Research on Tool Path Planning Algorithm in Core Machining

Core walking machining is a commonly used processing method for manufacturing electronic devices and semiconductor devices. Due to the need for high-precision and high-efficiency cutting processes in chip manufacturing, tool path planning algorithms play a crucial role in the core machining process. This article will provide a detailed introduction to the research progress of tool path planning algorithms in core machining.
1. Problem definition
The main goal of the tool path planning algorithm in core machining is to find the optimal path to minimize machining time, reduce cutting forces, and ensure the machining quality of the workpiece.
2. Common path planning algorithms
2.1. Shortest path algorithm
The shortest path algorithm is a commonly used path planning algorithm, whose basic idea is to plan the motion trajectory of the tool by determining the shortest path between the starting and ending points. Typical shortest path algorithms include Dijkstra’s algorithm and Freud’s algorithm. These algorithms can calculate the shortest path in the graphical model and apply it to tool path planning in core machining.
2.2. genetic algorithm
Genetic algorithm is an optimization algorithm that simulates natural selection and genetic mechanisms. In the tool path planning of core machining, the tool path can be regarded as a chromosome, and the chromosome can be optimized through genetic algorithm to find the optimal tool path. Genetic algorithm has global search capability and adaptive optimization capability, and has important application value in path planning.
3. Research progress
Recent research has shown that hybrid algorithms and artificial intelligence algorithms have made significant breakthroughs in tool path planning algorithms for core machining. Hybrid algorithms combine multiple algorithms to fully utilize their respective advantages and improve the effectiveness of path planning. Artificial intelligence algorithms such as neural networks and fuzzy logic algorithms can continuously improve the accuracy and efficiency of path planning through learning and optimization.
4. Application and Prospect
The research results of tool path planning algorithms in core machining have been widely applied in the electronic manufacturing industry. Optimized tool path planning can significantly improve machining efficiency and product quality, and reduce production costs. In the future, with the continuous development of computer technology and artificial intelligence, the tool path planning algorithm in core machining will be further improved, bringing more innovation and development to the electronic manufacturing industry.
The research on tool path planning algorithm in core machining is an important and challenging topic. By continuously innovating and optimizing algorithms, more efficient and accurate path planning can be achieved, promoting the development of core machining technology.