Optimisation of Numerical Control Tool Cutting Parameters Based on Thermodynamic Response and Machine Learning Algorithms

Abstract

With the burgeoning demand for high-precision and high-efficiency production, the optimisation of tool cutting parameters has become increasingly paramount. For the first time, a model rooted in thermodynamic response has been proposed, offering a scientific basis for the optimisation of numerical control tool cutting parameters. By conducting an in-depth analysis of the thermal energy produced during the cutting process, this model, through state-of-the-art sensor technology, captures real-time temperature variations in the cutting zone, thereby elucidating thermal flow characteristics under varying cutting parameters. Employing machine learning algorithms, optimal cutting speeds, cutting depths, and feed rates for specific workpiece materials can be predicted and recommended. Preliminary experimental validations indicate that, when compared to conventional optimisation methods, the thermodynamic response-based model significantly enhances cutting efficiency, reduces workpiece thermal deformation, and extends the tool's lifespan. This investigation paves the way for a novel perspective and methodology for the intelligent optimisation of future numerical control tool cutting parameters.