On-Line Condition Monitoring and Chatter Control in Thin-wall Machining Systems Using Machine Learning Algorithms
This research focuses on addressing machining vibration, or chatter, a critical concern in thin-wall machining systems. Chatter adversely affects productivity, dimensional accuracy, and surface quality of machined components. Particularly detrimental to thin-walled components, chatter can adversely affect productivity, tool life, machine health, as well as the dimensional accuracy and surface quality of machined components. The research aims to address this through on-line condition monitoring to accurately identify and predict processing stability, utilizing signals such as cutting force, voltage, acoustic, and acceleration. The research also employs stability lobe diagrams based on regenerative chatter theory to identify chatter-free optimal process parameters. Furthermore, the research applies machine learning models to detect and control chatter during thin-wall machining of difficult-to-cut materials, using the processed signal data for enhanced on-line performance.
