International Journal of Science, Engineering and TechnologyAuthors: Mr. Rahul Khobragade, Mr. Rahul Ghotkar, Ms. Amisha Malviya
Abstract: Mechanical engineering design has evolved significantly over the past decade due to the rapid development of Artificial Intelligence (AI) technologies. Traditional engineering design methods primarily depend on analytical calculations, empirical knowledge, numerical simulations, and repeated trial-and-error processes, which often require considerable time, computational effort, and engineering expertise. As engineering systems become increasingly complex and performance requirements continue to rise, conventional optimization techniques face limitations in handling multi-objective design problems, nonlinear constraints, and large-scale design spaces. Artificial Intelligence has emerged as a transformative technology that enables intelligent, adaptive, and data-driven design optimization capable of improving product performance, reducing development time, minimizing manufacturing costs, and enhancing sustainability. Consequently, AI-based optimization has become an essential component of modern mechanical engineering design. Recent advances in Artificial Intelligence have introduced numerous intelligent algorithms capable of solving complex engineering optimization problems more efficiently than traditional approaches. Technologies such as Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANN), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Reinforcement Learning (RL), Fuzzy Logic, Evolutionary Algorithms (EA), and Digital Twin technology are increasingly integrated with Computer-Aided Design (CAD), Computer-Aided Engineering (CAE), and Finite Element Analysis (FEA) to create intelligent engineering design environments. These technologies enable engineers to explore a large number of design alternatives automatically while simultaneously satisfying performance, reliability, cost, manufacturability, and environmental constraints. Artificial Intelligence significantly enhances the engineering design process by enabling intelligent prediction, optimization, automation, and decision support. Machine Learning models learn from historical engineering data to predict product behaviour, structural performance, material properties, manufacturing outcomes, and system reliability. Deep Learning techniques process complex multidimensional engineering datasets, allowing rapid evaluation of design alternatives without repeatedly performing computationally expensive simulations. Artificial Neural Networks provide accurate approximations of engineering responses, reducing computational time during optimization while maintaining high prediction accuracy.
