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<p>The study investigates the mechanical behavior and optimization of AA7050 hybrid composites under varied machining conditions. Hardness evaluations reveal an increase up to 8% reinforcement, followed by a decline at 10%, indicating the critical influence of reinforcement dispersion on material hardness. Wear rate analyses across reinforcement percentages highlight a general decrease with increased reinforcement, attributing improved mechanical properties to reduced abrasive wear effects. Distinct process parameters, including load and sliding distance, demonstrate significant impacts on wear rates and coefficient of friction (COF), influenced by the formation of mechanical mixed layers and oxidative wear mechanisms. Surface morphology analyses illustrate temperature-dependent wear patterns, correlating higher temperatures with reduced wear rates but increased COF due to thermal degradation effects. The study employs Python-based particle swarm optimization and L-BFGS-B methods to optimize machining parameters, achieving optimal conditions (10% reinforcement, 50&#xa0;N load, 5000&#xa0;m sliding distance, 15&#xa0;m/s sliding velocity, and 100&#xa0;°C temperature).</p>

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Optimizing Wear Characteristics of AA7050-Al2O3 Composites through Python Hybridization Technique

  • 期刊论文

  • 发表日期：

    2026年3月6日

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  • A. P. Antwin Koshy,
  • M. Felix Xavier Muthu,
  • J. Jebeen Moses,
  • P. Jose Aloysius

摘要

The study investigates the mechanical behavior and optimization of AA7050 hybrid composites under varied machining conditions. Hardness evaluations reveal an increase up to 8% reinforcement, followed by a decline at 10%, indicating the critical influence of reinforcement dispersion on material hardness. Wear rate analyses across reinforcement percentages highlight a general decrease with increased reinforcement, attributing improved mechanical properties to reduced abrasive wear effects. Distinct process parameters, including load and sliding distance, demonstrate significant impacts on wear rates and coefficient of friction (COF), influenced by the formation of mechanical mixed layers and oxidative wear mechanisms. Surface morphology analyses illustrate temperature-dependent wear patterns, correlating higher temperatures with reduced wear rates but increased COF due to thermal degradation effects. The study employs Python-based particle swarm optimization and L-BFGS-B methods to optimize machining parameters, achieving optimal conditions (10% reinforcement, 50 N load, 5000 m sliding distance, 15 m/s sliding velocity, and 100 °C temperature).