Enhancing Quality Control - The evolution from Traditional Tools to AI-integrated Vision Systems

This thesis investigates the evolution and current practices of quality control, emphasizing both traditional methods and modern advancements facilitated by Industry 4.0 and artificial intelligence (AI). The introductory chapter provides a comprehensive overview of quality, tracing its historical development and examining key methodologies such as Total Quality Management (TQM), Lean Manufacturing, Six Sigma, and various statistical tools. The chapter further covers statistical inference in quality control, discussing sampling methods, hypothesis testing, Statistical Process Control (SPC), and control charts. It also reviews the ISO 9000 family of standards, focusing on ISO 9001:2015 and its global diffusion. It concludes by identifying the criticalities and limitations of traditional quality control procedures. Chapter 2 transitions into the era of Industry 4.0, highlighting its definition and the performance of Italy's machine tool, robotics, and industrial automation sectors. It examines the significant impact of Industry 4.0 on quality control processes and the integration of advanced technologies into these processes. The third chapter focuses on the application of AI in quality control, detailing machine learning techniques, including linear regression and classification algorithms. It highlights a practical AI implementation, such as those developed by Cognex, international developer of machine vision systems and software used in automated manufacturing and quality control, and explores the financial analysis of automated inspection systems. The thesis concludes with a reflection from an ethical and social point of view and on possible future developments in quality control.