An advanced product inspection and sorting system using artificial intelligence

University of Derby, Markeaton Street, Derby DE22 3AW, United Kingdom.

^* Corresponding author: a.esther1@unimail.derby.ac.uk

Abstract

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 utilising 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. To extract features from images, CNN is trained on datasets containing both none-defective and defective product samples. 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%