Abstract
This chapter outlines the synergies achieved through the fusion of engineering and statistical approaches for quality improvement. It emphasizes the integration of data science and system theory, leveraging in-process sensing data for comprehensive process monitoring, diagnosis, and control. Multimodal data fusion is a key strategy for quality improvement, leading to root cause diagnosis, automatic compensation, and defect prevention. This approach goes beyond traditional aspects, such as change detection, off-line adjustment, and defect inspection. The chapter provides a concise overview of multimodal data fusion, highlights its recent developments and applications in data fusion for structured and unstructured high-dimensional data, and outlines challenges and opportunities in contemporary data-rich systems. Additionally, it explores future research directions, with a specific emphasis on harnessing emerging machine learning tools to enhance quality in systems with rich sensing data.
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Shi, J., Biehler, M., Mou, S. (2024). Synergy of Engineering and Statistics: Multimodal Data Fusion for Quality Improvement. In: Gaw, N., Pardalos, P.M., Gahrooei, M.R. (eds) Multimodal and Tensor Data Analytics for Industrial Systems Improvement. Springer Optimization and Its Applications, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-031-53092-0_12
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