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Artificial neural network-driven optimization and mechanical energy evaluation of silica nanoparticle-enhanced
1Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
Abstract:
This study develops lightweight Kevlar-polypropylene hybrid composites for prosthetic socket applications using experimental and machine-learning approaches. Laminates containing 0-2 wt.% silica nanoparticles were fabricated and evaluated for mechanical and puncture-energy performance. The 1.5 wt.% silica composite showed the best overall response, reaching a puncture load of 3459.1 N, tensile strength of 225 MPa, flexural strength of 157.24 MPa, interlaminar shear strength of 34 MPa, impact strength of 1813.6 J/m, and net energy absorption of 18.44 J. An artificial neural network achieved R² = 0.983, confirming reliable predictive capability and supporting the composite's suitability for advanced prosthetic applications.
