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

Thesis Abstract

Abstract
The advent of artificial intelligence (AI) has revolutionized various industries by providing advanced tools and techniques to optimize processes and enhance efficiency. In the field of Industrial and Production Engineering, the utilization of AI technologies has become increasingly popular to streamline manufacturing processes and improve overall productivity. This thesis focuses on the "Optimization of Manufacturing Processes using Artificial Intelligence Techniques in Industrial and Production Engineering" with the aim of investigating how AI can be effectively applied to enhance manufacturing operations. The research begins with a comprehensive introduction that outlines the background of the study, presents the problem statement, objectives, limitations, scope, significance of the study, and defines key terms to provide a clear understanding of the research context. A detailed literature review in Chapter Two delves into ten key areas related to AI applications in manufacturing processes, including AI algorithms, machine learning techniques, optimization models, and case studies highlighting successful implementations of AI in industrial settings. Chapter Three presents the research methodology employed in this study, which includes the research design, data collection methods, data analysis techniques, and tools used for the investigation. The methodology section consists of eight components, covering the experimental setup, data acquisition process, AI model development, validation procedures, and performance evaluation metrics to ensure the rigor and reliability of the research findings. In Chapter Four, the discussion of findings elaborates on the results obtained from the application of AI techniques in optimizing manufacturing processes. This section analyzes the effectiveness of AI algorithms in improving production efficiency, reducing costs, minimizing errors, and enhancing quality control measures. The findings are presented in detail, highlighting the impact of AI on different aspects of manufacturing operations and providing insights into the practical implications for industrial and production engineering professionals. Finally, Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research outcomes, and providing recommendations for future studies in this area. The conclusion emphasizes the importance of integrating AI technologies into manufacturing processes to achieve sustainable improvements in productivity, quality, and competitiveness. Overall, this thesis contributes to the growing body of knowledge on the application of AI in industrial and production engineering, offering valuable insights for researchers, practitioners, and stakeholders interested in enhancing manufacturing operations through advanced technology solutions.