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<p>This work examines the parametric optimization and the effect of plasma-spraying variables over the wear resilient and mechanical features of Al<sub>2</sub>O<sub>3</sub>-13%TiO<sub>2</sub>/8YSZ (AT13Z) coating developed on Ti-6Al-4V alloy substrate. The coatings porosity, microhardness, and wear rate are evaluated for the input conditions of plasma power (PP), primary gas flow rate (GF), and spraying distance (SD), and subsequent optimization was performed. The response surface methodology (RSM) was adopted to design the experiments and for optimization. To confirm the feasibility of statistical approach, the multi-objective bat algorithm (MOBA) and the cuckoo search algorithm (CSA) technique were also employed for further optimization. According to desirability analysis, the optimal setting was 35&#xa0;kW of PP, 93.808&#xa0;mm of SD, and 41.14 lpm of GF, which are predicted to have a porosity of 2.185%, a microhardness of 918.805 Hv, and a wear rate of 0.27x10<sup>-6</sup> mm<sup>3</sup>/Nm. MOBA suggests a PP of 33.63&#xa0;kW, SD of 108.25&#xa0;mm, and GF of 38.55 lpm as optimum condition. The CSA recommends 33.46&#xa0;kW PP, 107.54&#xa0;mm SD, and 38.89 lpm GF. MOBA speeds up convergence compared to CSA. The level of porosity dropped by 22.1%, the hardness went up by 4.24%, and the rate of wear went down by 18.5% in the confirmation experiment, all based on RSM. According to MOBA, the porosity level reduced by 35.92%, microhardness increased by 5.44%, and wear rate reduced by 29.62% than the predicted value.</p>

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Statistical Approach of Al2O3-13%TiO2/8YSZ Plasma Spray Coatings on Ti-6Al-4V alloy: Investigation, Modeling, and Optimization

  • 期刊论文

  • 发表日期：

    2026年4月3日

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  • M. Anbarasan,
  • G. Perumal,
  • S. Thirumalvalavan,
  • N. Senthilkumar

摘要

This work examines the parametric optimization and the effect of plasma-spraying variables over the wear resilient and mechanical features of Al2O3-13%TiO2/8YSZ (AT13Z) coating developed on Ti-6Al-4V alloy substrate. The coatings porosity, microhardness, and wear rate are evaluated for the input conditions of plasma power (PP), primary gas flow rate (GF), and spraying distance (SD), and subsequent optimization was performed. The response surface methodology (RSM) was adopted to design the experiments and for optimization. To confirm the feasibility of statistical approach, the multi-objective bat algorithm (MOBA) and the cuckoo search algorithm (CSA) technique were also employed for further optimization. According to desirability analysis, the optimal setting was 35 kW of PP, 93.808 mm of SD, and 41.14 lpm of GF, which are predicted to have a porosity of 2.185%, a microhardness of 918.805 Hv, and a wear rate of 0.27x10-6 mm3/Nm. MOBA suggests a PP of 33.63 kW, SD of 108.25 mm, and GF of 38.55 lpm as optimum condition. The CSA recommends 33.46 kW PP, 107.54 mm SD, and 38.89 lpm GF. MOBA speeds up convergence compared to CSA. The level of porosity dropped by 22.1%, the hardness went up by 4.24%, and the rate of wear went down by 18.5% in the confirmation experiment, all based on RSM. According to MOBA, the porosity level reduced by 35.92%, microhardness increased by 5.44%, and wear rate reduced by 29.62% than the predicted value.