Improving Product Quality Control in Smart Manufacturing through Transfer Learning-Based Fault Detection

Reducing product failure rates is crucial to ensure a healthy production line. However, the current approach for inspecting product quality is inefficient, costly, and time-consuming, relying on manual inspection at the end of the production process. This research paper focuses on the utilization of transfer learning, an intelligent machine-learning technique, to improve the accuracy and efficiency of product quality inspection in production lines. The proposed approach utilizes transfer learning to adapt a pre-trained model from a related domain to the target domain, enabling accurate product quality prediction with limited data. The reference architecture provides a framework for implementing the proposed approach in a manufacturing environment, enabling real-time monitoring and decision-making based on product quality predictions. The proposed approach can improve the accuracy of faulty product detection by up to 11% compared to traditional techniques, as demonstrated by evaluations on a real-world production dataset.