The Impact of AI on Enhancing the Quality of Digital Industrial Products

Industrial product quality control has moved on to a new level with the application of AI technology, to achieve the steps of detection and inspection. The research explores how AI models such as YOLOv8 affect product quality by decreasing the number of product defect and improving the detection of defective products. Our findings highlight the potential for integrating A YOLOv8 model for quality control solutions that will ultimately lead to fewer defects during production and improved efficiency. Moreover, the study delves into crucial evaluation metrics, such as defect reduction percentages, and MAE performance, to corroborate the effectiveness of the YOLOv8 implementation. The research, findings, and observations indicate that the implementation or usage of AI in industrial quality control provides a more reliable and scalable approach to scaling defect detection into multiple stages. But there are still obstacles, like high upfront costs and complicated integration. With the evolving field of AI, future efforts should address refining the models for quality control using AI-driven techniques and integrating real-time monitoring systems for improved manufacturing performance. The findings from this research highlight the potential impact AI can have on the quality and reliability of industrial products.