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<p>In this work, the tribological, mechanical, corrosion, and sliding wear behavior of cold metal transfer-wire arc additive manufactured (CMT-WAAM) Al4043 alloy is reported and optimized using the combined use of response surface methodology (RSM) and machine learning (ML). According to microstructural observations, the grain size at the bottom was 67&#xa0;μm, finer than that at the top (93&#xa0;μm) due to the higher cooling rate, which led to corresponding tensile strength and elongation. This fine-grained structure in the bottom region also contributed to higher hardness values (70.62 HV) and better wear resistance, as supported by the hardness profile and wear testing. Hardness was lower in the top quarter, which had larger grains. Tensile testing demonstrated that the 0° direction exhibits the highest UTS and elongation due to the fine microstructure developed at an optimal cooling rate. Corrosion test. The corrosion rate of the top part was the lowest (0.098&#xa0;mm/year) and showed the greatest potential for corrosion protection (Ecorr: − 751.23&#xa0;mV), indicating a stable passive oxide film. Optimum conditions (33.3 N load, 362 RPM speed, and 50&#xa0;mm wear track radius) derived by RSM optimization minimized the specific wear rate (SWR) and coefficient of friction (COF), thereby improving wear response. The ML model of XGBoost correctly predicted SWR and COF with an excellent fit in terms of R<sup>2</sup> value (0.992 and 0.998, respectively), verifying the validity of the model. This work systematically enlightens the optimization of tribological, mechanical, and corrosion behaviors of WAAM Al4043 alloys, shedding light on their applications in engineering.</p> Graphical Abstract <p></p>

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Assessment of the Microstructural, Mechanical, Corrosion, and Tribological Characteristics of Cold Metal Transfer-Wire Arc Additive Manufactured Al4043 via Response Surface Methodology and XGBoost Machine Learning

  • 期刊论文

  • 发表日期：

    2026年4月24日

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  • Manikandan Nagarajan,
  • Chakravarthi Gurijala,
  • V. S. Shaisundaram,
  • Saravanakumar Sengottaiyan

摘要

In this work, the tribological, mechanical, corrosion, and sliding wear behavior of cold metal transfer-wire arc additive manufactured (CMT-WAAM) Al4043 alloy is reported and optimized using the combined use of response surface methodology (RSM) and machine learning (ML). According to microstructural observations, the grain size at the bottom was 67 μm, finer than that at the top (93 μm) due to the higher cooling rate, which led to corresponding tensile strength and elongation. This fine-grained structure in the bottom region also contributed to higher hardness values (70.62 HV) and better wear resistance, as supported by the hardness profile and wear testing. Hardness was lower in the top quarter, which had larger grains. Tensile testing demonstrated that the 0° direction exhibits the highest UTS and elongation due to the fine microstructure developed at an optimal cooling rate. Corrosion test. The corrosion rate of the top part was the lowest (0.098 mm/year) and showed the greatest potential for corrosion protection (Ecorr: − 751.23 mV), indicating a stable passive oxide film. Optimum conditions (33.3 N load, 362 RPM speed, and 50 mm wear track radius) derived by RSM optimization minimized the specific wear rate (SWR) and coefficient of friction (COF), thereby improving wear response. The ML model of XGBoost correctly predicted SWR and COF with an excellent fit in terms of R2 value (0.992 and 0.998, respectively), verifying the validity of the model. This work systematically enlightens the optimization of tribological, mechanical, and corrosion behaviors of WAAM Al4043 alloys, shedding light on their applications in engineering.

Graphical Abstract