through the use of multi-factor authentication, permission management, and user role definition.

[0157] All data transmission is carried out via secure transmission protocols, such as HTTPS and TLS, which can protect the data from being intercepted during the data transmission process.

[0158] The system undergoes regular security audits to identify and patch possible security vulnerabilities. At the same time, security protocols and encryption algorithms are updated based on the latest security research to protect against emerging threats.

[0159] By implementing these security measures, the digital tool management system with intelligent production scheduling can protect key business data from external threats and internal abuse, ensuring the continuity of enterprise operations and the integrity of data. The design of this system not only meets the high standards of data security in the modern manufacturing industry, but also provides a solid foundation for the reliable operation of the system.

[0160] The production management module 104 is used to parse the production control system data, extract the production task list and the tool requirement list; establish the tool BOM record and the types of products processed by the tool; check whether the tool list is complete according to the process document; analyze the tool consumption and generate a procurement plan; adjust the production plan according to the production task list and the tool requirement list, the analysis results of the tool consumption, the tool life data from the tool basic management module, and the current tool inventory status and location information from the inventory management module.

[0161] The production management module 104 plays a core role in the digital tool management system with intelligent production scheduling. It is mainly responsible for parsing the data of the production control system to ensure the efficiency and accuracy of the production process. This module deeply integrates multiple functions of parsing, monitoring and adjusting the production process to meet the modern manufacturing industry's demand for highly accurate and flexible production.

[0162] First, the production management module obtains real-time data from the production line by connecting to the production control system. This data includes but is not limited to the type, quantity, scheduled completion time, and specific information of the required tools of the production task. The data parsing engine in the module converts these production task data into specific operation instructions, such as tool preparation and machine tool settings. This process ensures that production activities can proceed seamlessly according to the predetermined plan.

[0163] Next, the module is responsible for establishing and managing the tool bill of materials (BOM), which includes the type, quantity and specific configuration of the tool required for each production task. The accuracy of the tool BOM record is crucial to ensure production efficiency and tool utilization. This function is achieved through close collaboration with the tool basic management module (101) to ensure that all tool configuration information is up to date and accurate.

[0164] In addition, the production management module is also responsible for checking whether the tool list is complete according to the process documents. By comparing the design documents with the actual tool preparation, the module can promptly identify possible missing items or errors and guide the warehouse to make necessary tool replenishment or replacement to avoid production delays. This inspection process not only improves production reliability, but also reduces the cost waste caused by tool errors.

[0165] In terms of tool usage and consumption, the production management module has the ability to analyze tool wear and tear and generate a purchase plan accordingly. By collecting tool life data from the tool basic management module and inventory status information provided by the inventory management module, the module can predict tool replacement cycles and purchase needs, thereby making purchase preparations in advance and ensuring production continuity.

[0166] Finally, based on the analysis results of the production task list, tool requirement list and tool consumption, the production management module adjusts the production plan to respond to the actual production situation. This includes adjusting production priorities, reallocating resources or adjusting the configuration of the production line. This dynamic adjustment capability is a key feature of the intelligent scheduling system, which allows the manufacturing process to respond to various changes in the market or production with maximum flexibility.

[0167] Furthermore, the production management module is specifically used for:

[0168] Get the predicted remaining life L of tool i i (t);

[0169] According to the following formula 1, calculate the priority score S of production task k k :

[0170]

[0171] Among them, D k is the delivery urgency of production task k; C k is the customer importance corresponding to production task k; R k is the resource utilization rate corresponding to production task k; P k The expected profit corresponding to production task k; w1, w2, w3 and w4 are weight coefficients and satisfy the following formula 2:

[0172] w1+w2+w3+w4=1 (2)

[0173] Construct the tool demand matrix U and the tool inventory matrix N, where the element U in the tool demand matrix U ik represents the number of tools i required for production task k; the element N in the tool inventory matrix N ij represents the quantity of tool i in inventory location j;

[0174] The production plan is optimized by a mixed integer linear programming model to determine the optimized production plan; wherein the objective function Z of the mixed integer linear programming model is as shown in the following formula 3:

[0175]

[0176] Among them, S k is the priority score S of production task k k ;X k is a binary decision variable indicating whether production task k is executed. A decision variable of 0 indicates that production task k is not executed, and a decision variable of 1 indicates that production task k is executed; C ik M represents the unit time cost of tool i when completing production task k; ik is the usage time of tool i required for production task k; λ and μ are weight coefficients;

[0177] The constraints of the mixed integer linear programming model include:

[0178] The tool inventory constraint is shown in Equation 4 below:

[0179] I i +Y i ≥∑ k U ik ·X k (4)

[0180] Among them, I i represents the number of tool i in the initial inventory; Y i Indicates the number of additional tools i that need to be purchased; U ik represents the number of tools i required to produce task k;

[0181] Tool life constraint, as shown in Equation 5 below:

[0182] ∑ k T k ·X k ≤L i (t) (5)

[0183] Among them, T k represents the usage time of tool i required to complete production task k;

[0184] The machine tool time constraint is shown in Equation 6 below:

[0185] ∑ k T k ·X k ≤T max (6)

[0186] Among them, T max Indicates the maximum available time of the machine tool.

[0187] First, the production management module is used to obtain the predicted remaining life L of tool i i (t). The remaining life prediction can be obtained from a range of data sources, including tool usage records, process information of the machining task, and historical data provided by the tool basic management module. With this information, the system can calculate the remaining life of the tool in real time or periodically, thereby avoiding mid-term downtime caused by insufficient tool life during production scheduling.

[0188] In order to effectively manage the priority of production tasks during system scheduling, the production management module needs to calculate the priority score S of each production task k according to a specific formula. k The priority score is calculated according to Formula 1:

[0189]

[0190] Each parameter in the formula represents a different influencing factor: k is the urgency of the production task’s delivery date, representing the time requirement of the task; C k is the importance of the customer, reflecting the service priority of the task to key customers; R k is the resource utilization rate, which indicates the degree of resource utilization of the task; P k is the expected profit of the task, reflecting the economic return of the task. Coefficients w1, w2, w3, and w4 are weights used to adjust different factors, and their sum must satisfy Formula 2:

[0191] w1+w2+w3+w4=1

[0192] This formula ensures that the calculation of the priority score takes into account delivery time, customer importance, resource utilization and expected profit, and is adaptively adjusted according to specific production conditions.

[0193] The production management module then needs to construct the tool demand matrix U and the inventory matrix N. The elements U in the tool demand matrix u are ik represents the number of tools i required to produce task k. The element N in the inventory matrix N ijrepresents the number of tool i in inventory location j. The construction of these matrices is based on the tool list of the production task and the real-time inventory information provided by the inventory management module. The accuracy of the tool demand matrix and inventory matrix is ​​the basis for the successful application of the subsequent mixed integer linear programming model.

[0194] The system uses a mixed integer linear programming model to optimize the production plan and determine the optimal task execution sequence and tool configuration. The objective function Z is defined as follows:

[0195]

[0196] The first term in the objective function represents the cube of the priority score and the decision variable X k The product of k is a binary variable, when X k =1 indicates that task k is executed, X k = 0 means the task is not executed. k Incorporating the cubic value of into the objective function can enhance the influence of high-priority tasks in the optimization process, thereby better meeting the requirements of time-critical and important tasks. i C ik ·M ik ·X k ) is the sum of the tool usage costs during the task completion process, where C ik represents the unit time cost of tool i in completing task k, M ik The usage time of tool i required for producing task k. By optimizing the balance between these two items, an optimal solution can be found between task priority and tool cost.

[0197] To ensure the feasibility of the production plan, the mixed integer linear programming model also introduces a variety of constraints. The first is the tool inventory constraint:

[0198]

[0199] Among them I i is the number of tool i in the initial inventory, Y i is the number of additional tools that need to be purchased, U ik is the number of tools required to produce task k. This constraint ensures that the tool inventory is sufficient to meet the needs of all selected tasks.

[0200] The formula for tool life constraint is as follows:

[0201]

[0202] Where T kThe tool usage time required to complete production task k, L i (t) is the predicted remaining life of tool i at time t. This constraint ensures that the production task is completed within the tool life range and avoids tool failure due to excessive use.

[0203] The machine tool time constraints are as follows:

[0204]

[0205] Where T max is the maximum available time of the machine tool. This constraint ensures that production is planned within the capacity of the machine tool and avoids overloading operation.

[0206] In the production management system, when calculating the priority score of production tasks, several key factors are involved: delivery urgency D k , Customer Importance C k , resource utilization R k , and expected profit P k These factors help decision makers evaluate the relative priority of various production tasks, thereby effectively guiding production scheduling and resource allocation. The following will explain in detail the meaning of each factor and its calculation method.

[0207] The urgency of the delivery date reflects the time sensitivity of the task. The closer the task's delivery date is, the higher its urgency is. Usually, this parameter can be calculated by comparing the difference between the current date and the task's delivery deadline. The specific calculation method can be:

[0208]

[0209] Here, the remaining days is the number of days from the current date to the delivery deadline. 1 is added to avoid division by zero. This approach ensures that the urgency index increases linearly as the deadline approaches, giving higher priority to tasks that are due soon.

[0210] Customer importance refers to the strategic importance of a customer to a business, which can be based on the customer's order frequency, order size, or long-term partnership. Each customer can be assigned an importance score based on the business value they bring to the company. This is usually a quantitative score:

[0211] C k =Customer Rating

[0212] Customer scores can be derived from a combination of historical order data and potential future business value, which may include factors such as the customer's credit rating, historical purchase totals, and the stability of the partnership.

[0213] Resource utilization indicates the degree to which production resources are occupied to complete a task. If a task uses a large amount of scarce resources, its resource utilization is high. This indicator can be calculated using the following formula:

[0214]

[0215] R11 represents the total amount of resources required for the task; R12 represents the total available amount of resources; here, the total amount of resources required for the task refers to the sum of various types of resources (such as machine time, raw materials, etc.) required to complete the task, and the total available amount of resources refers to the total amount of all corresponding resources available within a specific time.

[0216] Expected profit refers to the direct financial gain that is likely to come from completing a specific task. This can usually be calculated by subtracting the cost of the task from the sales revenue:

[0217] P k = Sales revenue – Production cost

[0218] Sales revenue is the total revenue estimated based on the sales price and quantity of the product, and production costs include the sum of raw materials, labor, energy and other direct costs.

[0219] By combining these factors, the production management system can prioritize production tasks more accurately and effectively support the optimization and execution of production plans. These calculations require not only accurate data input, but also efficient data processing capabilities to ensure real-time updates and adjustments to production decisions.

[0220] Optimization of production planning:

[0221] The tool demand matrix U and inventory matrix N are constructed to record the number of tools required for each task U ik and the location and quantity N of each tool in the inventory ij . These data provide the basis for the optimization of production plans.

[0222] Through the mixed integer linear programming model, the module optimizes the production plan according to the priority score, tool usage cost, inventory status and tool life. The objective function is to maximize the production efficiency and economic benefits while ensuring that the usable life of the tool and the running time of the machine tool are not exceeded.

[0223] Tool inventory constraints are used to ensure that the number of tools required for all planned production tasks does not exceed the number available in inventory and the number to be purchased.

[0224] Tool life constraints are used to ensure that the total usage time of tools used in all tasks does not exceed their predicted life, thereby avoiding production quality problems or safety accidents caused by excessive tool wear.

[0225] Machine tool time constraints are used to ensure that the total processing time of all planned production tasks does not exceed the maximum available time of the machine tool to optimize the utilization efficiency of the machine tool and avoid excessive wear.

[0226] By implementing these functions, the production management module of the digital tool management system with intelligent scheduling will greatly improve production efficiency, reduce resource waste, and improve product quality. The design of this system not only reflects the cutting-edge development of tool management technology, but also meets the needs of modern manufacturing for intelligent and automated solutions.

[0227] Furthermore, the predicted remaining life L of the tool i is i (t) is obtained through a prediction model, the formula of the prediction model is shown in the following formula 7:

[0228]

[0229] Among them, α i , β i and δ i are model parameters obtained by fitting the data.

[0230] In the digital tool management system, the predicted remaining life of the tool L i (t) is calculated by a specially designed prediction model. This model is critical because it directly affects the production planning and the timing of tool maintenance, which in turn affects the efficiency and cost of the entire production process.

[0231] Prediction models mentioned:

[0232]

[0233] is an exponential decay model used to estimate the remaining service life of a tool. In this model, time t represents the time that has passed since tool i was used, and α i , β i and δ i are model parameters, which are obtained by fitting historical usage data.

[0234] Parameter fitting includes the following steps:

[0235] Data collection: First, sufficient tool usage data needs to be collected, including tool usage time, usage conditions (such as cutting speed, load, etc.), and the time point when the tool is replaced or fails.

[0236] Parameter estimation: Use statistical or machine learning methods to estimate model parameters. Common methods include least squares, maximum likelihood estimation, or other optimization algorithms, which can help accurately estimate α from historical data.i , β i and δ i The value of .

[0237] Model Validation: After parameter estimation, it is necessary to validate the model. This is usually done by comparing the results predicted by the model with the actual observed tool life to ensure the accuracy and reliability of the model.

[0238] Real-time monitoring and updates:

[0239] During the production process, tool usage data is collected in real time and used to continuously update model parameters. This dynamic update can improve the accuracy of predictions and adapt to changes in tool wear rates.

[0240] Tool life prediction: At any given time, the predicted remaining life of each tool can be calculated using the latest model parameters. This information is essential for planning maintenance and tool replacement, helping to reduce production interruptions and improve production efficiency.

[0241] Implementing this kind of predictive model requires ensuring the following aspects:

[0242] Computational resources: Because the model parameters need to be fitted from a large amount of data, powerful computing resources are required to process the data and perform complex calculations.

[0243] High-quality data collection system: Ensuring the accuracy and completeness of data is the key to the success of the model. A reliable data collection system is needed to monitor the use of the tool in real time.

[0244] From the above description, it can be seen that the prediction model of tool remaining life not only has high technical requirements, but also has extremely high value in practical applications. It can help enterprises optimize production processes, reduce unnecessary costs, and improve product quality and customer satisfaction. The design and implementation of this model makes the digital tool management system with intelligent scheduling an important tool in modern manufacturing.

[0245] Furthermore, the production management module realizes tool allocation optimization through the following steps:

[0246] According to the following formula 11, calculate the efficiency score E of tool m m :

[0247]

[0248] Among them, F m is the current cutting force of tool m; G m is the real-time temperature of tool m; H mis the vibration amplitude of tool m; y1, y2, y3 are weight coefficients used to balance different parameters and satisfy y1+y2+y3=1;

[0249] According to the following formula 12, the resource requirement matching score M of each task n for tool m is calculated: mn :

[0250] M mn =z1·R n +z2·S n +z3·T n (12)

[0251] Among them, R n is the processing time requirement of task n; S n is the accuracy requirement of the task; T n is the tool life requirement of the task; z1, z2, and z3 are coefficients used to adjust the weights of different task requirements;

[0252] According to the following formula 13, calculate the matching score P between tool m and task n: mn :

[0253] P mn =w1·E m +w2·M mn (13)

[0254] Among them, w1 and w2 are weight coefficients that balance tool efficiency and task requirements;

[0255] According to the adaptation score P of the tool task combination mn , dynamically adjust tool allocation and priority.

[0256] In the digital tool management system with intelligent production scheduling, the control module 104 plays a key role. Its main function is to achieve dynamic allocation and priority adjustment of tools through a series of calculations and optimization processes. The implementation of the control module 104 needs to fully consider the performance status of the tool and the specific requirements of the task, so as to ensure efficient production planning.

[0257] The control module 104 is first responsible for calculating the efficiency score E of each tool. m , which is the basis of the optimization process. The score is calculated using formula 11:

[0258]

[0259] Among them, F m represents the current cutting force of tool m, which reflects the force applied by the tool during the actual machining process; G m is the real-time temperature of tool m, reflecting the thermal load of the tool under working condition; and Hm is the vibration amplitude of tool m, which is used to measure whether the tool remains stable during the machining process. Parameters y1, y2, and y3 are weight coefficients used to balance these three different factors, and their sum is equal to 1 to ensure the balance and rationality of the score. Through this formula, the control module 104 can evaluate the comprehensive performance score of each tool, thereby providing a reference for subsequent task matching and allocation.

[0260] After obtaining the tool usage efficiency score, the control module 104 further calculates the task resource requirement matching score M mn The matching score is used to evaluate the suitability of task n for tool m and is calculated using formula 12:

[0261] M mn =z1·R n +z2·S n +z3·T n

[0262] In this formula, R n represents the processing time requirement of task n, which is usually determined by the process path length and workpiece material of the task; S n Represents the accuracy requirement of the task, which is determined by the processing tolerance and surface quality requirements of the task; T n Indicates the requirement of the task on the tool life, which usually depends on the processing depth and material hardness of the task. The coefficients z1, z2, and z3 are used to adjust the influence of these three factors on the matching score, and their sum can be 1. Through this calculation, the control module 104 can quantify the demand of the task on the tool and provide data support for the adaptation evaluation of the tool and the task.

[0263] Obtain tool efficiency score E m The resource requirement matching score M of the task mn After that, the control module 104 continues to calculate the tool and task adaptation score P mn , which is used to determine the best combination of tool and task. The adaptation score is calculated according to formula 13:

[0264] P mn =w1·E m +w2·M mn (13)

[0265] Wherein, w1 and w2 are weight coefficients for balancing tool efficiency and task requirements, and their sum is equal to 1 to ensure a reasonable match between tool performance and task requirements. The adaptation score is used to evaluate whether tool m is suitable for performing task n, and how to prioritize among similar tools. In the system implementation process, the control module 104 selects the tool m based on the adaptation score P. mnThe maximum values ​​are prioritized and the tool allocation is dynamically adjusted through the optimization algorithm.

[0266] In actual applications, the control module 104 continuously collects and updates the state parameters of the tool and the demand information of the task in real time, thereby ensuring that the tool allocation process is dynamic and adaptable. For example, when a new task is added to the production plan, the system will recalculate the adaptation scores of all tools and the new task, and adjust the scheduling of the tools according to the new priority. At the same time, the control module 104 will also monitor the state changes of the tool, such as wear, temperature rise, etc., to adjust its adaptation score, and replace the allocated tool when necessary. Through the above steps, the control module 104 realizes the intelligent dynamic allocation of tools, which not only improves the efficiency of tool use, but also improves the adaptability and flexibility of the entire production process.

[0267] Although the present application is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present application. Any technical personnel in this field may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims

1. A digital tool management system with intelligent production scheduling, characterized in that: include: Tool basic management module, used to manage 2D and 3D graphics of tool units and components, and supports editing and external data import; Provide tool assembly management function; Manage cutting parameters for different materials; monitor and manage tool life in real time; Inventory management module, which uses barcodes or QR codes to manage tool in and out of the warehouse; tracks tool usage, including order number, product number, tool list number, tool assembly number and cost center; automatically calculates purchase requisitions and provides automatic purchase reminders for minimum inventory tools; supports the creation, approval, editing, publishing, locking and archiving of tool lists; Interface module, used to realize data interaction with CNC programming software, including importing and exporting tool data; supports data transmission with computer-aided manufacturing software; Realize data exchange with enterprise-level management systems including enterprise resource planning systems, manufacturing execution systems, product lifecycle management systems, distributed numerical control systems and warehouse management systems; Production management module, used to parse production control system data and extract production task lists and tool requirement lists; Establish tool BOM records and the types of products processed by the tools; Check whether the tool list is complete according to the process documents; analyze tool consumption and generate a purchase plan; The production plan is adjusted according to the production task list and tool requirement list, the analysis results of tool consumption, the tool life data from the tool basic management module, and the current tool inventory status and location information from the inventory management module.

2. The digital tool management system with intelligent production scheduling according to claim 1 is characterized in that: The tool base management module is also used to record a detailed log of each assembly activity, including time, operator, components used and exceptions, for future quality control and auditing purposes.

3. The digital tool management system with intelligent production scheduling according to claim 1 is characterized in that: The tool base management module provides a user interface that allows operators to manually adjust cutting parameters according to specific production tasks and feed the optimization results back to the central database to improve future automatic settings.

4. The digital tool management system with intelligent production scheduling according to claim 1 is characterized in that: The tool base management module includes an automatic alarm unit for alerting the maintenance team when a tool is approaching the end of its predetermined life or a potential failure is detected.

5. The digital tool management system with intelligent production scheduling according to claim 1 is characterized in that: The inventory management module is also used to automatically track the specific location and status of each tool in the inventory using IoT-based sensors, thereby achieving dynamic visualization of the inventory and optimizing the inventory layout.

6. The digital tool management system with intelligent production scheduling according to claim 1 is characterized in that: The inventory management module includes a supplier automatic replenishment unit, which automatically sends purchase or replenishment requests to suppliers based on preset inventory thresholds and real-time usage data to keep inventory levels at an optimal state.

7. The digital tool management system with intelligent production scheduling according to claim 1 is characterized in that: The interface module is used to achieve direct communication with various sensor devices on the production line and collect data generated during the use of the tool, including cutting force, temperature and vibration data, for real-time monitoring and analysis of the tool status.

8. The digital tool management system with intelligent production scheduling according to claim 1 is characterized in that: The production management module achieves tool allocation optimization through the following steps: According to the following formula 11, calculate the efficiency score E of tool m m : Among them, F m is the current cutting force of tool m; G m is the real-time temperature of tool m; H m is the vibration amplitude of tool m; y1, y2, y3 are weight coefficients used to balance different parameters and satisfy y1+y2+y3=1; According to the following formula 12, calculate each task n Tool Alignment m The resource demand matching score M mn : M mn =z1·R n +z2·S n +z3·T n (12) Among them, R n is the processing time requirement of task n; S n is the accuracy requirement of the task; T n is the tool life requirement of the task; z1, z2, and z3 are coefficients used to adjust the weights of different task requirements; According to the following formula 13, calculate the matching score P between tool m and task n: mn : P mm =w1·E m +w2·M mn (13) Among them, w1 and w2 are weight coefficients that balance tool efficiency and task requirements; According to the adaptation score P of the tool task combination mn , dynamically adjust tool allocation and priority.

9. The digital tool management system with intelligent production scheduling according to claim 1 is characterized in that: The production management module is specifically used for: Get the predicted remaining life L of tool i i (t); According to the following formula 1, calculate the priority score S of production task k k : Among them, D k is the delivery urgency of production task k; C k is the customer importance corresponding to production task k; R k is the resource utilization rate corresponding to production task k; P k The expected profit corresponding to production task k; w1, w2, w3 and w4 are weight coefficients and satisfy the following formula 2: w1+w2+w3+w4=1 (2) Construct the tool demand matrix U and the tool inventory matrix N, where the element U in the tool demand matrix U ik represents the number of tools i required for production task k; the element N in the tool inventory matrix N ij represents the quantity of tool i in inventory location j; The production plan is optimized by a mixed integer linear programming model to determine the optimized production plan; wherein the objective function Z of the mixed integer linear programming model is as shown in the following formula 3: Among them, S k is the priority score S of production task k k ;X k is a binary decision variable indicating whether production task k is executed. A decision variable of 0 indicates that production task k is not executed, and a decision variable of 1 indicates that production task k is executed; C ik M represents the unit time cost of tool i when completing production task k; ik is the usage time of tool i required for production task k; λ and μ are weight coefficients; The constraints of the mixed integer linear programming model include: The tool inventory constraint is shown in Equation 4 below: I i +Y i ≥∑ k U ik ·X k (4) Among them, I i represents the number of tool i in the initial inventory; Y i Indicates the number of additional tools i that need to be purchased; U ik represents the number of tools i required to produce task k; Tool life constraint, as shown in Equation 5 below: ∑ k T k ·X k ≤L i (t) (5) Among them, T k represents the usage time of tool i required to complete production task k; The machine tool time constraint is shown in Equation 6 below: ∑ k T k ·X k ≤T max (6) Among them, T max Indicates the maximum available time of the machine tool.

10. The digital tool management system with intelligent production scheduling according to claim 9, characterized in that: The predicted remaining life L of the tool i i (t) is obtained through a prediction model, the formula of the prediction model is shown in the following formula 7: Where t represents the time α that has passed since tool i was used. i , β i and δ i are model parameters obtained by fitting the data.

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