International Journal of Mechanical Engineering

The selection of machining process parameters has a significant impact on processing efficiency and quality. Traditional empirical methods struggle to meet the complex and variable processing requirements. This paper proposes an intelligent recommendation system based on machine learning, which constructs a model to realize automatic optimization and recommendation of parameters by collecting historical processing data and process parameters. The system utilizes various machine learning algorithms to analyze data features, improving the accuracy and adaptability of parameter recommendations. Experimental results show that the system can effectively reduce manual parameter tuning time, enhance processing efficiency and quality, and provide technical support for intelligent manufacturing in the machining industry.

Machining; Machine learning; Process parameters; Intelligent recommendation; Data-driven