，用智能化功能优化铣削加工
Introduction
Milling is a machining process identified by the use of rotary cutters to finish a surface. It is an important element of many manufacturing processes, as it can provide specific features, precise shapes, or tight tolerances. However, due to the complex geometry of the milling process, a detailed optimization of tools and parameters is essential for achieving the best performance and quality of milled surfaces. Hence, recent attempts have involved the use of artificial intelligence (AI) and other techniques to optimize the milling process. These technologies are expected to provide precise and consistent results, improved production efficiency, and reduced energy consumption. This paper provides an overview of the current research into the use of AI-assisted optimization of the milling process, and the potential of such technologies to improve the quality and efficiency of machining operations.
Optimization of Milling Processes with AI
AI-assisted optimization of milling processes typically involves the use of supervised, unsupervised, or reinforcement learning algorithms. These algorithms can be used to analyze historical data and identify patterns of milling process parameters that could lead to increased productivity and improved quality. In this way, AI-assisted optimization can be used to create optimized cutting strategies and tool geometries that take advantage of the advantages of different cutting parameters.
In particular, AI-enabled optimization enables faster computation of a large number of variables during the milling process, allowing the user to optimize the tool path or cutting parameters in real-time. With this approach, the machine can adjust the parameters during the cutting process to optimize the result according to user-defined objectives, such as minimizing tool wear or maximizing surface quality. AI-enabled optimization can also be used to identify the best cutting parameters based on milling speed, feedrate, and tool wear.
AI-assistance in the optimization of milling processes has been applied in a range of industries, including aerospace, automotive, and medical device manufacturing. For example, a team from the Institute of Aircraft Design at the Technical University of Munich (TUM) used AI-assisted optimization of the milling process to develop optimal tool paths for the fabrication of aircraft components made from titanium alloy. The AI system was used to analyze the recorded machining data and design tool paths with varying cutting parameters. The AI-optimized tool path increased the productivity and decreased the energy costs of the milling process.
In the automotive industry, AI-assisted optimization of the milling process has been used to improve the production of aluminum components. The AI system was used to identify cutting parameters, such as feed and spindle speed, that would maximize the quality of the final machined surface. The optimized milling process was found to reduce cycle time and tool wear, while providing superior surface quality.
Advantages of AI-assisted Optimization of Milling Processes
The AI-assisted optimization of milling processes offers several advantages over manual optimization. First, it reduces the time required to achieve optimal results. AI-assisted optimization is typically faster than manual optimization, as the system can analyze a much larger number of variables in real-time. This reduces the time needed to identify the best parameters for a particular machining operation.
In addition, AI-assisted optimization can improve product quality, as the system can identify optimal cutting parameters for a wide range of materials and tool geometries. This can lead to better surface finishes and improved accuracy of machined surfaces, decreasing production costs and increasing productivity. Furthermore, AI-assisted optimization can reduce tool wear, as the system can identify tool strategies that reduce the need for frequent tool replacements.
Conclusion
In conclusion, AI-assisted optimization of the milling process enables faster computation of a large number of variables during the milling process, allowing users to optimize the tool path or cutting parameters in real-time. AI-enabled optimization has been applied in a range of industries, offering advantages over manual optimization, such as faster computation of variables, improved product quality, improved surface finish, and reduced tool wear. On the whole, AI-assisted optimization of milling processes is an important step forward for improving the efficiency and quality of machining operations.
