Chatter detection in simulated machining data: a simple refined approach to vibration data

• Published in: International journal of advanced manufacturing technology 2024-06, Vol.132 (9-10), p.4541-4557
• Main Authors: Alberts, Matthew, St. John, Sam, Jared, Bradley, Karandikar, Jaydeep, Khojandi, Anahita, Schmitz, Tony, Coble, Jamie
• Format: Article
• Language: English
• Subjects: CAE) and Design; Chatter; Computer-Aided Engineering (CAD; Data collection; Engineering; Fast Fourier transformations; Feature extraction; Fourier series; Fourier transforms; Industrial and Production Engineering; Machine learning; Machining; Mechanical Engineering; Media Management; Original Article; Software; Vibration monitoring
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-024-13590-z

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.