About this Research Topic
In recent years, the integration of Artificial Intelligence (AI) has revolutionized the field of Mechatronics, creating a symbiotic relationship that enhances the capabilities of both disciplines. Mechatronics, a multidisciplinary field combining mechanical engineering, electronics, computer science, and control engineering, has found a powerful ally in AI. The infusion of AI technologies, particularly deep learning and optimization techniques, has significantly improved the design, reliability, and maintenance of mechatronic systems. This collection aims to explore the profound impact of AI on mechatronics, highlighting the synergy between these domains and showcasing innovative approaches that push the boundaries of intelligent mechatronic systems.
The primary goal of this collection is to provide a comprehensive overview of the intersection between Mechatronics and Artificial Intelligence, emphasizing advancements in design, reliability, and maintenance through the lens of deep learning and optimization methodologies. We seek to assemble a diverse range of research articles, case studies, and reviews that delve into the successful integration of AI techniques in mechatronic applications. By fostering collaboration and knowledge exchange, this collection aims to contribute to the evolving landscape of intelligent mechatronic systems, promoting a deeper understanding of the challenges and opportunities presented by the convergence of AI and Mechatronics.
We invite researchers, practitioners, and experts in the fields of Mechatronics and Artificial Intelligence to contribute to this collection. Manuscripts should focus on original research, novel applications, or comprehensive reviews that explore the utilization of AI, particularly deep learning and optimization methods, in enhancing the design, reliability, and maintenance of mechatronic systems. Topics of interest include but are not limited to autonomous systems, sensor integration, intelligent control, fault detection, reliability, maintenance, and real-time decision-making. We encourage submissions that showcase practical implementations, theoretical advancements, and interdisciplinary collaborations. Join us in advancing the frontiers of Mechatronics through the lens of Artificial Intelligence.
The primary goal of this collection is to provide a comprehensive overview of the intersection between Mechatronics and Artificial Intelligence, emphasizing advancements in design, reliability, and maintenance through the lens of deep learning and optimization methodologies. We seek to assemble a diverse range of research articles, case studies, and reviews that delve into the successful integration of AI techniques in mechatronic applications. By fostering collaboration and knowledge exchange, this collection aims to contribute to the evolving landscape of intelligent mechatronic systems, promoting a deeper understanding of the challenges and opportunities presented by the convergence of AI and Mechatronics.
We invite researchers, practitioners, and experts in the fields of Mechatronics and Artificial Intelligence to contribute to this collection. Manuscripts should focus on original research, novel applications, or comprehensive reviews that explore the utilization of AI, particularly deep learning and optimization methods, in enhancing the design, reliability, and maintenance of mechatronic systems. Topics of interest include but are not limited to autonomous systems, sensor integration, intelligent control, fault detection, reliability, maintenance, and real-time decision-making. We encourage submissions that showcase practical implementations, theoretical advancements, and interdisciplinary collaborations. Join us in advancing the frontiers of Mechatronics through the lens of Artificial Intelligence.
Keywords: Mechatronics, Artificial intelligence, Design, Reliability, Maintenance, Deep learning, Optimization
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
