A Review of Artificial Intelligence Integration in Mechanical Engineering with Emphasis on Indian Contributions and Applications

Abstract

The integration of Artificial Intelligence (AI) into mechanical engineering has marked a transformative shift from traditional analytical approaches to data-driven, intelligent methodologies. AI enables mechanical systems to become more adaptive, efficient, and autonomous by supporting applications such as predictive maintenance, design optimization, quality control, and intelligent automation. The increasing computational capabilities, access to large datasets, and advancements in machine learning algorithms have empowered engineers to solve complex mechanical problems with greater speed and accuracy. This study explores the evolution and current role of AI in mechanical engineering, with a focus on contributions from Indian researchers and institutions. The literature review spans from early rule-based expert systems in the 1980s to modern applications of deep learning, digital twins, and explainable AI in the 2020s. A detailed case study on AI-enabled predictive maintenance in an Indian manufacturing setup is presented, demonstrating practical benefits such as reduced downtime and improved operational efficiency. The paper also highlights major benefits, including improved design cycles, automation, and fault detection, alongside challenges such as data quality issues, a lack of AI expertise among mechanical engineers, and model interpretability. By synthesizing past research, present trends, and real-world applications, this study offers a comprehensive understanding of how AI is reshaping the mechanical engineering landscape, particularly within the Indian context. The findings suggest that future mechanical systems will increasingly rely on AI to achieve enhanced performance, sustainability, and innovation.