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 plastic injection molding. Compared to conventional approaches domain, recent learning prove useful for on-site problems involving complex underlying dynamics with large number variables. In addition, advent advanced sensors generate data types multiple modalities prompts need multimodal neural networks detect faults. This process is able facilitate information from various an end-to-end fashion. proposed approach opts early fusion scheme, which low-level feature representations are combined. A case study real-world data, obtained car parts company and related window side molding process, validates outperforms late methods models solving problem.