Improving Die-Casting Part Classification using Transfer Learning with Deep Convolutional Neural Networks

Product quality is a crucial factor in the manufacturing process today, as it determines the company's competitive advantages and the consumer's requirements. The problem arises from traditional techniques and quality control methods becoming less effective in the current environments, hence the increasing demand for advanced technologies. This study aims to design and evaluate the effectiveness of the deep learning (DL) technique in identifying defects in die-casting parts. The dataset has been preprocessed, and data augmentation techniques are then employed to mitigate the risk of model overfitting and improve generalization capability. Five pretrained DL models—AlexNet, DenseNet, EfficientNet, SqueezeNet, and WideResNet—were compared with a proposed custom Convolutional Neural Network (CNN) model. The evaluation was conducted using performance metrics, including precision, recall, accuracy, and F1-score. The proposed custom CNN model achieved the highest accuracy of 98.08%, outperforming all other models, with the next best accuracy being 97.70% from the SqueezeNet model. The results show that the proposed DL-based approach, especially the custom CNN model, can greatly improve the quality control process and lower the manufacturing of faulty goods.