The integration of artificial intelligence into the development and production of mechatronic products offers a substantial opportunity to enhance efficiency, adaptability, and system performance. This paper examines ...The integration of artificial intelligence into the development and production of mechatronic products offers a substantial opportunity to enhance efficiency, adaptability, and system performance. This paper examines the utilization of reinforcement learning as a control strategy, with a particular focus on its deployment in pivotal stages of the product development lifecycle, specifically between system architecture and system integration and verification. A controller based on reinforcement learning was developed and evaluated in comparison to traditional proportional-integral controllers in dynamic and fault-prone environments. The results illustrate the superior adaptability, stability, and optimization potential of the reinforcement learning approach, particularly in addressing dynamic disturbances and ensuring robust performance. The study illustrates how reinforcement learning can facilitate the transition from conceptual design to implementation by automating optimization processes, enabling interface automation, and enhancing system-level testing. Based on the aforementioned findings, this paper presents future directions for research, which include the integration of domain-specific knowledge into the reinforcement learning process and the validation of this process in real-world environments. The results underscore the potential of artificial intelligence-driven methodologies to revolutionize the design and deployment of intelligent mechatronic systems.展开更多
Mechatronic product development is a complex and multidisciplinary field that encompasses various domains, including, among others, mechanical engineering, electrical engineering, control theory and software engineeri...Mechatronic product development is a complex and multidisciplinary field that encompasses various domains, including, among others, mechanical engineering, electrical engineering, control theory and software engineering. The integration of artificial intelligence technologies is revolutionizing this domain, offering opportunities to enhance design processes, optimize performance, and leverage vast amounts of knowledge. However, human expertise remains essential in contextualizing information, considering trade-offs, and ensuring ethical and societal implications are taken into account. This paper therefore explores the existing literature regarding the application of artificial intelligence as a comprehensive database, decision support system, and modeling tool in mechatronic product development. It analyzes the benefits of artificial intelligence in enabling domain linking, replacing human expert knowledge, improving prediction quality, and enhancing intelligent control systems. For this purpose, a consideration of the V-cycle takes place, a standard in mechatronic product development. Along this, an initial assessment of the AI potential is shown and important categories of AI support are formed. This is followed by an examination of the literature with regard to these aspects. As a result, the integration of artificial intelligence in mechatronic product development opens new possibilities and transforms the way innovative mechatronic systems are conceived, designed, and deployed. However, the approaches are only taking place selectively, and a holistic view of the development processes and the potential for robust and context-sensitive artificial intelligence along them is still needed.展开更多
文摘The integration of artificial intelligence into the development and production of mechatronic products offers a substantial opportunity to enhance efficiency, adaptability, and system performance. This paper examines the utilization of reinforcement learning as a control strategy, with a particular focus on its deployment in pivotal stages of the product development lifecycle, specifically between system architecture and system integration and verification. A controller based on reinforcement learning was developed and evaluated in comparison to traditional proportional-integral controllers in dynamic and fault-prone environments. The results illustrate the superior adaptability, stability, and optimization potential of the reinforcement learning approach, particularly in addressing dynamic disturbances and ensuring robust performance. The study illustrates how reinforcement learning can facilitate the transition from conceptual design to implementation by automating optimization processes, enabling interface automation, and enhancing system-level testing. Based on the aforementioned findings, this paper presents future directions for research, which include the integration of domain-specific knowledge into the reinforcement learning process and the validation of this process in real-world environments. The results underscore the potential of artificial intelligence-driven methodologies to revolutionize the design and deployment of intelligent mechatronic systems.
文摘Mechatronic product development is a complex and multidisciplinary field that encompasses various domains, including, among others, mechanical engineering, electrical engineering, control theory and software engineering. The integration of artificial intelligence technologies is revolutionizing this domain, offering opportunities to enhance design processes, optimize performance, and leverage vast amounts of knowledge. However, human expertise remains essential in contextualizing information, considering trade-offs, and ensuring ethical and societal implications are taken into account. This paper therefore explores the existing literature regarding the application of artificial intelligence as a comprehensive database, decision support system, and modeling tool in mechatronic product development. It analyzes the benefits of artificial intelligence in enabling domain linking, replacing human expert knowledge, improving prediction quality, and enhancing intelligent control systems. For this purpose, a consideration of the V-cycle takes place, a standard in mechatronic product development. Along this, an initial assessment of the AI potential is shown and important categories of AI support are formed. This is followed by an examination of the literature with regard to these aspects. As a result, the integration of artificial intelligence in mechatronic product development opens new possibilities and transforms the way innovative mechatronic systems are conceived, designed, and deployed. However, the approaches are only taking place selectively, and a holistic view of the development processes and the potential for robust and context-sensitive artificial intelligence along them is still needed.
