A multimodal deep learning-based fault detection model for a plastic injection molding process

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 u...

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Bibliographic Details
Published in:IEEE access 2021-01, Vol.9, p.1-1
Main Authors: Kim, Gyeongho, Choi, Jae Gyeong, Ku, Minjoo, Cho, Hyewon, Lim, Sunghoon
Format: Article
Language:English
Subjects:
Artificial neural networks
Automotive parts
Data models
Deep learning
early fusion
Fault detection
Feature extraction
industrial AI
Injection molding
Machine learning
multimodal learning
plastic injection molding
Plastics
Temperature measurement
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3115665