A Cutting Mechanics-based Machine Learning Modeling Method to Discover Governing Equations of Machining Dynamics

TL;DR

This paper introduces a physics-informed machine learning approach that automatically discovers the governing equations of machining dynamics from data, integrating cutting mechanics principles with data-driven modeling.

Contribution

It presents a novel CMML method that combines cutting mechanics knowledge with machine learning to identify unknown differential equations governing machining dynamics.

Findings

Successfully discovers exact milling dynamics models from noisy data

Validates the approach with time domain simulation of milling processes

Demonstrates potential for practical machining modeling advancements

Abstract

This paper proposes a cutting mechanics-based machine learning (CMML) modeling method to discover governing equations of machining dynamics. The main idea of CMML design is to integrate existing physics in cutting mechanics and unknown physics in data to achieve automated model discovery, with the potential to advance machining modeling. Based on existing physics in cutting mechanics, CMML first establishes a general modeling structure governing machining dynamics, that is represented by a set of unknown differential algebraic equations. CMML can therefore achieve data-driven discovery of these unknown equations through effective cutting mechanics-based nonlinear learning function space design and discrete optimization-based learning algorithm. Experimentally verified time domain simulation of milling is used to validate the proposed modeling method. Numerical results show CMML can discover the exact milling dynamics models with process damping and edge force from noisy data. This indicates that CMML has the potential to be used for advancing machining modeling in practice with the development of effective metrology systems.