Optimization of Manufacturing Processes Using Artificial Intelligence Techniques in Industrial and Production Engineering

Thesis Abstract

Abstract
The integration of Artificial Intelligence (AI) techniques in industrial and production engineering has significantly transformed the manufacturing landscape by enhancing process efficiency and productivity. This thesis focuses on the optimization of manufacturing processes through the application of AI techniques, aiming to improve overall operational performance in industrial settings. The research investigates how AI algorithms and tools can be leveraged to streamline production processes, minimize waste, reduce costs, and enhance product quality in industrial and production engineering domains. Chapter One provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the foundation for understanding the importance of optimizing manufacturing processes using AI techniques in the industrial and production engineering context. Chapter Two is a comprehensive literature review that examines existing studies, frameworks, and applications related to AI in manufacturing processes. The review covers ten key areas, including AI technologies in industrial engineering, optimization techniques, AI in production planning, predictive maintenance, quality control, supply chain management, and human-robot collaboration. It also explores challenges and opportunities associated with integrating AI in industrial settings. Chapter Three details the research methodology employed in this study. It outlines the research design, data collection methods, AI tools and algorithms utilized, experimental setup, and data analysis techniques. The chapter elaborates on the steps taken to investigate and optimize manufacturing processes using AI techniques, providing a clear overview of the research methodology adopted. Chapter Four presents a detailed discussion of the research findings, highlighting the outcomes of implementing AI techniques for process optimization in industrial and production engineering. The chapter analyzes the results obtained from the experiments conducted, showcasing improvements in production efficiency, resource utilization, and product quality. It also discusses the challenges encountered during the optimization process and proposes solutions for future research. Chapter Five serves as the conclusion and summary of the thesis, encapsulating the key findings, implications, limitations, and recommendations for future research. The chapter emphasizes the significance of integrating AI techniques in manufacturing processes to enhance competitiveness, sustainability, and innovation in industrial and production engineering sectors. In conclusion, this thesis contributes to the growing body of knowledge on the application of AI techniques for optimizing manufacturing processes in industrial and production engineering. By leveraging AI algorithms and tools, organizations can achieve operational excellence, improve decision-making processes, and drive continuous improvement in their manufacturing operations. The research underscores the transformative potential of AI in reshaping industrial practices and fostering a more efficient and sustainable manufacturing ecosystem.