Modern sports equipment must balance performance enhancement with structural reliability, especially as designs push toward lighter, stronger materials at microscopic scales. This research addresses a critical challenge in sports engineering: predicting how advanced materials behave under intense athletic demands before physical prototypes are built. We develop an innovative approach that combines artificial intelligence with fundamental physics to model the stability of nonuniform composite structures commonly used in high-performance gear. Our method learns from real-world performance data and material behavior to forecast potential failure points in equipment like bicycle frames, tennis rackets, and running prosthetics. By analyzing how microscopic material variations affect overall stability during dynamic movements such as sprinting, jumping, or rapid direction changes, our framework provides designers with actionable insights to optimize equipment safety and performance. Testing across multiple sports applications demonstrates our model's ability to reduce development time while increasing equipment durability by anticipating structural weaknesses that traditional methods often miss. This work bridges advanced computational techniques with practical sports engineering needs, offering manufacturers a powerful tool to create equipment that enhances athlete confidence and competitive outcomes through scientifically validated design improvements.
Lin Hu — Department of Physical Education, Donghua University, Shanghai 200051, China
Di Lu — School of Physical Education and Health, Shanghai Lixin University of Accounting and Finance, Shanghai 201603, China
Mostafa Habibi — Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, India/ Department of Mechanical Engineering, Faculty of Engineering, Haliç University, Istanbul, Turkey
Wi Liu — Institute of Sciences and Design of AL-Kharj, Dubai, United Arab Emirates
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