Machine Vision Lab Experiment Based Convolutional Neural Network for Potential Intelligent Manufacturing Applications

Ardian Webi Kirda, Kamil Gatnar, Khairul Muzaka, Nur Arifin Akbar

This study developed a deep learning-based image classification method for the automated assessment of aluminum edge quality after machining processes. The proposed approach classified edge conditions into three categories: Normal Edge, Burr Edge, and Sharp Edge. A Convolutional Neural Network (CNN) based on the VGG16 architecture was employed as the feature extraction backbone, with modifications to the final fully connected layers to accommodate the three-class classification task. The model was trained and evaluated on a dataset of 660 aluminum edge images captured under controlled laboratory conditions at the Robotic Manufacturing Laboratory, University of Brunei Darussalam. The training strategy employed a stratified split with 360 training images, 240 testing images, and 60 validation images. Data augmentation techniques (horizontal flip, rotation ±15°, brightness adjustment) were applied to enhance model generalization. The optimized model achieved an overall classification accuracy of 98.75% on the test set. Precision, recall, and F1-scores for all three classes exceeded 0.97. For practical deployment, the trained model was deployed on an NVIDIA Jetson Nano embedded platform, achieving an average inference time of 47 ms per image at an input resolution of 500×500 pixels (approximately 21 FPS). These results demonstrated the feasibility of real-time edge quality assessment for intelligent manufacturing applications.