Intelligent and Automated Product Quality Assurance System using Machine Learning

Abstract

The paper discusses the use of advanced technologies like the Internet of Things (IoT) and cyber-physical systems (CPS) to enhance product quality assurance in production industries. It proposes an intelligent approach using machine learning and termed the model as ETLViT, which integrates transfer learning models and vision transformers (ViT). The proposed model is a three-layered architecture integrated with a cyber-physical system (CPS). The first layer collects images of the processed products using a CPS, while the second layer transmits the data to a cloud server. The third layer analyzes the input image and predicts any faults in the product, with results also stored in the cloud server. The learning process involves noise removal, feature extraction, and classification, which are performed simultaneously by the ensemble model called ETLViT, designed using transfer learning and vision transformers. The result was presented with transfer learning models, ensemble model with machine learning and ETLViT. Among all of them, ETLViT outperforms the best.