An advanced product inspection and sorting system using artificial intelligence

The manufacturing sector is experiencing a notable transformation due to the incorporation of Industry 4.0 and the emerging concepts of Industry 5.0. Artificial intelligence (AI) plays a significant role in driving this transformation, particularly in the domain of product inspection and sorting systems. Incorporating digital computer vision, high accuracy and resolution sensors, and bigdata-driven simulations into manufacturing processes, the vision of smart manufacturing becomes tangible. These technologies offer practical solutions for automating product inspection and sorting processes, providing non-destructive and cost-effective alternatives. This ongoing research aims to develop a real-time product inspection and sorting system utilizing artificial intelligence, specifically focusing on convolutional neural networks (CNNs) and machine learning algorithms. The proposed approach adopts a dynamic methodology, leveraging the synergistic capabilities of CNNs and machine learning algorithms. CNN is trained on datasets containing both none-defective and defective product samples, to extract features from images. These features are then further refined and classified by machine learning algorithms. Through rigorous training on diverse datasets, the system developed a robust ability to distinguish between none-detective and defective products and achieving an accuracy close to 98.10%.