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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.19652 |
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| _version_ | 1866909803900567552 |
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| author | Song, Xiaoyang Sun, Wenbo Kayitmazbatir, Metin Jionghua Jin |
| author_facet | Song, Xiaoyang Sun, Wenbo Kayitmazbatir, Metin Jionghua Jin |
| contents | This paper proposes a deep learning-based approach for in-situ process monitoring that captures nonlinear relationships between in-control high-dimensional process signature signals and offline product quality data. Specifically, we introduce a Deep Canonical Correlation Analysis (DCCA)-based framework that enables the joint feature extraction and correlation analysis of multi-modal data sources, such as optical emission spectra and CT scan images, which are collected in advanced manufacturing processes. This unified framework facilitates online quality monitoring by learning quality-oriented representations without requiring labeled defective samples and avoids the non-normality issues that often degrade traditional control chart-based monitoring techniques. We provide theoretical guarantees for the method's stability and convergence and validate its effectiveness and practical applicability through simulation experiments and a real-world case study on Direct Metal Deposition (DMD) additive manufacturing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19652 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quality-Ensured In-Situ Process Monitoring with Deep Canonical Correlation Analysis Song, Xiaoyang Sun, Wenbo Kayitmazbatir, Metin Jionghua Jin Applications This paper proposes a deep learning-based approach for in-situ process monitoring that captures nonlinear relationships between in-control high-dimensional process signature signals and offline product quality data. Specifically, we introduce a Deep Canonical Correlation Analysis (DCCA)-based framework that enables the joint feature extraction and correlation analysis of multi-modal data sources, such as optical emission spectra and CT scan images, which are collected in advanced manufacturing processes. This unified framework facilitates online quality monitoring by learning quality-oriented representations without requiring labeled defective samples and avoids the non-normality issues that often degrade traditional control chart-based monitoring techniques. We provide theoretical guarantees for the method's stability and convergence and validate its effectiveness and practical applicability through simulation experiments and a real-world case study on Direct Metal Deposition (DMD) additive manufacturing. |
| title | Quality-Ensured In-Situ Process Monitoring with Deep Canonical Correlation Analysis |
| topic | Applications |
| url | https://arxiv.org/abs/2509.19652 |