Thesis Abstract

Abstract
This thesis explores the application of artificial intelligence (AI) techniques in optimizing manufacturing processes within an industrial setting. With the rapid advancements in AI technologies, industries are increasingly turning to these innovative solutions to enhance efficiency, productivity, and competitiveness. The research aims to investigate how AI can be leveraged to streamline manufacturing operations and improve overall performance. The study begins by providing an introduction to the topic, outlining the background of the study, identifying the problem statement, stating the objectives, highlighting the limitations, defining the scope, emphasizing the significance, and presenting the structure of the thesis. Chapter two delves into a comprehensive literature review that examines existing research, theories, and case studies related to AI applications in manufacturing optimization. This section aims to provide a solid theoretical foundation for the research. Chapter three details the research methodology employed in this study. It includes the research design, data collection methods, data analysis techniques, sampling procedures, and ethical considerations. The methodology chapter aims to provide a clear and systematic framework for conducting the research and analyzing the results effectively. Chapter four presents an in-depth discussion of the findings obtained from the research. The results of applying AI techniques to optimize manufacturing processes are analyzed and interpreted, providing insights into the benefits, challenges, and implications of such implementations. This chapter aims to contribute valuable knowledge to the field of industrial engineering and production optimization. Finally, chapter five concludes the thesis by summarizing the key findings, discussing the implications for practice and future research directions. The conclusion also reflects on the significance of the study, its contributions to the field, and the potential impact on industrial practices. Overall, this research contributes to the body of knowledge on AI applications in manufacturing optimization and provides practical insights for industrial practitioners and researchers alike.

Thesis Overview

The project titled "Optimization of manufacturing processes using artificial intelligence techniques in an industrial setting" aims to address the growing need for enhancing efficiency and productivity in manufacturing industries through the integration of artificial intelligence (AI) technologies. With the rapid advancement of AI in recent years, there is a significant opportunity to revolutionize traditional manufacturing processes and achieve higher levels of optimization. This research project focuses on leveraging AI techniques such as machine learning, predictive analytics, and optimization algorithms to analyze and optimize various aspects of manufacturing operations. By harnessing the power of AI, the project seeks to improve key performance indicators such as production output, quality, energy efficiency, and overall cost-effectiveness. The industrial setting chosen for this study provides a real-world context for implementing and testing AI-driven optimization strategies. By collaborating with industry partners, the project aims to gather valuable insights and data to develop tailored AI solutions that address specific challenges faced by manufacturing companies. Key aspects of the research will include identifying the existing manufacturing processes, conducting a thorough analysis of operational data, designing AI models for process optimization, and implementing these models in a controlled industrial environment. The project will also evaluate the performance of AI-driven optimization solutions against traditional methods to demonstrate the potential benefits and advantages of adopting AI technologies in manufacturing. Overall, this research overview highlights the significance of applying AI techniques to optimize manufacturing processes, improve efficiency, and drive innovation in industrial settings. By exploring the capabilities of AI in enhancing decision-making, resource allocation, and process control, this project aims to contribute valuable insights to the field of industrial engineering and pave the way for a more intelligent and efficient manufacturing future.