Machine Learning-based Online Stability Lobe Diagram Estimation and Chatter Suppression Control in Milling Process

TL;DR

This paper introduces an adaptive machine learning-based control system for milling that estimates the stability lobe diagram and surface roughness in real time to suppress chatter and enhance surface quality.

Contribution

It presents a novel integrated framework combining machine learning and stability analysis for real-time chatter suppression in milling.

Findings

Superior chatter suppression performance demonstrated in simulations.

Real-time estimation of stability lobe diagram achieved.

Improved surface finish compared to traditional methods.

Abstract

Chatter is a self-excited vibration in milling that degrades surface quality and accelerates tool wear. This paper presents an adaptive process controller that suppresses chatter by leveraging machine learning-based online estimation of the Stability Lobe Diagram (SLD) and surface roughness in the process. Stability analysis is conducted using the semi-discretization method for milling dynamics modeled by delay differential equations. An integrated machine learning framework estimates the SLD from sensor data and predicts surface roughness for chatter detection in real time. These estimates are integrated into an optimal controller that adaptively adjusts spindle speed to maintain process stability and improve surface finish. Simulations and experiments are performed to demonstrate the superior performance compared to the existing approaches.