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

Thesis Abstract

Abstract
The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies in the field of Industrial and Production Engineering has revolutionized manufacturing processes by enabling optimization and automation. This thesis explores the application of AI and ML techniques to enhance efficiency, productivity, and quality in manufacturing operations. The primary objective is to develop a framework that leverages these advanced technologies to optimize manufacturing processes and address challenges faced in the industry. Chapter One provides an introduction to the research topic, discussing the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. The chapter sets the foundation for the study and outlines the key areas of focus. Chapter Two presents a comprehensive literature review on the utilization of AI and ML in industrial and production engineering. The review covers ten key areas, including existing methodologies, applications, benefits, challenges, and future trends in the field. It also examines case studies and best practices to provide a holistic understanding of the subject matter. Chapter Three details the research methodology applied in this study. It includes the research design, data collection methods, tools, and techniques utilized to analyze and interpret the data. The chapter outlines eight key components of the research methodology, ensuring a systematic and robust approach to investigating the research questions. Chapter Four presents an in-depth discussion of the findings derived from the application of AI and ML techniques in optimizing manufacturing processes. The chapter analyzes the results obtained, discusses the implications for industrial and production engineering, and explores potential areas for further research and development. Chapter Five concludes the thesis by summarizing the key findings, highlighting the contributions to the field, and discussing the implications for practice and future research. The chapter also offers recommendations for industry practitioners and policymakers to leverage AI and ML technologies effectively in optimizing manufacturing processes. Overall, this thesis contributes to the body of knowledge in Industrial and Production Engineering by demonstrating the potential of AI and ML techniques in enhancing manufacturing processes. It provides valuable insights for researchers, practitioners, and stakeholders seeking to harness the power of advanced technologies for process optimization and efficiency improvement in the industrial sector.