CNN-Based Defect Inspection for Injection Molding Using Edge Computing and Industrial IoT Systems
Source abstract
Abstract
Currently, the development of automated quality inspection is drawing attention as a major component of the smart factory. However, injection molding processes have not received much attention in this area of research because of product diversity, difficulty in obtaining uniform quality product images, and short cycle times. In this study, we proposed a defect inspection system for injection molding in edge intelligence. Using data augmentation, we solved the data shortage and imbalance problem of small and medium-sized enterprises (SMEs), introduced the actual smart factory method of the injection process, and measured the performance of the developed artificial intelligence model. The accuracy of the proposed model was more than 90%, proving that the system can be applied in the field.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Digital Transformation in Industry · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Smart Factory Convergence, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea
Funding
Ministry of Science and ICT, South Korea · ITRC (Information Technology Research Center)
References (62)
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