Intelligent CNC tool management: Study on the improvement of efficiency of digital management of high-value tool consumables and tool scrap management in workshops

I. Introduction

In modern manufacturing, CNC (Computer Numerical Control) machining technology has become one of the core production methods. As CNC cutting tools are the key consumables that enable this technology to achieve high-precision, high-efficiency machining, the quality of their management directly impacts workshop productivity, cost control, and product quality. Traditional tool management methods often rely on manual record-keeping and empirical judgment. When faced with increasingly complex production tasks and a wide variety of high-value cutting tools, these methods reveal numerous issues, such as inaccurate tool inventory, difficulties in monitoring tool life, and unreasonable tool disposal practices. The emergence of intelligent CNC cutting tool management systems, leveraging digital technology, provides an effective solution to these problems, improving the efficiency of managing high-value cutting tools on the shop floor and enabling scientific management of tool disposal.

II. Current Status and Issues in the Management of High-Value Cutting Tools and Consumables in the Workshop

(1) Disorganized Inventory Management

1. The data is inaccurateManually recording information on cutting tools entering and leaving inventory is prone to errors such as clerical mistakes and omissions. For example, when cutting tools are issued, staff may forget to record the transaction promptly, causing the quantity shown in the inventory system to differ from the actual inventory. Such inaccurate data can mislead procurement decisions, potentially leading to excess inventory or shortages.
2. Excess Inventory and Shortages CoexistDue to a lack of accurate information on the actual usage of cutting tools, procurement plans are often based on experience. Some infrequently used cutting tools may accumulate in inventory due to over-purchasing, tying up significant amounts of capital; meanwhile, delays in restocking frequently used cutting tools can cause production stoppages and disrupt the workshop’s production schedule.

(2) Difficulties in Managing Tool Life

1. Lack of real-time monitoringTraditional methods make it difficult to monitor tool wear in real time during the machining process. Tool life is primarily determined based on the operator’s experience; tools are replaced only when wear reaches a level that affects machining quality. This can lead to quality issues in some products and also fails to fully utilize the tool’s effective service life.
2. Inaccurate predictionPredicting tool life based solely on historical data and experience results in significant errors. There are variations in tool quality across different batches, and changes in machining processes and materials also affect tool life, making it difficult for the predictions to accurately reflect actual conditions.

(3) Inappropriate Management of Scrap Cutting Tools

1. Unclear Scrap StandardsCurrently, the decision to scrap cutting tools is primarily based on vague criteria such as the quality of the machined products and the visible wear on the tools. The lack of scientific, quantifiable criteria for scrapping has led to some tools that still have some usable value being scrapped prematurely, thereby increasing production costs; conversely, some tools that have reached the scrapping criteria are not replaced in a timely manner, which affects machining quality and production efficiency.
2. Non-standard scrapping proceduresThe process for scrapping cutting tools typically lacks rigorous review and documentation. The scrapping of cutting tools is often arbitrary, with no detailed records kept of information such as the reasons for scrapping or the duration of use. This makes it difficult for companies to summarize and analyze cutting tool usage and to derive effective insights for production improvements from the management of scrapped cutting tools.

(4) Poor Communication

1. Issues with Interdepartmental CollaborationInformation flow between different departments within the workshop—such as the production, procurement, and cutting tool management departments—is often untimely and inaccurate. Changes in the production department’s demand for cutting tools are not promptly communicated to the procurement department, resulting in procurement delays; furthermore, the cutting tool management department is unable to effectively share information on cutting tool inventory and usage, which hinders collaboration among departments.
2. Disconnect Between Equipment and Management SystemsCNC machines contain certain tooling information, but this information has not been effectively integrated with the shop floor tool management system. The tool usage data collected by machine operators during the machining process cannot be fed back to the management system in real time, preventing the system from making tool management decisions based on actual machining data.

III. Principles and Components of an Intelligent CNC Tool Management System

(1) System Principles

The intelligent CNC cutting tool management system leverages technologies such as the Internet of Things (IoT), sensors, and big data analytics to enable comprehensive management of CNC cutting tools. By installing various sensors on the cutting tools, tool holders, or machine tools, the system collects real-time usage data, such as cutting time, cutting force, and vibration frequency. This data is transmitted via a wireless network to a central server, where big data analytics algorithms are used to assess and predict the wear status and remaining service life of the cutting tools. At the same time, the system integrates with the workshop’s production management system, procurement system, and other systems to enable information sharing and collaboration. Based on the actual usage and inventory status of the cutting tools, it automatically generates instructions such as procurement plans and tool replacement reminders, thereby achieving intelligent and automated cutting tool management.

(2) System Architecture

1. Hardware Section
   • Cutting Tool Sensor: These include optical sensors for monitoring tool wear, piezoelectric sensors for detecting cutting forces, and accelerometers for measuring vibration. These sensors can capture key parameters of the tool in real time during the machining process, providing data to support tool condition assessment. For example, optical sensors can directly reflect the tool’s wear status by detecting the degree of wear on the tool’s cutting edge.
   • Tool Holder Identification Device: Using RFID (Radio Frequency Identification) technology, an RFID tag is installed on the tool shank. The tag stores basic tool information, such as the tool model, specifications, and material. The machine tool is equipped with an RFID reader/writer; when a tool is mounted on the machine tool, the system automatically reads the tool information and transmits it to the management system, enabling rapid tool identification and location.
   • Data Collection Terminal: Responsible for collecting signals from various sensors, converting them into digital signals, and transmitting them to a central server via wired or wireless networks. The data acquisition terminal features data preprocessing capabilities, enabling it to perform preliminary filtering and organization of the collected data, thereby reducing the transmission of invalid data.
2. Software Section
   • Tool Management Software: This is the system’s core software, featuring functional modules such as tool inventory management, tool life management, tool scrap management, and report generation. In terms of inventory management, it updates tool receipt and shipment information in real time and provides inventory alert functionality; For tool life management, it predicts tool life based on sensor data and data analysis models to generate tool replacement schedules; for tool scrapping management, it automatically flags tools that meet predefined scrapping criteria and records relevant scrapping information.
   • Data Analysis Software: Utilize big data analytics to conduct in-depth mining and analysis of collected cutting tool usage data. By establishing models for tool wear and service life prediction, identify the relationships between tool wear and machining parameters, tool materials, and workpiece materials, thereby providing a scientific basis for cutting tool management. For example, by analyzing a large volume of tool usage data, we can determine the reasonable service life range for tools under different machining processes, thereby improving the accuracy of tool life predictions.
   • System Integration Interfaces: Used to integrate the intelligent CNC cutting tool management system with other shop floor information systems (such as the production management system, procurement system, and equipment management system). Through standardized data interfaces and communication protocols, it enables data sharing and interaction among these systems, ensuring that cutting tool management is aligned with the shop floor’s overall production operations. For example, tool inventory information is synchronized in real time with the procurement system, enabling the procurement department to stay informed of tool inventory levels and plan purchases accordingly.

IV. How Intelligent CNC Tool Management Systems Improve Workshop Management Efficiency

(1) Optimizing Inventory Management

1. Real-time, accurate inventory dataBy collecting real-time data on cutting tools entering and leaving the warehouse, the system automatically updates inventory data to ensure that inventory levels accurately reflect actual conditions. Staff can check tool inventory status at any time via computer terminals or mobile devices, providing an accurate basis for procurement decisions. At the same time, the inventory alert feature notifies relevant personnel in a timely manner based on preset upper and lower inventory limits, prompting them to restock or address excess inventory, thereby preventing stockouts or excess inventory.
2. Smart Procurement PlanBased on actual tool usage and inventory data, the system uses data analysis models to automatically generate scientifically sound and reasonable procurement plans. These plans not only take current inventory levels into account but also factor in production schedules and tool life predictions, ensuring that the procured tools meet production needs without causing excess inventory. For example, based on recent production orders and tool consumption rates, the system forecasts the demand for various types of tools over a future period and provides the procurement department with detailed purchase lists and recommended procurement timelines.

(2) Precise Tool Life Management

1. Real-Time Status MonitoringThe tool sensor continuously monitors key parameters of the tool during the machining process. By analyzing these parameters, the management system tracks the tool’s wear status in real time. As soon as tool wear approaches a preset threshold, the system immediately issues a warning, notifying the operator to replace the tool promptly and prevent excessive wear from compromising machining quality. For example, when cutting forces suddenly increase or vibration frequencies become abnormal, the system can determine that the cutting tool may be damaged or excessively worn and promptly alert the operator to shut down the machine for inspection.
2. Accurate Lifespan PredictionUsing big data analytics, the system’s tool life prediction model comprehensively considers various factors—such as tool material, machining processes, and workpiece material—to accurately predict tool life. Compared to traditional empirical prediction methods, prediction accuracy has been significantly improved. This enables companies to prepare for tool replacement in advance, optimize production schedules, and avoid production interruptions caused by unexpected tool failure. For example, when machining a batch of complex parts, the system accurately predicts the remaining tool life before the batch is completed based on the tool’s usage history and current machining parameters, allowing companies to prepare replacement tools in advance.

(3) Improving Production Efficiency

1. Reduce DowntimeThrough precise tool life management and inventory management, downtime caused by excessive tool wear or inventory shortages is avoided. The system issues tool replacement alerts in advance, allowing operators to replace tools promptly during production breaks and ensuring production continuity. According to statistics, after implementing the intelligent CNC tool management system, downtime in the workshop caused by tool-related issues was reduced by 30% – 50%, effectively improving equipment utilization and production efficiency.
2. Optimize Machining ParametersThe results of data analysis software’s analysis of tool usage data can also provide a reference for optimizing machining parameters. By analyzing tool wear and machining efficiency under different machining parameters, companies can adjust parameters such as cutting speed and feed rate to improve machining efficiency and extend tool life while ensuring machining quality. For example, data analysis may reveal that, for a specific machining task, appropriately reducing the cutting speed can significantly extend tool life while having minimal impact on machining efficiency; companies can then adjust their machining processes accordingly.

(4) Strengthen Information Sharing and Coordination

1. Interdepartmental Information SharingThe intelligent CNC cutting tool management system is integrated with the information systems of various departments within the workshop, enabling real-time sharing of cutting tool-related information among the production, procurement, and cutting tool management departments. The production department can monitor cutting tool inventory and replacement schedules in real time to optimize production scheduling; The procurement department procures cutting tools in a timely manner based on the procurement plans generated by the system, ensuring the smooth progress of production; the cutting tool management department can comprehensively monitor tool usage and effectively allocate and maintain the tools. Collaboration among departments has become more seamless, reducing communication costs and work errors caused by poor information flow.
2. Integration of Equipment and Management SystemsThe system tightly integrates CNC equipment with the tool management system, enabling real-time data exchange between the equipment and the management system. Tooling issues identified by machine operators during the machining process can be promptly reported to the management system, which then adjusts its tooling management strategy based on this feedback. At the same time, the management system can send information—such as tool change instructions—directly to the machine, guiding operators through the process and thereby improving work efficiency and accuracy.

V. Tool Scrap Management in the Intelligent CNC Tool Management System

(1) Clarify the Scrap Standards

1. Quantitative Scrap CriteriaThe intelligent CNC cutting tool management system establishes scientific, quantifiable criteria for tool scrapping by analyzing data on tool wear, machining quality, and other factors. In addition to traditional indicators such as tool wear and damage, the system also takes into account quality-related metrics such as machining accuracy and surface roughness. For example, when tool wear reaches a certain percentage of the tool’s initial dimensions and the surface roughness of the machined product exceeds the specified range, the system determines that the tool meets the criteria for scrapping.
2. Dynamically Adjusted StandardsDepending on the machining task, tool material, and workpiece material, the system can dynamically adjust the tool wear-out criteria. For high-precision machining tasks, the tool wear-out criteria are more stringent; for general machining tasks, however, the criteria are relatively lenient. At the same time, as tool usage data continues to accumulate and is analyzed, the system can optimize and adjust the wear-out criteria to better align with actual production conditions.

(2) Standardizing the Scrapping Process

1. Automatic Scrapping TriggerWhen a cutting tool meets the set scrap criteria, the system automatically triggers the tool scrap process. In the cutting tool management software, the tool is marked as scrapped, and detailed information—such as the reason for scrapping, duration of use, and number of parts machined—is recorded. This information provides a crucial basis for companies to analyze tool usage and improve tool management.
2. Strict Review ProcessThe tool scrapping process includes strict review procedures. First, the system automatically generates a scrapping request, which is submitted to the head of the tool management department for review. Based on the tool usage data and reason for scrapping provided by the system, and in light of actual production conditions, the head determines whether to approve the scrapping. Once approved, the scrap tools enter the disposal process, ensuring that every scrap tool undergoes rigorous review to prevent erroneous or unjustified scrapping.

(3) Analysis and Improvement of Scrap Cutting Tools

1. Data Statistics and AnalysisThe system collects and analyzes data on scrapped cutting tools, such as the distribution of reasons for scrapping, the scrapping rates of different tool models, and average service life. Through this data analysis, companies can identify common issues encountered during tool use and pinpoint the key factors affecting tool life and scrapping. For example, if an analysis reveals that a particular tool model is frequently scrapped due to breakage under a specific machining process, the company can further investigate whether that process subjects the tool to excessive impact, thereby identifying potential improvements.
2. Guidance on Tool Selection and Process ImprovementBased on the analysis results of worn-out cutting tools, companies can optimize their tool selection strategies. For tool models that frequently experience issues, consider switching to a more suitable tool brand or model. At the same time, for machining process issues that lead to premature tool failure, companies can implement process improvements, adjust machining parameters, or optimize toolpaths to extend tool life and reduce tooling costs. For example, by analyzing scrapped cutting tools, a company discovered that tools were wearing out too quickly in a particular machining process. After implementing process improvements and adopting more appropriate cutting parameters, the company extended tool life by 30% while simultaneously reducing machining costs.

VI. Case Study on the Implementation of an Intelligent CNC Tool Management System

(1) Background of the Company in the Case Study

[Company Name] is a large machinery manufacturing company that primarily produces various types of precision components. The company operates multiple CNC machining production lines and uses a wide variety of cutting tools, resulting in high monthly tool procurement costs. Before implementing the intelligent CNC tool management system, the shop floor faced numerous tool management issues—such as severe inventory backlogs, disorganized tool life management, and unreasonable tool scrapping—which led to low production efficiency and persistently high production costs.

(2) Implementation Process

1. Requirements Research and PlanningThe company established a project team composed of shop floor managers, technical staff, and cutting tool managers to conduct a comprehensive survey of the current state of cutting tool management in the shop floor and analyze existing issues and needs. Based on the survey results, the company developed an implementation plan for the intelligent CNC cutting tool management system, clearly defining the project objectives, implementation steps, and expected outcomes.
2. System Selection and ProcurementThe project team conducted research and evaluations of several suppliers of intelligent CNC tool management systems on the market. After comprehensively considering factors such as system functionality, technical capabilities, project experience, and after-sales service, they selected a specialized supplier. Following the signing of the contract with the supplier, both parties established a joint project team to jointly advance the project’s implementation.
3. Hardware Installation and DebuggingIn accordance with the system design plan, hardware devices such as sensors and RFID readers are installed on CNC machines, cutting tools, and tool holders. The installation process is carried out in strict accordance with technical specifications to ensure that the equipment is securely installed and operates stably. After installation, the hardware devices were commissioned to test the accuracy of sensor data collection, the recognition rate of the RFID readers, and other parameters, ensuring that the hardware operates properly.
4. Software Deployment and Custom DevelopmentDeploy system software—such as cutting tool management software and data analysis software—on the company’s servers, and customize the software based on the company’s actual business processes and management needs. For example, within the tool management software, we configure functional modules such as inventory alert rules and tool scrap criteria that align with the company’s specific production characteristics. At the same time, we develop system integration interfaces to enable integration with the company’s existing production management and procurement systems.
5. Staff Training and System LaunchThe supplier provides comprehensive training to relevant company personnel, covering topics such as hardware operation, software system usage, and data analysis methods. The training combines theoretical instruction, on-site demonstrations, and hands-on practice to ensure that employees become proficient in using the system. Upon completion of the training, the intelligent CNC tool management system was officially launched. During the initial rollout phase, the project team assigned dedicated personnel to provide on-site guidance and resolve issues, ensuring a smooth transition to the new system.

(3) Implementation Results

1. Optimizing Inventory ManagementInventory accuracy improved from 80% to over 98%, and capital tied up in excess inventory decreased by 40%. Through intelligent procurement planning, tool procurement has become more rational, effectively preventing stockouts and ensuring the smooth operation of production.
2. Improving Tool Life ManagementThe accuracy of tool life prediction has improved by 30%, and machining quality issues caused by excessive tool wear have been reduced by 50%. The system’s proactive tool replacement alerts enable operators to replace tools in a timely manner, reducing downtime caused by tool failure and increasing equipment utilization by 25%.
3. Guidelines for the Management of Scrap Cutting ToolsScientific and quantifiable standards for tool scrapping were established, significantly improving the rationality of tool scrapping decisions. By analyzing data on scrapped tools, the company optimized tool selection and machining processes, reducing tool costs by 20%.
4. Overall Production Efficiency ImprovesProduction efficiency in the workshop has increased by more than 30%, and product quality has steadily improved. Communication between departments has become smoother, collaboration has become significantly more efficient, and the company’s overall competitiveness has been enhanced.

VII. Challenges and Strategies for Implementing Intelligent CNC Tool Management Systems

(1) Technical Aspects

1. Sensor Reliability IssuesTool sensors operate in complex machining environments and may be affected by factors such as coolant, high temperatures, and vibration, which can lead to inaccurate sensor data or sensor failure. This can impact the accurate assessment of tool condition and service life prediction.
   • Response Strategies: Select sensor products that are reliable and have a high protection rating, and choose the appropriate model based on the processing environment. Perform regular maintenance and calibration on the sensors to ensure the accuracy of the sensor data. Additionally, equip critical sensors with backup sensors so that, in the event of a failure in the primary sensor, a timely switch can be made to ensure the system continues to operate normally.
2. Optimization of Data Analysis ModelsTool wear and service life are influenced by a variety of factors, making it challenging to establish an accurate data analysis model. As factors such as machining processes and tool materials change, existing data analysis models may need to be continuously optimized to improve the accuracy of tool life predictions and condition assessments.
   • Response Strategies: Strengthen cooperation with research institutions or professional data analysis teams to continuously study and improve data analysis models. Use more processed data to train and validate the models, thereby enhancing their accuracy and adaptability. Regularly evaluate and optimize the data analysis models, adjusting model parameters based on actual production conditions to ensure that the models accurately reflect the actual usage of cutting tools.

(2) Personnel

A Shift in Attitudes Among StaffThe introduction of an intelligent CNC cutting tool management system requires a shift in employees’ traditional attitudes toward cutting tool management and their work methods; some employees may be resistant to the change.

Employee Skills DevelopmentIntelligent CNC tool management systems involve new technologies such as the Internet of Things and data analytics, and place high demands on employees' skills. Some employees may lack the relevant knowledge and skills, making it difficult for them to adapt to the operation and management of the new system.

Response Strategies: Develop a comprehensive employee training program that includes training on the fundamentals of new technologies, system operations, and data analysis. Training methods may include in-house training, sessions led by external experts, and online learning, among others, to meet the diverse learning needs of employees. At the same time, encourage employees to engage in self-directed learning by providing learning resources and incentives to help them enhance their skill levels.