Integration of Deep Learning Techniques in Mechatronic Devices and Systems: Advancement, Challenges, and Opportunities

Abstract

The significance of incorporating deep learning methods into diverse fields has grown because of their transformative influence on resolving complex issues, improving efficiency, and unlocking new capabilities. By integrating deep learning, mechatronic devices and systems can become more intelligent, adaptive, and effective in their operations. Deep learning techniques enable these systems and devices to learn from data, identify patterns, and make decisions in real time, thereby improving their ability to adapt to changing environments and maximize performance. However, the integration of deep learning techniques into mechatronic devices and systems involves several challenges such as data scarcity, model interpretability, real-time processing needs, environmental change resistance, safety, dependability, and ethical issues. This paper reviews the integration of deep learning techniques into mechatronic devices and systems, exploring its impact on automation and control while addressing prevalent challenges and proposing potential solutions for adapting deep learning models to meet the specific requirements of mechatronic systems. Advancements in Multimodal sensor fusion, system identification, and domain adaptation are also explored to enhance model interpretability and robustness. The paper delineates the promising prospect for deep learning integration in mechatronics, emphasizing collaborative efforts among academia, industry, and regulators to ensure responsible deployment of these technologies. This paper serves as a guiding framework for researchers, engineers, and policymakers, facilitating the effective integration of deep learning methodologies in mechatronics devices and systems