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Lim, Sunghoon
Unstructured Data Mining and Machine Learning Lab
Research Interests
  • Unstructured Data Mining, Machine Learning, Industrial Artificial Intelligence (AI+X)

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A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

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Title
A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process
Author
Kim, GyeonghoChoi, Jae GyeongKu, MinjooCho, HyewonLim, Sunghoon
Issue Date
2021-09
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE ACCESS, v.9, pp.132455 - 132467
Abstract
The authors of this work propose a deep learning-based fault detection model that can be implemented in the field of plastic injection molding. Compared to conventional approaches to fault detection in this domain, recent deep learning approaches prove useful for on-site problems involving complex underlying dynamics with a large number of variables. In addition, the advent of advanced sensors that generate data types in multiple modalities prompts the need for multimodal learning with deep neural networks to detect faults. This process is able to facilitate information from various modalities in an end-to-end learning fashion. The proposed deep learning-based approach opts for an early fusion scheme, in which the low-level feature representations of modalities are combined. A case study involving real-world data, obtained from a car parts company and related to a car window side molding process, validates that the proposed model outperforms late fusion methods and conventional models in solving the problem.
URI
https://scholarworks.unist.ac.kr/handle/201301/54063
URL
https://ieeexplore.ieee.org/document/9548039
DOI
10.1109/access.2021.3115665
ISSN
2169-3536
Appears in Collections:
SME_Journal Papers
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