Artificial Intelligence and Machine Learning in Mechanical Engineering
Keywords:
Artificial Intelligence (AI), Machine Learning (ML), Mechanical Engineering, Design Optimization, Predictive MaintenanceAbstract
The convergence of Artificial Intelligence (AI) and Machine Learning (ML) within the domain of mechanical engineering stands as a transformative force reshaping the landscape of innovation and technological advancement. This review encapsulates the multifaceted impact, challenges, and future prospects arising from the integration of AI and ML technologies within mechanical engineering. The exploration begins by elucidating the paradigm shift in design and optimization processes, empowered by generative design and data-driven algorithms. It delves into predictive maintenance strategies, highlighting AI’s role in pre-empting machinery failures and optimizing reliability. Furthermore, it uncovers the landscape of smart manufacturing and robotics, elucidating how AI augments efficiency and agility on factory floors. The review navigates the role of AI and ML in fostering energy efficiency and sustainability, elucidating their pivotal contribution to eco-friendly practices and resource optimization. Additionally, it tackles the array of challenges encompassing data quality, interpretability, ethical dilemmas,
and societal impacts inherent in the proliferation of these technologies. Looking toward the future, the review outlines the potential advancements in algorithms, human-machine collaboration, and ethical frameworks that pave the path for a future defined by intelligent, adaptive, and responsible systems within mechanical engineering. In conclusion, the review underscores the imperative for responsible innovation, ethical considerations, and interdisciplinary collaborations toharness the full potential of AI and ML while ensuring their integration aligns with societal aspirations for progress, sustainability, and inclusivity within the realm of mechanical engineering.
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