Optimization of Manufacturing Processes using Artificial Intelligence in Industrial and Production Engineering

Thesis Abstract

Abstract
This thesis focuses on the Optimization of Manufacturing Processes using Artificial Intelligence (AI) in the field of Industrial and Production Engineering. The integration of AI technologies in manufacturing processes has gained significant attention due to its potential to enhance efficiency, productivity, and decision-making. The objective of this research is to investigate how AI can be leveraged to optimize various manufacturing processes and improve overall performance in the industrial sector. The study begins with a comprehensive introduction to the research topic, providing background information on the significance of AI in industrial engineering. The problem statement highlights the challenges faced by traditional manufacturing processes and the potential benefits of incorporating AI technologies. The objectives of the study are outlined to guide the research towards achieving specific goals, while also acknowledging the limitations and scope of the study. A thorough review of relevant literature is conducted in Chapter Two, exploring existing studies, methodologies, and technologies related to the optimization of manufacturing processes using AI. The literature review provides insights into current trends, challenges, and opportunities in the field, laying the foundation for the research methodology. Chapter Three details the research methodology employed in this study, including data collection methods, tools, and techniques. The chapter outlines the research design, sampling strategy, data analysis procedures, and validation methods to ensure the reliability and validity of the research findings. The findings of the study are presented and discussed in Chapter Four, highlighting the outcomes of applying AI techniques to optimize manufacturing processes. The discussion explores the implications of the findings, identifies key trends, and offers insights into the practical implications for industrial and production engineering. Finally, Chapter Five presents the conclusion and summary of the thesis, summarizing the key findings, contributions, and implications of the research. The study concludes with recommendations for future research directions and practical applications of AI in optimizing manufacturing processes in the industrial sector. In conclusion, this thesis contributes to the growing body of knowledge on the application of AI in industrial and production engineering, specifically focusing on the optimization of manufacturing processes. By leveraging AI technologies, organizations can enhance efficiency, reduce costs, and improve overall performance in the manufacturing industry. This research serves as a valuable resource for academics, practitioners, and policymakers seeking to harness the power of AI for industrial optimization.