Deep Learning for Vibration Signature Analysis in CNC Turning: A Review of Modern Techniques and Future Directions

Abstract

The digital transformation of manufacturing, driven by Industry 4.0, demands unprecedented levels of autonomy, quality, and operational efficiency. At the heart of these smart production systems lies Computer Numerical Control (CNC) turning, a critical subtractive manufacturing process known for its versatility, repeatability, and ability to achieve sub-micron precision. As the backbone of modern machining, CNC turning plays a vital role in sectors such as aerospace, automotive, and medical device manufacturing. Realizing its full potential requires robust, real-time in-process monitoring to ensure zero-defect output and stable performance. Vibration Signature Analysis (VSA) is a critical, non-invasive diagnostic tool for characterizing the dynamic behavior of machining operations. However, conventional VSA methods rely heavily on manual feature extraction and linear modeling, which are limited in handling the nonlinear, high-dimensional nature of machining dynamics. Deep learning (DL) has introduced a transformative shift, enabling automated, end-to-end learning from raw sensor data to deliver precise diagnostics and prognostics. This review provides a comprehensive overview of DL-based approaches applied to VSA in CNC turning. It critically evaluates dominant architectures, highlights the persistent challenge of model generalizability, and proposes-es concrete solutions. Furthermore, it highlights promising future directions such as domain adaptation, transfer learning, and physics-informed neural networks, aiming to guide researchers and practitioners toward the development of intelligent, adaptive, and self-optimizing manufacturing systems.