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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.21389 |
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| _version_ | 1866916727943593984 |
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| author | Zhang, Jianyu |
| author_facet | Zhang, Jianyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21389 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhanced semi-supervised stamping process monitoring with physically-informed feature extraction Zhang, Jianyu Machine Learning 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. |
| title | Enhanced semi-supervised stamping process monitoring with physically-informed feature extraction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.21389 |