Optimization of Production Processes using Artificial Intelligence Techniques in a Manufacturing Setting

Thesis Abstract

Abstract
This thesis focuses on the application of Artificial Intelligence (AI) techniques for optimizing production processes in a manufacturing setting. The integration of AI technologies has gained significant attention in industrial and production engineering due to its potential in enhancing efficiency, productivity, and decision-making processes. This research aims to investigate the effectiveness of utilizing AI techniques, such as machine learning algorithms and predictive analytics, to optimize production processes and maximize operational performance. The introduction provides an overview of the research background, highlighting the significance of AI in industrial settings. The background of the study discusses the evolution of AI technologies and their impact on manufacturing processes. The problem statement identifies the existing challenges and limitations in traditional production optimization methods, emphasizing the need for advanced AI solutions. The objectives of the study outline the specific goals and targets that this research aims to achieve, including improving production efficiency, reducing costs, and enhancing overall performance. The literature review explores existing studies and scholarly works related to AI applications in production optimization. It covers ten key areas, including AI in manufacturing, machine learning algorithms, predictive maintenance, process optimization, and decision support systems. By analyzing these literature sources, this research aims to build upon existing knowledge and identify gaps that can be addressed through empirical research. The research methodology section outlines the approach and techniques employed to investigate the research objectives. It includes details on data collection methods, AI model development, experimental design, and performance evaluation criteria. The chapter consists of eight subsections, covering aspects such as data preprocessing, model training, validation, and testing procedures. The discussion of findings chapter presents the results and outcomes of the empirical study conducted to evaluate the effectiveness of AI techniques in production optimization. It provides a detailed analysis of the data collected, model performance metrics, and the impact of AI solutions on production processes. The chapter discusses key findings, challenges encountered, and potential implications for industrial applications. In conclusion, this thesis summarizes the key findings, implications, and contributions to the field of industrial and production engineering. The research demonstrates the potential of AI techniques in optimizing production processes, improving efficiency, and enhancing decision-making capabilities in manufacturing settings. The study highlights the importance of adopting advanced technologies to stay competitive in the rapidly evolving industrial landscape. Overall, this research contributes valuable insights into the application of AI techniques for production optimization and provides a foundation for further research and development in this area. By leveraging the power of AI, manufacturing companies can achieve greater operational efficiency, reduce costs, and drive innovation in their production processes.