Thesis Abstract

Abstract
The field of Industrial and Production Engineering is witnessing a rapid transformation with the integration of artificial intelligence (AI) techniques to optimize manufacturing processes. This thesis explores the application of AI techniques in enhancing efficiency, productivity, and quality within industrial and production settings. The primary objective of this research is to investigate how AI can be leveraged to streamline manufacturing processes and improve overall performance in the industrial sector. Chapter 1 provides an introduction to the research topic, including the background of the study, problem statement, research objectives, limitations, scope, significance, and the structure of the thesis. The chapter also defines key terms relevant to the study, laying the foundation for the subsequent chapters. Chapter 2 presents a comprehensive literature review on AI techniques and their applications in industrial and production engineering. The review covers ten key areas, including machine learning, neural networks, optimization algorithms, predictive maintenance, quality control, supply chain management, scheduling, robotics, and simulation. Chapter 3 outlines the research methodology employed in this study. It includes detailed descriptions of the research design, data collection methods, data analysis techniques, experimental setup, and validation procedures. The chapter also discusses ethical considerations and limitations encountered during the research process. Chapter 4 presents an in-depth discussion of the findings obtained from the research. It analyzes the impact of AI techniques on manufacturing processes, identifies key challenges and opportunities, and discusses the implications for industrial and production engineering. The chapter also highlights specific case studies and real-world examples to illustrate the practical applications of AI in optimizing manufacturing processes. Finally, Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and providing recommendations for future studies. The conclusion emphasizes the significance of integrating AI techniques in industrial and production engineering to achieve higher levels of efficiency, productivity, and quality. In conclusion, this thesis contributes to the existing body of knowledge by exploring the potential of AI techniques in optimizing manufacturing processes within the industrial and production engineering domain. By leveraging AI technologies, organizations can enhance their competitive advantage, improve operational performance, and drive innovation in the manufacturing sector.

Thesis Overview

The project titled "Optimization of Manufacturing Processes using Artificial Intelligence Techniques in Industrial and Production Engineering" aims to address the growing need for efficient and effective manufacturing processes in the industrial and production engineering sector. This research seeks to leverage the power of artificial intelligence (AI) techniques to optimize various aspects of manufacturing operations, ultimately enhancing productivity, quality, and cost-effectiveness within industrial settings. The utilization of AI in manufacturing processes has gained significant attention in recent years due to its potential to revolutionize traditional methods and streamline operations. By integrating AI techniques such as machine learning, predictive analytics, and automated decision-making systems, manufacturers can achieve higher levels of efficiency and performance across the production chain. Key objectives of this research include investigating the current challenges and limitations faced by industrial and production engineers in optimizing manufacturing processes, exploring the potential benefits of AI integration in improving efficiency and quality control, and developing practical strategies for implementing AI solutions in real-world manufacturing environments. The study will begin with a comprehensive review of existing literature on AI applications in manufacturing, highlighting key findings, trends, and best practices. Subsequently, a detailed methodology will be outlined, which will include data collection procedures, AI model selection criteria, implementation strategies, and performance evaluation metrics. Through empirical data analysis and experimentation, the research aims to demonstrate the effectiveness of AI techniques in optimizing manufacturing processes, showcasing improvements in key performance indicators such as production output, quality control, and resource utilization. Furthermore, the study will assess the economic implications of AI adoption, including cost savings, return on investment, and competitive advantage. The findings of this research are expected to contribute valuable insights to the field of industrial and production engineering, offering practical recommendations for industry professionals, researchers, and policymakers looking to leverage AI technologies for process optimization. By bridging the gap between theory and practice, this study seeks to advance the adoption of AI in manufacturing sectors, driving innovation and sustainable growth in the industry.