Synergy of Engineering and Statistics: Multimodal Data Fusion for Quality Improvement

Jianjun Shi (), Michael Biehler () and Shancong Mou ()
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Jianjun Shi: Georgia Institute of Technology
Michael Biehler: Georgia Institute of Technology
Shancong Mou: Georgia Institute of Technology

A chapter in Multimodal and Tensor Data Analytics for Industrial Systems Improvement, 2024, pp 255-279 from Springer

Abstract: 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.

Keywords: Data fusion; In-process quality improvement; Engineering-driven data science (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-53092-0_12

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DOI: 10.1007/978-3-031-53092-0_12

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