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<p>This study examines the corrosion and the tribological performance of a thin-walled structure fabricated using Rotational Arc Dual Wire (RADW) Wire Arc Additive Manufacturing (WAAM) with interlayer hammering. ER70S-6 low-alloy steel was used as the primary wire and Nichrome as the secondary wire for the build. Microstructural characterization was done by using Optical microscopy, Scanning electron microscopy and with Electron backscatter diffraction techniques. The hardness of the build was measure throughout the height of the build. Corrosion studies on the build were done by using potentiodynamic polarisation and Electrochemical impedance spectroscopy. A detailed tribological study was conducted by varying load, sliding velocity, and sliding distance, and the wear rate was optimized using two approaches: Exhaustive Search and Genetic Algorithm (GA)-based optimization. Additionally, a machine learning model based on Multiple Linear Regression (MLR) with interaction terms was employed to predict wear behaviour. The addition of nichrome and interlayer hammering was found to modify the corrosion response and wear behaviour of the RADW-WAAM builds, while the optimization techniques and the MLR model helped in identifying and predicting the tribological behaviour for a given set of parameters.</p>

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Synergistic effects of nichrome inoculation and interlayer hammering on the tribological and corrosion performance of rotational arc waam steel

  • 期刊论文

  • 发表日期：

    2026年5月5日

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  • C. T. Justus Panicker,
  • R. Shishir,
  • Prasanna Nagasai Bellamkonda,
  • N. K. Sreejith,
  • Maheshwar Dwivedy,
  • Vinayak Kalluri

摘要

This study examines the corrosion and the tribological performance of a thin-walled structure fabricated using Rotational Arc Dual Wire (RADW) Wire Arc Additive Manufacturing (WAAM) with interlayer hammering. ER70S-6 low-alloy steel was used as the primary wire and Nichrome as the secondary wire for the build. Microstructural characterization was done by using Optical microscopy, Scanning electron microscopy and with Electron backscatter diffraction techniques. The hardness of the build was measure throughout the height of the build. Corrosion studies on the build were done by using potentiodynamic polarisation and Electrochemical impedance spectroscopy. A detailed tribological study was conducted by varying load, sliding velocity, and sliding distance, and the wear rate was optimized using two approaches: Exhaustive Search and Genetic Algorithm (GA)-based optimization. Additionally, a machine learning model based on Multiple Linear Regression (MLR) with interaction terms was employed to predict wear behaviour. The addition of nichrome and interlayer hammering was found to modify the corrosion response and wear behaviour of the RADW-WAAM builds, while the optimization techniques and the MLR model helped in identifying and predicting the tribological behaviour for a given set of parameters.