International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025
p-ISSN: 2395-0072
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A Review of AI Approaches for Enhancing Mechanical Materials Performance 1P.H.J. Venkatesh, 2Padmakar Maddala, 3Saikumar Amda 1Assistant Professor, Dept.of Mechanical Engineering, Vignan’s Institute of Information Technology, beside VSEZ,
Duvvada, Visakhapatnam, A.P, India
2Assistant Professor, Dept.of Civil Engineering, Vignan’s Institute of Information Technology, beside VSEZ,
Duvvada, Visakhapatnam, A.P, India
3Assistant Professor Department of Mechanical Engineering, JB Institute of Engineering and Technology (A),
Hyderabad, Telangana, India JB Institute of Engineering and Technology (A) Hyderabad, Telangana state. India. -------------------------------------------------------------------------***------------------------------------------------------------------------
Abstract: The Artificial Intelligence is revolutionizing
very important role in improving performance and applications, holding potential to revolutionize the technologies of energy harvesting [2]. Another AI application is in revolutionizing the design of supra molecular materials; this shows how it can spur innovation in a field ranging from materials chemistry to engineering [3]. The influence of AI goes further in the prediction and optimization of mechanical properties in composite materials. For instance, interpretable deep learning models have been applied to explore the effects of particulate reinforcements in metal matrix composites [4], while AI-enhanced models for fault prediction have been used in improving industrial processes, mainly when dealing with incomplete data [5]. Lastly, the contribution of AI in heat transfer enhancement and optimization techniques is an area that keeps on progressing and has really contributed much toward efficiency and effectiveness in thermal management systems [6]. With the development of automation and robotics, the AI-assisted method is increasingly applied in the improvement of machining process, especially in lightweight material fabrication [7]. In this regard, one of the most promising areas is the design of architectured materials, where hybrid approaches using AI are adopted to create materials with improved stiffness and strength, therefore showing the potential of AI in datadriven design [8] [16]. Furthermore, the application of AI in optimizing performance of polymeric meta-hybrid structures is a typical example of its usefulness in optimizing auxetic behavior for advanced engineering applications [9] [26]. Application of AI is also gaining huge momentum in additive manufacturing, where hybrid statistical approaches are used to optimize the processes to enhance product quality and raise the efficiency of production [10]. The ability of Quantam AI applications is further extending this capability of materials design, ushering in new waves of innovations by integrating quantum computing and artificial intelligence [11]. The AI-based sensitivity analysis methods in material selection will provide improved decision processes for the composite materials design in which AI has started playing a central role in multi-
the materials science arena, enabling the discovery, design, and optimization of innovative mechanical and bioinspired materials. This review consolidates insights from recent developments in AI-enabled methods to improve properties such as stiffness, strength, thermal performance, and durability of materials. This work focuses on diverse applications, including nanogenerator, Graphene-based electrodes, composites, metamaterials, and polymers, with an emphasis on how generative AI, deep learning, and machine learning approaches optimize material configurations to enhance prediction accuracy and streamline manufacturing processes. It further demonstrates the ability of AI in reducing redundancies, minimizing defects, and improving material quality by integrating into additive manufacturing, metamaterials design, and electrochemical energy storage. The results underline the importance of AI in pushing innovations within these fields: sustainable polymers, advanced ceramics, and precision manufacturing rendering transformative solutions for challenges in materials engineering. Keywords: Artificial Intelligence, Mechanical Materials Performance, Generative AI, Nano generators, Graphenebased, Electrodes, Additive Manufacturing, Metamaterials Design, Machine Learning; Electrochemical Energy Storage, Sustainable Polymers, Advanced Ceramics, Precision Manufacturing, Fault Prediction, Thermal Performance Optimization. 1.INTRODUCTION In recent years, materials science has experienced certain remarkable developments based on increasing integration with artificial intelligence techniques. In material science, AI has shown to be an essential tool for the enhancement of mechanical and bioinspired materials; this is clearly evidenced through the recent studies of generation of new materials and optimization of the properties of materials through AI approaches [1]. In the domain of nanogenerator, AI has also played a
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