Hybrid manufacturing technologies and their future in industrial applications

Hybrid manufacturing signifies an approach that combines traditional production methods with digital technologies, enabling more flexible, efficient, and customizable manufacturing processes. Artificial intelligence is a branch of science aiming to endow computer systems with human-like cognitive abilities. artificial intelligence focuses on simulating human-like capabilities such as learning, problem-solving, language understanding, and decision-making. Data analytics is a discipline that utilizes statistical and mathematical methods to extract meaning from large datasets and enhance business strategies. This thesis examines the role and evolution of hybrid manufacturing technologies in industrial applications, particularly addressing the impact of artificial intelligence and data analytics in these hybrid production processes. The thesis introduces the definition of hybrid manufacturing technologies and analyzes their current usage in industrial applications. It provides a fundamental explanation of artificial intelligence and data analytics concepts and discusses how these technologies can be integrated into hybrid manufacturing processes. Additionally, through a literature review, the thesis explores how artificial intelligence and data analytics optimize efficiency, predictive capabilities, and decision-making processes in manufacturing. The thesis evaluates the future potential of artificial intelligence and data analytics in industrial applications within the context of evolving hybrid manufacturing technologies. In this context, the thesis aims to understand the evolution of hybrid manufacturing technologies and provide insights into how these technologies may shape future industrial applications.