Abstract
The present disclosure provides a tool edge parameter optimization method based on workpiece-tool performance parameters. The method includes: obtaining cutting and workpiece characteristic parameters under different tool edge parameter combinations; establishing a tool characteristic prediction model, inputting tool edge parameters, and outputting cutting characteristic parameters; under constraint conditions, combining the tool edge parameters randomly, and inputting into the prediction model to obtain the cutting characteristic parameters; performing data processing and correlation analysis of workpiece combinations, and constructing a functional relationship among cutting characteristics, tool life coefficient and workpiece surface quality coefficient, using the genetic algorithm to iteratively optimize the individual of the initial population and extract the best tool edge parameters. The present disclosure significantly enhances the intelligence of tool design, enabling not only real-time optimization based on actual machining conditions but also effective adaptation to diverse scenarios, thereby ensuring optimal tool performance in practical applications.