A Comparative Study on Application of Artificial Intelligence for Quality Assurance in Manufacturing

Increasing demands in industrial processes necessitate early fault detection, efficient production, and quality assurance. In the manufacturing process, a s more sensors, smart devices, and cyber-physical networks are interconnected, more data is created. This data might be used to enhance manufacturing procedures and product quality. Machine learning algorithms may improve quality assurance (QA). But integration of artificial intelligence (AI) and machine learning (ML) into manufacturing system seldom have the skills to obtain this data. This paper describes an automated visual inspection system that ensures optical quality. An automated quality assurance platform for additive manufacturing based on machine learning is described in this paper. This paper also presented a comparative feature analysis of existing AI based quality assurance in different application areas of manufacturing. After complete analysis of these techniques, the paper also explored the current challenges and future research directions.