Multimodal Deep Learning for Manufacturing Systems: Recent Progress and Future Trends

Yinan Wang () and Xiaowei Yue ()
Additional contact information
Yinan Wang: Rensselaer Polytechnic Institute
Xiaowei Yue: Tsinghua University

A chapter in Multimodal and Tensor Data Analytics for Industrial Systems Improvement, 2024, pp 221-252 from Springer

Abstract: Abstract The development of sensing technology provides large amounts and various types of data (e.g., profile, image, point cloud) to describe each stage of a manufacturing process. Deep learning methods have the advantages of efficiently and effectively processing and fusing large-scale datasets and demonstrating outstanding performance in different tasks such as process monitoring and diagnosis. However, multimodal monitoring data raise new challenges to apply existing deep learning methods to solve manufacturing tasks: (1) features across modalities contain complementary yet redundant information (inter-modal data fusion); (2) single modal data can also contain information from different viewpoints (intra-modal data fusion);(3) besides the fusion among data, domain knowledge in advanced manufacturing should also be actively fused into the feature extraction (domain knowledge fusion). This chapter provides three examples of cutting-edge multimodal deep learning methods focusing on inter-modal, intra-modal, and domain knowledge fusion, respectively. From the application aspect, they are designed to solve online process monitoring, product quality inspection, and manufacturing process design, which cover both the forward prediction task and backward optimization task in manufacturing systems. Several prospective directions in multimodal deep learning for advanced manufacturing are further discussed.

Keywords: Multimodal; deep; learning; -; Process; monitoring; -; Product; quality; inspection; -; Manufacturing; systems (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-53092-0_11

Ordering information: This item can be ordered from
http://www.springer.com/9783031530920

DOI: 10.1007/978-3-031-53092-0_11

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().