Thesis Abstract

Abstract
The optimization of manufacturing processes using artificial intelligence (AI) techniques in the field of Industrial and Production Engineering represents a significant advancement in modern manufacturing practices. This thesis explores the application of AI methodologies to enhance efficiency, productivity, and quality in industrial production settings. Through the integration of AI technologies, such as machine learning, neural networks, and predictive analytics, this research aims to address the complex challenges faced by manufacturing industries in achieving operational excellence. The introduction provides a comprehensive overview of the research topic, highlighting the growing importance of AI in the manufacturing sector and the potential benefits it offers. The background of the study delves into the historical context of manufacturing processes and the evolution of AI technologies in this domain. This sets the stage for a detailed exploration of the problem statement, which identifies the key issues that AI can help address within manufacturing operations. The objectives of the study are outlined to guide the research process, focusing on improving process efficiency, reducing waste, optimizing resource utilization, and enhancing overall production performance. The limitations of the study are also acknowledged, emphasizing the need for a targeted and focused approach within the scope of the research. The significance of the study underscores the potential impact of AI-driven optimization on the competitiveness and sustainability of manufacturing industries. The structure of the thesis outlines the organization of the research content, providing a roadmap for readers to navigate through the various chapters and sections. Definitions of key terms used throughout the thesis are provided to ensure clarity and understanding of the terminology employed. The literature review in Chapter Two presents a comprehensive analysis of existing research and developments in the application of AI techniques to manufacturing processes. Drawing on a diverse range of scholarly sources, this chapter evaluates the current state-of-the-art in AI technologies and their potential implications for industrial and production engineering. Chapter Three focuses on the research methodology, detailing the approach, data collection methods, experimental design, and analytical techniques employed in the study. By outlining a systematic framework for data analysis and interpretation, this chapter aims to ensure the reliability and validity of the research findings. In Chapter Four, the discussion of findings critically examines the results of the research, highlighting the key insights, trends, and outcomes derived from the application of AI techniques to manufacturing processes. Through a rigorous analysis of the data, this chapter offers valuable insights into the effectiveness and applicability of AI-driven optimization strategies. Finally, Chapter Five presents the conclusion and summary of the thesis, encapsulating the main findings, implications, and contributions of the research. By synthesizing the key takeaways and recommendations, this chapter provides a comprehensive overview of the research outcomes and their potential impact on future advancements in industrial and production engineering. In conclusion, this thesis offers a detailed exploration of the optimization of manufacturing processes using AI techniques in Industrial and Production Engineering. By leveraging the power of AI technologies, manufacturing industries can enhance their operational efficiency, improve product quality, and drive innovation in a rapidly evolving global market landscape.

Thesis Overview