Enhanced Semi-Supervised Stamping Process Monitoring with Physically-Informed Feature Extraction

• URL: http://arxiv.org/abs/2504.21389v1
• Date: Wed, 30 Apr 2025 07:42:19 GMT
• Title: Enhanced Semi-Supervised Stamping Process Monitoring with Physically-Informed Feature Extraction
• Authors: Jianyu Zhang, Jianshe Feng, Yizhang Zhu, Fanyu Qi,
• Abstract summary: This study introduces a novel semi-supervised in-process anomaly monitoring framework, utilizing accelerometer signals and physics information, to capture the process anomaly effectively.<n>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.
• Score: 3.0043530290654585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In tackling frequent anomalies in 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.

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