Thesis Abstract

Abstract
This thesis explores the application of machine learning algorithms to optimize production processes in manufacturing settings. The escalating demands for efficiency and productivity in industrial and production engineering have necessitated the adoption of advanced technologies to streamline operations. Machine learning, a subset of artificial intelligence, offers a range of algorithms that can be trained to analyze complex data patterns and make informed decisions to enhance production processes. This research investigates the effectiveness of utilizing machine learning algorithms in optimizing various facets of production within a manufacturing environment. The introduction provides an overview of the significance of this research topic in the context of industrial and production engineering. It discusses the background of the study, highlighting the current challenges faced in production processes and the potential benefits of integrating machine learning algorithms. The problem statement elucidates the gaps in existing methodologies and underscores the need for advanced optimization techniques to improve manufacturing efficiency. The objectives of the study are delineated to identify specific goals and outcomes expected from the research. These objectives serve as guiding principles for the investigation into the application of machine learning algorithms in production optimization. The limitations of the study are acknowledged to delineate the boundaries within which the research findings can be interpreted. The scope of the study outlines the specific areas of production processes that will be targeted for optimization using machine learning algorithms. The literature review delves into existing research and developments in the field of machine learning and its applications in manufacturing optimization. Ten comprehensive items are discussed, encompassing various aspects such as predictive maintenance, quality control, supply chain management, and production scheduling. This review provides a solid foundation for understanding the current state-of-the-art techniques and practices in the integration of machine learning in manufacturing processes. The research methodology section outlines the approach taken to investigate the application of machine learning algorithms in production optimization. Eight key components are elucidated, including data collection methods, algorithm selection, model training, and performance evaluation metrics. The methodology provides a structured framework for conducting experiments and analyzing the results to achieve the research objectives. The discussion of findings chapter presents a detailed analysis of the results obtained from applying machine learning algorithms to optimize production processes. Various insights and observations are discussed, highlighting the efficacy of different algorithms in improving efficiency, reducing downtime, and enhancing overall productivity. The implications of these findings on industrial and production engineering practices are also explored. In conclusion, this thesis summarizes the key findings and contributions of the research, emphasizing the significance of utilizing machine learning algorithms for optimizing production processes in manufacturing settings. The summary encapsulates the main outcomes of the study and offers recommendations for future research directions in this burgeoning field. Overall, this research underscores the transformative potential of machine learning in revolutionizing production optimization and advancing industrial and production engineering practices.

Thesis Overview