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• W3202911331 endingPage "132467" @default.
• W3202911331 startingPage "132455" @default.
• W3202911331 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." @default.
• W3202911331 created "2021-10-11" @default.
• W3202911331 creator A5001125586 @default.
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• W3202911331 date "2021-01-01" @default.
• W3202911331 modified "2023-10-17" @default.
• W3202911331 title "A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process" @default.
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• W3202911331 doi "https://doi.org/10.1109/access.2021.3115665" @default.
• W3202911331 hasPublicationYear "2021" @default.
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