Enhancing mechanical and bioinspired materials through generative AI approaches

Abstract

The integration of generative artificial intelligence (AI) into the design and additive manufacturing processes of mechanical and bioinspired materials has emerged as a transformative approach in engineering and material science, allowing to explore relationships across different field (e.g., mechanics-biology) or disparate domains (e.g., failure mechanics-3D printing). In addition, generative AI techniques, including generative adversarial networks (GAN), genetic algorithms, and large language models (LLMs), offer efficient and tunable solutions for optimizing material properties, reducing production costs, and accelerating the development timelines.In the field of mechanical materials design, generative AI enables the rapid generation of novel structures with enhanced mechanical performance. Instead, bioinspired materials design benefits significantly from the synergy of generative AI with bioinspired concepts and additive manufacturing. By harnessing generative algorithms and topology optimization, researchers can explore complex biological phenomena and translate them into innovative engineering solutions. Lastly, the emergence of LLMs in additive manufacturing optimization demonstrates their potential to optimize printing parameters, debug errors, and enhance productivity.This review highlights the pivotal role of generative AI in advancing materials science and engineering, unlocking new possibilities for innovation, and accelerating the development of efficient material solutions. As generative AI continues to evolve, its integration promises to revolutionize engineering design and drive the field towards unprecedented levels of efficiency, thus turns information into knowledge.