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<p>This study presents the mechanical properties analysis and optimization of machining parameters of customized Al-Cu metal matrix composite (MMC) reinforced with silicon carbide (SiC, 5 wt.%) and graphene nanoplatelets (GNP, 3 wt.%), produced using the Wire Arc Additive Manufacturing (WAAM) technique. The microstructural characterization was done using Scanning Electron Microscopy (SEM) to evaluate the dispersion and distribution of reinforcements. In the present research work, Water Jet Machining process (WJM) is used, and the machining parameters such as MRR (material removal rate), SR (surface roughness), and KW (kerf width) are optimized using machine learning (Random Forest) and advanced optimization techniques, including Teaching–Learning-Based Optimization (TLBO) and the JAYA algorithm. The predictive accuracy of these techniques is further validated using Analysis of Variance (ANOVA), ensuring a robust comparison of machinability outputs. The results offer valuable insights into the machinability optimization and microstructural enhancement, contributing to the advancement of WAAM-fabricated MMCs for engineering applications.</p>

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Mechanical and Machining Performance of WAAM-Fabricated Al-Cu-SiC-GNP Composite Using Machine Learning and TLBO–JAYA Optimization

  • 期刊论文

  • 发表日期：

    2025年9月23日

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  • Madduri Rajkumar Reddy,
  • Santhosh Kumar Gugulothu,
  • T. Krishnaiah

摘要

This study presents the mechanical properties analysis and optimization of machining parameters of customized Al-Cu metal matrix composite (MMC) reinforced with silicon carbide (SiC, 5 wt.%) and graphene nanoplatelets (GNP, 3 wt.%), produced using the Wire Arc Additive Manufacturing (WAAM) technique. The microstructural characterization was done using Scanning Electron Microscopy (SEM) to evaluate the dispersion and distribution of reinforcements. In the present research work, Water Jet Machining process (WJM) is used, and the machining parameters such as MRR (material removal rate), SR (surface roughness), and KW (kerf width) are optimized using machine learning (Random Forest) and advanced optimization techniques, including Teaching–Learning-Based Optimization (TLBO) and the JAYA algorithm. The predictive accuracy of these techniques is further validated using Analysis of Variance (ANOVA), ensuring a robust comparison of machinability outputs. The results offer valuable insights into the machinability optimization and microstructural enhancement, contributing to the advancement of WAAM-fabricated MMCs for engineering applications.