Design and Development of a Conventional Mechatronic System with Deep Learning–Based Industrial Applications

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. The proposed system combines mechanical components, electronic hardware, sensors, actuators, and embedded control units with advanced deep learning models to achieve intelligent decision-making and improved operational efficiency. Deep learning techniques are employed to analyze complex, high-dimensional sensor data for tasks such as system state estimation, performance optimization, fault detection, and predictive analysis in industrial environments. The developed framework supports adaptive learning capabilities, allowing the system to respond dynamically to changing operational conditions. 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.