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 the modern manufacturing industry, CNC (Computer Numerical Control) machining technology has become one of the core production means. CNC tooling as this technology to achieve high-precision, high-efficiency machining of the key consumables, the management of its strengths and weaknesses directly affect the workshop's production efficiency, cost control and product quality. Traditional tool management, often relying on manual records and empirical judgment, in the face of increasingly complex production tasks and a wide range of high-value tool consumables, exposed a number of problems, such as inaccurate tool inventory, difficult to monitor the tool life, tool scrapping unreasonable. The emergence of intelligent CNC tool management system, with the help of digital technology, provides an effective way to solve these problems, realizing the improvement of the efficiency of high-value tool consumables management in the workshop and the scientific management of tool scrapping.

II. Current status and problems in the management of high-value tool consumables in workshops

(i) Disorganized stockpile management

1. Inaccurate dataManual recording of tool access information is prone to clerical errors and omissions. For example, when a tool is picked up, staff may forget to register it in a timely manner, resulting in discrepancies between the quantity shown in the inventory system and the actual inventory. Such inaccurate data can mislead purchasing decisions, and inventory backlogs or shortages may occur.
2. Inventory backlogs and shortages coexistDue to the lack of accurate grasp of the actual use of tools, procurement plans are often made on the basis of experience. Some infrequently used tools may be over-purchased and backlogged in the warehouse, taking up a lot of money; while commonly used tools may not be replenished in a timely manner, resulting in production stagnation, affecting the workshop production schedule.

(ii) Difficulty in tool life management

1. Lack of real-time monitoringThe traditional way is difficult to real-time tracking tool wear in the machining process. Tool life mainly relies on the operator's experience to judge, when the tool is worn to a certain degree to affect the quality of machining, before replacement, which may lead to some of the products have quality problems, but also failed to make full use of the effective life of the tool.
2. Inaccurate predictionsPredicting tool life based only on historical data and experience is subject to large errors. Differences in the quality of different batches of tools, changes in machining processes and machining materials can also affect tool life, making it difficult to accurately reflect the actual situation in the prediction results.

(iii) Unreasonable management of tool obsolescence

1. Unclear end-of-life criteriaAt present, tool scrapping is mainly based on the quality of processed products, tool wear appearance and other fuzzy standards. The lack of scientific quantitative end-of-life indicators, resulting in some still have a certain value of the tool prematurely scrapped, increasing production costs; while some have reached the end of the standard tool has not been replaced in a timely manner, affecting the quality of processing and production efficiency.
2. Unstandardized scrapping processThe tool scrapping process usually lacks strict audit and record. Tool scrapping is arbitrary, without detailed records of the reasons for scrapping, length of use and other information, which is not conducive to summarizing and analyzing the use of tools, and it is also difficult to obtain effective information for improving production from tool scrapping management.

(iv) Poor communication of information

1. Intersectoral collaboration issuesInformation transfer between different departments in the workshop, such as production department, purchasing department and tool management department is not timely and accurate. Changes in demand for tools from the production department cannot be communicated to the purchasing department in a timely manner, resulting in procurement lag; information on tool inventory and utilization cannot be shared effectively by the tool management department, affecting the collaborative work of all departments.
2. Disconnect between equipment and management systemsCNC equipment itself has a certain amount of tool information, but this information is not effectively integrated with the workshop tool management system. Equipment operators in the machining process to obtain the tool use, can not be real-time feedback to the management system, so that the management system can not be based on the actual machining data for tool management decisions.

Principle and Composition of Intelligent CNC Tool Management System

(i) System principles

Intelligent CNC tool management system is based on the Internet of Things, sensors, big data analysis and other technologies to realize the comprehensive management of CNC tools. By installing various types of sensors on the tool, toolholder or machine tool, real-time collection of tool usage data, such as cutting time, cutting force, vibration frequency and so on. These data are transmitted to the central server through a wireless network, and big data analysis algorithms are utilized to evaluate and predict the wear state and life of the tool. At the same time, the system is integrated with the workshop's production management system, procurement system, etc., to realize information sharing and collaborative work, and automatically generates purchase plans, tool replacement reminders and other commands according to the actual use of the tool and the inventory status, so as to realize the intelligence and automation of tool management.

(ii) System components

1. hardware part
   • Tool Sensors: These include optical sensors for monitoring tool wear, piezoelectric sensors for detecting cutting forces, and acceleration sensors for measuring vibration. These sensors can obtain the key parameters of the tool in the machining process in real time and provide data support for tool condition assessment. For example, optical sensors can directly reflect the wear state of the tool by detecting the wear degree of the tool edge.
   • Tool shank identification deviceRFID (Radio Frequency Identification) technology is used to install RFID tags on the tool shanks, which store the basic information of the tool, such as tool model, specification, material, etc. The machine is equipped with RFID readers and writers. The machine tool is equipped with an RFID reader, which automatically reads the tool information and transmits it to the management system when the tool is installed in the machine tool, realizing the rapid identification and positioning of the tool.
   • Data Acquisition Terminal: It is responsible for collecting signals from various sensors, converting them into digital signals, and transmitting them to the central server through wired or wireless network. The data acquisition terminal is equipped with data pre-processing function, which can preliminarily screen and organize the collected data to reduce the transmission of invalid data.
2. software component
   • Tool management softwareThis is the core software of the system, with tool inventory management, tool life management, tool scrap management, report generation and other functional modules. In terms of inventory management, it updates the information of tools in and out of the warehouse in real time and provides inventory warning function; in terms of tool life management, it predicts tool life and generates tool replacement plan based on sensor data and data analysis model; in terms of tool scrapping management, it automatically labels the tools that reach the scrapping conditions according to the scrapping standards and records the scrapping related information.
   • Data analysis software: Apply big data analysis technology to deeply mine and analyze the collected tool usage data. Through the establishment of tool wear models, life prediction models, etc., to find out the relationship between tool wear and machining parameters, tool materials, machining materials, to provide a scientific basis for tool management. For example, by analyzing the use data of a large number of tools, it determines the reasonable service life range of tools under different machining processes and improves the accuracy of tool life prediction.
   • System Integration Interface: It is used to realize the integration of the intelligent CNC tool management system with other information systems in the workshop (e.g. production management system, procurement system, equipment management system). Through standardized data interfaces and communication protocols, it realizes data sharing and interaction between various systems to ensure the synergy and consistency between tool management and overall production and operation of the workshop. For example, real-time synchronization of tool inventory information to the purchasing system enables the purchasing department to understand the dynamics of tool inventory in a timely manner and reasonably arrange the purchasing plan.

Intelligent CNC tool management system to improve the efficiency of workshop management

(i) Optimization of inventory management

1. Real-time accurate inventory dataThe system automatically updates inventory data by collecting real-time information on the entry and exit of tools, ensuring that the inventory quantity is exactly the same as the actual situation. Staff can check the inventory status of knives at any time through computer terminals or mobile devices, providing an accurate basis for purchasing decisions. At the same time, the inventory warning function can notify the relevant personnel to replenish or deal with the backlog of tools according to the preset upper and lower limits of the inventory, so as to avoid the occurrence of inventory backlog or out-of-stock phenomenon.
2. Intelligent Purchasing ProgramBased on the actual use of tools and inventory data, the system utilizes a data analysis model to automatically generate a scientific and reasonable procurement plan. The procurement plan not only considers the current inventory level, but also combines factors such as production task scheduling and tool life prediction to ensure that the purchased tools can meet the production demand without causing inventory backlog. For example, based on recent production orders and tool consumption rates, the system predicts the demand for various types of tools in the coming period, providing the purchasing department with a detailed list of purchases and suggestions for purchasing time.

(ii) Precise tool life management

1. Real-time status monitoringTool sensor real-time acquisition of key parameters of the tool in the machining process, the management system through the analysis of these parameters, real-time grasp of the tool wear state. Once the tool wear is close to the set threshold, the system immediately issues an early warning to notify the operator to replace the tool in a timely manner, so as to avoid affecting the machining quality due to excessive tool wear. For example, when the cutting force suddenly increases or the vibration frequency is abnormal, the system is able to determine that the tool may be broken or excessively worn, and promptly remind the operator to stop the machine for inspection.
2. Accurate Life PredictionWith the help of big data analysis technology, the tool life prediction model established by the system is able to predict tool life accurately by taking into account a variety of factors such as tool material, machining process and machining material. Compared with the traditional empirical prediction method, the prediction accuracy is greatly improved. This allows companies to prepare for tool replacement in advance, rationalize 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 life of the tool before completing the batch of parts machining based on the tool's usage history and current machining parameters, so that the spare tool can be prepared in advance.

(iii) Improving productivity

1. Reduced downtimeDowntime due to excessive tool wear or stock shortages is avoided through accurate tool life management and inventory management. The system sends out tool change reminders in advance, enabling operators to change tools in a timely manner during production breaks to ensure production continuity. According to statistics, after adopting the intelligent CNC tool management system, the downtime caused by tooling problems in the workshop can be reduced by 30% - 50%, which effectively improves equipment utilization and production efficiency.
2. Optimization of machining parametersThe results of the analysis of tool usage data by the data analysis software can also provide a reference for optimizing machining parameters. By analyzing the tool wear and machining efficiency under different machining parameters, companies can adjust cutting speed, feed and other parameters to improve machining efficiency and extend tool life under the premise of ensuring machining quality. For example, after data analysis found that in a particular machining task, appropriate reduction of cutting speed can significantly extend tool life, while less impact on machining efficiency, enterprises can adjust the machining process accordingly.

(iv) Enhancing communication and synergies

1. Interdepartmental information sharingIntelligent CNC tool management system integrates with the information system of each department in the workshop, realizing the real-time sharing of tool-related information among the production department, procurement department and tool management department. The production department can know the tool inventory and tool replacement plan in real time, so as to reasonably arrange the production tasks; the procurement department purchases the tools in time according to the procurement plan generated by the system, so as to ensure the smooth progress of the production; the tool management department can comprehensively grasp the use of the tool, and effectively deploy and maintain the tool. Collaboration between departments is smoother, reducing communication costs and work errors caused by poor information.
2. Integration of equipment and management systemsThe system closely integrates the CNC equipment with the tool management system, realizing real-time data interaction between the equipment and the management system. Tool problems found by equipment operators during machining can be fed back to the management system in a timely manner, and the management system adjusts the tool management strategy according to the feedback information. At the same time, the management system can also send tool replacement instructions and other information directly to the equipment side to guide the operator to operate, improving work efficiency and accuracy.

Intelligent CNC tool management system for tool scrap management

(i) Clarification of end-of-life criteria

1. Quantitative end-of-life indicatorsThe intelligent CNC tool management system establishes scientific and quantitative tool scrap criteria by analyzing tool wear data, machining quality data, and other data. In addition to traditional indicators such as tool wear and breakage, machining accuracy, surface roughness and other indicators related to machining quality are also taken into account. For example, when the tool wear reaches a certain percentage of the initial size of the tool and the surface roughness of the processed product exceeds the specified range, the system determines that the tool reaches the end-of-life conditions.
2. Dynamic adjustment criteriaAccording to different machining tasks, tool materials and machining materials, the system can dynamically adjust the tool scrap standard. For high-precision machining tasks, the end-of-life standard is more strict; for some common machining tasks, the end-of-life standard is relatively loose. At the same time, with the continuous accumulation and analysis of tool use data, the system can optimize and adjust the end-of-life standard to make it more in line with the actual production situation.

(ii) Standardizing the end-of-life process

1. Automatically triggered obsolescenceWhen a tool reaches the set end-of-life criteria, the system automatically triggers the tool scrap process. In the tool management software, the tool is marked as end-of-life, and detailed information such as the reason for the tool's end-of-life, the length of time it has been used, and the number of parts machined are recorded. This information provides an important basis for enterprises to analyze tool usage and improve tool management.
2. Strict Audit ProcessThe tool scrapping process has set up a strict audit link. Firstly, the system automatically generates the scrapping application and submits it to the person in charge of the tool management department for review. The person in charge will make judgment according to the tool usage data and the reason for scrapping provided by the system, combined with the actual production situation, and confirm whether to approve the scrapping or not. After passing the audit, the end-of-life tool enters the end-of-life process to ensure that each end-of-life tool is strictly audited to avoid misuse or unreasonable end-of-life.

(iii) Analysis and improvement of end-of-life tools

1. Statistics and analysis of dataThe system provides statistics and analysis of end-of-life tool data, such as the distribution of end-of-life causes, end-of-life rates of different tool models, and average service life. Through the analysis of these data, companies can find common problems in the use of tools, and identify the key factors affecting tool life and scrap. For example, through the analysis found that a certain type of tool in a particular machining process frequently due to breakage and scrap, the enterprise can further study the machining process whether the tool caused too much impact, so as to find improvement measures.
2. Guidance on tool selection and process improvementBased on the results of the end-of-life tool analysis, companies can optimize their tool selection strategy. For tool models with frequent problems, consider replacing the tool with a more suitable brand or model. At the same time, for the tool premature end-of-life machining process problems, companies can carry out process improvement, adjust the processing parameters or optimize the machining path, improve tool life, reduce tool costs. For example, through the analysis of end-of-life tools, the enterprise found that a processing process in the tool wear is too fast, after process improvement, the use of more reasonable cutting parameters, so that the tool life extended by 30%, while reducing processing costs.

Intelligent CNC tool management system implementation case study

(i) Case enterprise background

[Company Name] is a large-scale machinery manufacturing enterprise, mainly producing various types of precision parts. The enterprise has several CNC machining production lines, using a wide variety of tools, and the monthly tool procurement cost is high. Before the introduction of the intelligent CNC tool management system, there were many problems in tool management in the workshop, such as serious inventory backlog, confusing tool life management, unreasonable tool scrapping, etc., which led to low production efficiency and high production costs.

(ii) Implementation process

1. Requirements Research and PlanningThe enterprise set up a project team composed of workshop managers, technicians, tool managers, etc. to conduct a comprehensive research on the current situation of tool management in the workshop and analyze the existing problems and needs. According to the results of the research, the implementation plan of the intelligent CNC tool management system was formulated, and the project objectives, implementation steps and expected results were clarified.
2. System Selection and ProcurementThe project team researched and evaluated a number of intelligent CNC tool management system suppliers in the market, and selected a professional supplier after considering system functions, technical strength, case experience, after-sales service and other factors. After signing the contract with the supplier, both parties set up a joint project team to promote the project implementation.
3. Hardware installation and commissioningAccording to the system design program, install sensors, RFID readers and other hardware devices on CNC equipment, tools and shanks. The installation process is carried out in strict accordance with the technical specifications to ensure that the equipment is firmly installed and stable operation. After the installation is completed, the hardware equipment is debugged to test the accuracy of the sensor data acquisition, the recognition rate of the RFID reader, etc., to ensure that the hardware equipment works properly.
4. Software deployment and customized developmentDeploy tool management software, data analysis software and other system software on the enterprise server, and customize the development of the software according to the actual business processes and management needs of the enterprise. For example, set up in the tool management software in line with the enterprise production characteristics of the inventory warning rules, tool scrapping standards and other functional modules. At the same time, the development of system integration interfaces to achieve integration with the enterprise's existing production management system, procurement system.
5. Personnel training and system go-liveThe supplier provides systematic training for the relevant personnel of the enterprise, including training on the operation of hardware equipment, the use of software systems, and data analysis methods. The training adopts a combination of theoretical explanation, on-site demonstration and practical operation to ensure that employees can master the use of the system. After the training, the intelligent CNC tool management system was officially launched. During the initial period, the project team arranged specialists to provide on-site guidance and handle problems to ensure a smooth transition of the system.

(iii) Effectiveness of implementation

1. Inventory management optimizationInventory accuracy has increased from 80% to over 98%, and inventory backlog has been reduced by 40%. Through intelligent purchasing plan, tool purchasing is more reasonable, effectively avoiding the occurrence of inventory shortages, and ensuring the smooth progress of production.
2. Tool Life Management EnhancementTool life prediction accuracy improved by 301 TP3T, and machining quality issues due to excessive tool wear were reduced by 501 TP3T. tool change reminders sent in advance by the system enabled operators to change tools in a timely manner, reducing downtime due to tool failure, and increasing equipment utilization by 251 TP3T.
3. Tool scrap management specificationThe scientific and quantitative tool scrapping standards have been clarified, and the rationality of tool scrapping has been significantly improved. Through the data analysis of the scrapped tools, the enterprise optimized the tool selection and machining process, and the tool cost was reduced by 20%.
4. Increased overall productivityWorkshop productivity has increased by more than 30%, and product quality has been steadily improved. The information communication between departments is smoother, the efficiency of cooperative work is obviously improved, and the comprehensive competitiveness of the enterprise is enhanced.

VII. Challenges and Strategies for the Application of Intelligent CNC Tool Management Systems

(i) Technical aspects

1. Sensor reliability issuesTool sensors operate in complex machining environments and can be affected by cutting fluids, high temperatures, vibration and other factors that can lead to inaccurate sensor data or sensor failure. This can affect the accurate assessment of tool condition and life prediction.
   • response strategy: Select sensor products with reliable quality and high protection level, and make reasonable selection according to the processing environment. Regular maintenance and calibration of sensors to ensure the accuracy of sensor data. At the same time, the key sensors are equipped with backup sensors, when the main sensor fails, it can be switched in time to ensure the normal operation of the system.
2. Data Analytics Model OptimizationTool wear and life are affected by a variety of factors, making it difficult to establish an accurate data analysis model. With the change of machining process, tool material and other factors, the original data analysis model may need to be continuously optimized to improve the accuracy of tool life prediction and condition assessment.
   • response strategy: Strengthen cooperation with scientific research institutions or professional data analysis teams to continuously research and improve data analysis models. Use more machining data to train and validate the model to improve the accuracy and adaptability of the model. Evaluate and optimize the data analysis model on a regular basis, and adjust the model parameters according to the actual production situation to ensure that the model can accurately reflect the actual use of the tool.

(ii) Personnel

Change in personnel mindsetThe introduction of an intelligent CNC tool management system requires a change in the traditional concepts of tool management and work style of employees, and some employees may be resistant to it.

Employee Skills EnhancementIntelligent CNC tool management systems involve new technologies such as the Internet of Things and data analysis, which require high skills from employees. Some employees may lack the knowledge and skills to adapt to the operation and management of the new system.

response strategy: Develop a comprehensive employee training program, including training in the basics of new technologies, system operation training, data analysis training, etc. Training methods can be in the form of internal training, lectures by external experts, online learning and other forms to meet the learning needs of different employees. At the same time, it encourages employees to learn independently and provides learning resources and incentives to help employees improve their skills.