2025/09/24 by Song, Xiaoyang, Sun, Wenbo, Kayitmazbatir, Metin +2
#Applications (stat.AP) #FOS: Computer and information sciences
paper · doi:10.48550/arxiv.2509.19652
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.