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.

Thesis Overview

The project titled "Optimization of Manufacturing Processes Using Artificial Intelligence Techniques in Industrial and Production Engineering" focuses on leveraging cutting-edge artificial intelligence (AI) techniques to enhance efficiency, productivity, and quality in manufacturing processes within the realm of Industrial and Production Engineering. Industrial and Production Engineering is a critical field that deals with optimizing complex systems for the production of goods and services. In recent years, the integration of AI technologies has revolutionized various industries, offering tremendous opportunities for process optimization, predictive maintenance, quality control, and overall operational enhancement. This research project aims to explore the application of AI techniques such as machine learning, deep learning, and optimization algorithms in the context of manufacturing processes. By harnessing the power of AI, industrial and production engineers can analyze vast amounts of data, identify patterns, predict outcomes, and make data-driven decisions to streamline operations and maximize efficiency. The project will begin with a comprehensive literature review to examine existing research, methodologies, and technologies related to AI in manufacturing. This review will provide a solid foundation for understanding the current state of the field and identifying gaps that warrant further exploration. Subsequently, the research methodology will be meticulously designed to incorporate data collection, model development, experimentation, and analysis. Various AI algorithms will be applied to real-world manufacturing data to optimize processes, minimize waste, reduce downtime, and improve overall production performance. The findings of this study will be presented in detail in the discussion section, highlighting the effectiveness of AI techniques in optimizing manufacturing processes. The results will demonstrate how AI can significantly enhance efficiency, reduce costs, and increase the competitiveness of industrial and production systems. Finally, the project will conclude with a comprehensive summary and conclusion, outlining key insights, implications, and recommendations for future research and practical applications. By delving into the realm of AI-driven optimization in manufacturing processes, this research endeavors to contribute valuable insights to the field of Industrial and Production Engineering and pave the way for enhanced operational excellence in the industry.