Integration of TiC-Reinforced Aluminum Matrix Composites with CNN-Based Systems for Automotive Steering Knuckle Applications
Abstract
This study delivers a 35% reduction in steering knuckle failures by an innovative integration between advanced materials and AI-enhanced quality control. We prove that 6061+10%Si+4%TiC aluminum matrix composites behave most optimally with 138 MPa fatigue strength (25% improvement), 86 GPa elastic modulus, and 9.2/10 rating overall while still retaining manufacturability. Our neural network built upon convolutional principles posts 87% defect detection accuracy-the best compared to classical systems (SVM: 75%; RF: 70%)-and it performs inference within 28 milliseconds, allowing for production-line deployment. The amalgamation sees warranty claims go down by 28%, while costs increase by less than 5%, thus setting a new paradigm in the world of safety-critical automotive components. This work is bridging materials science with Industry 4.0 by supplying a validated framework for lightweight and high-reliability automotive design.
About this article
Keywords
- Aluminum matrix composites, Titanium carbide reinforcement, Automated quality control, Deep learning in manufacturing, Lightweight automotive compo-nents, Industry 4.0
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