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<p>This study investigates the optimization of process parameters for fabricating Inconel 625 components using wire arc additive manufacturing (WAAM), focusing to enhance microstructural characterization and mechanical performance. A Taguchi-based statistical design was employed to investigate the effects of wire feed speed, travel speed, and arc voltage on deposition efficiency, microstructural evolution, and hardness distribution across the deposited layers. Field emission scanning electron microscopy (FESEM) revealed a combination of dendritic, equiaxed, and cellular structures.&#xa0;Refined grains&#xa0;were observed in the bottom region due to rapid cooling, while coarser grains&#xa0;formed in the top region as a result&#xa0;of thermal accumulation. Hardness analysis demonstrated that optimized process parameters significantly enhance mechanical properties. The maximum hardness measured in the bottom region is 258 HV, due to grain refinement and faster cooling rates. An artificial neural network (ANN) model was developed to predict optimal parameter combinations and&#xa0;achieved a high prediction&#xa0;accuracy of 95.6%. This study presents an integrated approach combining experimental analysis, statistical optimization, and machine learning-based prediction to enhance the efficiency, structural integrity, and mechanical performance of WAAM-fabricated Inconel 625 component. These findings advance the development of&#xa0;WAAM as a sustainable and cost-effective manufacturing&#xa0;alternative for aerospace, marine, and&#xa0;other high-performance industrial sectors.</p>

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Investigation of Process Parameters of Inconel 625 Fabricated Parts by Wire Arc Additive Manufacturing

  • 期刊论文

  • 发表日期：

    2025年6月3日

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  • Suresh Gain,
  • Dhinakaran Veeman,
  • Murugan Vellaisamy

摘要

This study investigates the optimization of process parameters for fabricating Inconel 625 components using wire arc additive manufacturing (WAAM), focusing to enhance microstructural characterization and mechanical performance. A Taguchi-based statistical design was employed to investigate the effects of wire feed speed, travel speed, and arc voltage on deposition efficiency, microstructural evolution, and hardness distribution across the deposited layers. Field emission scanning electron microscopy (FESEM) revealed a combination of dendritic, equiaxed, and cellular structures. Refined grains were observed in the bottom region due to rapid cooling, while coarser grains formed in the top region as a result of thermal accumulation. Hardness analysis demonstrated that optimized process parameters significantly enhance mechanical properties. The maximum hardness measured in the bottom region is 258 HV, due to grain refinement and faster cooling rates. An artificial neural network (ANN) model was developed to predict optimal parameter combinations and achieved a high prediction accuracy of 95.6%. This study presents an integrated approach combining experimental analysis, statistical optimization, and machine learning-based prediction to enhance the efficiency, structural integrity, and mechanical performance of WAAM-fabricated Inconel 625 component. These findings advance the development of WAAM as a sustainable and cost-effective manufacturing alternative for aerospace, marine, and other high-performance industrial sectors.