Enhanced semi-supervised stamping process monitoring with physically-informed feature extraction
This work addresses batch defects in stamping manufacturing, but it is incremental as it combines existing semi-supervised and feature extraction methods with physics information.
This study tackled batch anomalies in high-speed stamping processes by introducing a semi-supervised anomaly monitoring framework that uses accelerometer signals and physics-informed feature extraction, resulting in enhanced performance for process anomaly monitoring as validated on a real-world dataset.
In tackling frequent batch anomalies in high-speed stamping processes, this study introduces a novel semi-supervised in-process anomaly monitoring framework, utilizing accelerometer signals and physics information, to capture the process anomaly effectively. The proposed framework facilitates the construction of a monitoring model with imbalanced sample distribution, which enables in-process condition monitoring in real-time to prevent batch anomalies, which helps to reduce batch defects risk and enhance production yield. Firstly, to effectively capture key features from raw data containing redundant information, a hybrid feature extraction algorithm is proposed to utilize data-driven methods and physical mechanisms simultaneously. Secondly, to address the challenge brought by imbalanced sample distribution, a semi-supervised anomaly detection model is established, which merely employs normal samples to build a golden baseline model, and a novel deviation score is proposed to quantify the anomaly level of each online stamping stroke. The effectiveness of the proposed feature extraction method is validated with various classification algorithms. A real-world in-process dataset from stamping manufacturing workshop is employed to illustrate the superiority of proposed semi-supervised framework with enhance performance for process anomaly monitoring.