Application of artificial intelligence in CNC machining
Changes in the global economic environment have led to a significant increase in corporate awareness of the application of digital technologies, and more and more companies are focusing on AI technologies and their applications. Deloitte’s latest research on more than 2,000 enterprises worldwide shows that most of them believe that AI in manufacturing is the key for enterprises to adapt to future market development. CNC machining is the core part of the modern manufacturing industry, the application of artificial intelligence in CNC machining brings many advantages to CNC machining, which can greatly improve the precision, efficiency, and automation degree of CNC machining, and has a far-reaching impact on modern manufacturing industry.
Artificial Intelligence (Artificial Intelligence, or AI for short) has been applied to various fields, such as face recognition technology, artificial intelligence navigation systems, artificial intelligence voice assistants, and so on.
It aims to research and develop theories, methods, technologies, and application systems that can simulate, extend, and expand human intelligence, and its main goal is to produce an intelligent machine that can respond in a way similar to human intelligence, which can perform a variety of complex tasks.
CNC machining is a manufacturing method based on digital control technology that uses a computer program to control the machining process of a machine tool. Compared to traditional machining methods, CNC machining offers higher accuracy, more flexible machining methods, and a wider range of machining materials. CNC machining, that is, the workpiece is fixed on the machine tool, cutting through the tool, processing molding.
The trajectory of the tool is controlled by a computer program, which requires precise positioning and path planning, and changes in the computer program can be adjusted to the machining process, thus realizing the processing of complex shapes and structures.
Advantages of artificial intelligence in CNC machining
(1) Accuracy improvement: artificial intelligence can control and optimize the CNC machining process more accurately through deep learning and big data analysis. For example, AI can accurately predict tool wear through real-time monitoring of vibration, temperature, sound, and other small changes in the cutting process so that real-time adjustments can be made during the machining process to ensure machining accuracy.
(2) Efficiency optimization: AI can quickly analyze a large amount of machining data to find the optimal combination of machining parameters, such as cutting speed, feed rate, and depth. This optimization not only improves machining efficiency but also reduces tool wear, extends tool life, and reduces production costs.
(3) Increased automation: AI can fully automate CNC machining tasks, and automatic programming, from tool path planning, and cutting process control, to quality inspection. And no manual intervention is required, which can save a lot of time and energy and make the production line more efficient. This automation can reduce the requirements for workers’ skills, reduce the chances of human error, and improve production stability.
(4) Responding to complex tasks: For some complex and delicate parts processing, artificial intelligence can ensure the accuracy and consistency of the processing process through high-precision modeling and simulation.
(5) Failure warning: AI can monitor the operating status of the machine tool in real time and predict possible failures in advance by analyzing various sensor data. In this way, maintenance personnel can intervene before the failure occurs, reducing downtime and improving equipment utilization.
(6) Strong flexibility: artificial intelligence has a strong learning ability and adaptability, and can quickly adapt to different processing environments and conditions. When processing needs change, AI can quickly adapt to the new production environment by re-learning and adjusting.
(7) Data-driven decision-making: AI can transform a large amount of production data into valuable information to provide managers with a basis for decision-making. For example, through the analysis of historical processing data, the key factors affecting processing efficiency and accuracy can be identified to further optimize the production process.
Specific applications of artificial intelligence in CNC machining
1. Intelligent identification and localization
In CNC machining, an accurate positioning method can ensure the accuracy and stability of machining, and through artificial intelligence technology, intelligent identification and positioning of the workpiece can be realized.
First collect a large number of pictures and samples containing the target workpiece, or even images containing different scenes.
For example, images at different angles or under different lighting conditions; then the collected images are processed to extract features, such as edges and shapes;
The extracted features and corresponding labels are then used to train the AI model so that the model can recognize the type of workpiece based on the input information;
Afterward, the optimized model can be deployed into the actual production environment by adjusting the model parameters so that the machine can automatically identify the features of the workpiece, determine its position and attitude, and even quickly and accurately identify the information of the processed material, including the type of the material, its size, and the material, etc. This information is crucial for the subsequent processing.
This information is crucial to the subsequent processing process, helping to ensure the accuracy and efficiency of processing, so as to realize the rapid and accurate positioning of the workpiece.
2. Process Planning and Execution
In CNC machining, process planning is a key link in CNC machining, and by using artificial intelligence technology, the historical process data can be learned through machine learning algorithms to automatically generate the optimal process plan. At the same time, through real-time monitoring and adjustment of the machining process, it can ensure the stability and consistency of the machining process, and improve product quality and productivity.
It involves a number of aspects such as machining task allocation, tool selection, parameter optimization, and so on. First of all, artificial intelligence technology can learn and optimize process parameters through the analysis of historical machining data, so as to automatically generate the optimal process plan.
For example, through the analysis of cutting parameters, tool wear, machining accuracy, and other data, the appropriate cutting parameters and tools can be automatically selected to improve machining efficiency and reduce costs.
Secondly, artificial intelligence technology can also realize the intelligent allocation of machining tasks. By prioritizing and task scheduling machining tasks, the optimal allocation of resources can be achieved to further improve productivity.
Using artificial intelligence technology, a more automated and precise machining process can be realized, and the stability and consistency of machining can be ensured by real-time monitoring of the working status of the machine tool and timely detection and adjustment of abnormalities.
For example, through the real-time monitoring of machine vibration and cutting force, abnormal cutting parameters can be found and adjusted in time to avoid overshooting or damage to the workpiece and realize the intelligent adjustment of the machining process. At the same time, through real-time analysis and optimization of the machining process, automatic compensation and adjustment can be realized to further improve machining accuracy and stability.
3. Intelligent selection of tools
In CNC machining, tool selection not only directly affects the quality and efficiency of machining, but also is closely related to tool life and production costs. Different workpieces and materials have different machining requirements and require the selection of different tools. Through artificial intelligence technology, according to the machining requirements, workpiece materials, and other factors, the type, specification, and state of the tool can be intelligently identified and selected to achieve the most optimized machining process.
AI tool intelligent selection technology can automatically recommend suitable tool types and parameters according to the machining requirements and tool library data, and can also realize automated tool changing, including automatic identification, grasping, and replacement of the tool, which helps to reduce manual intervention and errors and improve the efficiency and precision of tool changing.
Sensors can use a variety of technologies, such as optical, electromagnetic, acoustic wave, etc., in order to realize the monitoring and analysis of various parameters of the tool. Through the intelligent identification technology of the tool, the automatic detection, classification, and management of the tool can be realized, which can provide basic data for the subsequent automatic selection.
Automatic tool selection technology refers to the automatic selection of the most suitable tool for machining through intelligent algorithms and decision-making models according to the requirements of the machining task and the tool information in the tool library. In addition, AI can predict the life of the tool according to the tool’s use history, wear, and other information, and replace the tool in time to ensure the smooth progress of the machining process.
4. Intelligent Programming
Through artificial intelligence technology, machining programs can be automatically identified and generated to reduce the time and error of manual programming; cutting parameters, machining paths, etc. can be automatically optimized according to the processing requirements to improve processing efficiency. Using machine learning algorithms, the language and rules of CNC programming are trained and a model is generated so that it can automatically generate executable CNC programs.
This model can generate CNC programs by talking to a human and generating the appropriate instructions as required. Simply entering verbal commands instead of manually writing complex CNC programs makes the programming process easier and more efficient, while avoiding the errors associated with manually writing CNC programs, improving machining accuracy and quality, and reducing waste.
5. Intelligent monitoring of the machining process
Previously, the processing process mainly relied on manual inspection, which made it difficult to achieve real-time monitoring. Through artificial intelligence technology, real-time monitoring of the machining process can be carried out to facilitate the timely detection of abnormalities and take appropriate measures.
AI can monitor various parameters in the machining process in real-time by installing sensors and monitoring equipment, such as cutting force, cutting temperature, tool wear, etc., and mining and analyzing the machining data, real-time monitoring of these parameters helps to detect abnormalities in a timely manner, and AI can analyze various data in the machining process to determine whether the machining status is normal.
If abnormalities occur, AI can automatically adjust the processing parameters, or issue an alarm to notify the operator to deal, to avoid equipment damage and product quality problems, but also constantly optimize the processing parameters and machining process to improve processing efficiency and precision.
6. Error Detection
Through the use of artificial intelligence technology, accurate error detection can be carried out on the finished workpiece, including size error, shape error, surface roughness, and so on. This detection method mainly relies on machine learning and deep learning technology and requires the use of sensors and other data acquisition tools to obtain data in real time during the machining process, including the size of the workpiece, the wear of the tool, and the machining parameters. Preprocessing and feature extraction are performed on this raw data to enable it to be used for subsequent error detection.
For example, error-related features can be extracted from the workpiece size data, and error detection models can be built based on these features. These models can be trained and optimized to improve the accuracy and stability of the error detection.
Once the error is detected, the system can take immediate steps to make adjustments, such as adjusting machining parameters, changing tools, or performing error compensation. At the same time, the feedback data can also be used for further optimization and upgrading of the model.
7. Intelligent fault diagnosis and prevention
In CNC machining, equipment failure is inevitable, but timely fault diagnosis and prevention can greatly reduce the impact of faults on production, artificial intelligence technology provides a new solution for fault diagnosis and prevention of CNC machining equipment, the main content includes automatic fault detection, real-time monitoring and surveillance, predictive maintenance, abnormal early warning system, intelligent data analysis, fault pattern recognition, fault root cause analysis, preventive maintenance strategies, optimization of equipment parameters, and remote diagnosis and maintenance.
For example, Al intelligent fault diagnosis and prevention technology can monitor the equipment in real-time, discover potential faults in time, and carry out early warning and automatic detection. It can determine whether the state of the equipment is normal or not by analyzing a variety of data during the operation of the equipment, such as temperature, vibration, sound, and so on.
If there is an abnormal situation, AI can automatically diagnose the fault and give the corresponding processing recommendations; in terms of predictive maintenance, AI can analyze and learn from the fault history of the equipment, predict the life of the equipment and the maintenance cycle, and provide data support for the preventive maintenance of the equipment, to effectively reduce the equipment faults and improve the production efficiency.
Challenges and Prospects
Although the application of artificial intelligence in CNC machining has significant advantages, it also faces many challenges.
(1) Data security and privacy protection issues. In the intelligent process, a large amount of data needs to be processed, and how to ensure data security and privacy has become an important issue.
(2) Establishment of an accurate mathematical model. Since the machining process of CNC machines involves complex physical phenomena and variable working conditions, how establishing an accurate mathematical model is a great challenge.
(3) Accurate extraction of useful information. Due to the existence of a large amount of noise and abnormal data in the machining process, how to accurately extract useful information for fault prediction is also a challenge.
(4) Demand for technology updates and talent training. The rapid development of artificial intelligence technology requires regular upgrading of equipment, as well as technical training for operators to ensure the normal operation and effectiveness of equipment use. Colleges and training institutions should strengthen the construction of relevant specialties and curricula to cultivate CNC machining talents with artificial intelligence knowledge and skills to meet the needs of enterprises.
(5) Ability to respond to abnormal situations. Although AI technology can handle a large number of routine tasks, manual intervention is still required for the handling of abnormal situations.
Enterprises should strengthen their investment in technology research and development and innovation to improve the adaptability and stability of AI technology in CNC machining and reduce the cost of use and maintenance.
These challenges arise from a variety of factors, such as the limitations of hardware equipment. Advanced AI algorithms cannot run directly on resource-constrained equipment such as CNC machines due to limitations in computing resources and storage space, and imperfections in software are also a major obstacle.
Existing CNC systems have limited support for AI algorithms and lack unified interfaces and standards, which makes integration and deployment more difficult.
Despite the many challenges, the development of AI in CNC machining is still promising.
(1) It helps to optimize the existing machining process, and the combination with new machining technologies, such as additive manufacturing and micro-nano manufacturing, opens up new application areas. In addition, AI technology also helps to realize intelligent manufacturing, making the production process more automated, flexible, and personalized.
(2) With More intelligent adaptive control, through deep learning and reinforcement learning technology, the CNC machining system will be able to better adapt to a variety of complex and dynamic processing environments.
(3) With the continuous upgrading and optimization of hardware equipment, AI algorithms will run more efficiently on CNC machines.
(4) With the popularization and application of 5G and other new-generation communication technologies, the stability and transmission speed of the network will also be greatly improved, which provides a possibility for the application of AI in remote monitoring and maintenance.
(5) A new mode of human-machine collaboration, with the help of augmented reality and virtual reality technology, to realize close collaboration between humans and machines, and improve productivity and safety.
(6) Intelligent decision support, in production planning, quality control, and other aspects of the provision of more intelligent decision support to help enterprises improve product quality, productivity, and competitiveness.
(7) The government and all sectors of society are also increasing their support for the application of artificial intelligence technology in the field of CNC machining, which in turn continues to promote industrial upgrading and high-quality development.
Conclusion
In short, the application of artificial intelligence in CNC machining has achieved remarkable results, can effectively improve processing efficiency, precision, and equipment reliability, and promote the transformation and upgrading of the manufacturing industry, but it is also a double-edged sword while enjoying the convenience it brings, it is also necessary to pay attention to and solve the problems and challenges that may be brought.