Exploring and optimizing deep neural networks for precision defect detection system in injection molding process

This research employs transfer learning to explore and compare pre-trained deep learning models for defect detection in injection molding processes. It introduces advanced neural network architectures, specifically Inception and ResNet50, which have not been extensively studied in this context. Through systematic evaluation using techniques such as data augmentation, architecture modification, and hyperparameter tuning, the study aims to enhance detection precision. The methodology addresses deployment challenges inherent in defect detection systems and emphasizes the importance of model selection for achieving desired goals. Comparative assessments with contemporary models highlight the effectiveness of the proposed approach in real-world production settings. Improved results obtained with the Inception model demonstrate a precision of 92.3%, recall of 100%, and F1 score of 96%, surpassing ResNet50 as well as previous studies utilizing VGG16 and Yolo v5. This underscores the reliability of the Inception model for defects detection in practical scenarios. Furthermore, beyond accuracy enhancement, the study aligns with the broader goal of advancing sustainable manufacturing by integrating smarter defect detection mechanisms. The findings not only offer a robust framework for selecting optimal detection models but also lay the groundwork for future research endeavors aimed at improving adaptability and efficiency in defect detection systems across various industrial applications. This contributes to the evolution of intelligent manufacturing processes, balancing quality and profitability objectives.