ISSN (Print): 3079-4722 ISSN (Online): 3079-4722
International Journal of Unique and New Updates, Hong Kong, ISSN: 3079-4722 Official Publication of Octopus Publication, Hong Kong
Cover of January-June 2024
research article

AI-Driven Optimization of Mechanical Components for Enhanced Performance

  • Dr. Yong Hua

Vol. 6 , Issue 1 (2024) · pp. 20-29

DOI: 10.64180/ijunu.612403

Abstract

The rapid advancements in artificial intelligence (AI) are revolutionizing various fields, including mechanical engineering. This paper explores the application of AI-driven optimization techniques to enhance the performance of mechanical components. Traditional optimization methods often rely on empirical testing and manual adjustments, which can be time-consuming and limited by human intuition. In contrast, AI-driven approaches leverage machine learning algorithms and data-driven models to automate and refine the optimization process.

Keywords: AI Optimization Mechanical Components Machine Learning Performance Enhancement Design Efficiency
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