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<p>Dry turning of AISI D2 steel requires a balance between productivity, surface integrity, thermal loading, and energy demand. This study compares the machinability of a chamfered Xcel insert and a conventional carbide insert during dry turning of AISI D2 steel using a 2<sup>4</sup> full-factorial design with cutting speed (V<sub>CS</sub>: 100–150&#xa0;m/min), feed rate (F<sub>R</sub>:&#xa0;0.2–0.3&#xa0;mm/rev), and depth of cut (D<sub>OC</sub>:&#xa0;0.5–1.0&#xa0;mm). Volumetric material removal rate (VMR), microhardness (MH), turning-zone temperature (TTZ), and power consumption (PC) were measured and analysed using ANOVA, ANN modelling, and NSGA-II optimization. The results showed that the Xcel insert consistently outperformed the conventional carbide insert within the investigated range. The highest measured VMR and MH were 1341.57&#xa0;cm<sup>3</sup> and 177.78 HV, respectively, whereas the lowest TTZ and PC were 272&#xa0;°C and 420.28 W under conservative cutting conditions. ANOVA showed that F<sub>R</sub> was the dominant factor for VMR (27.69%), while V<sub>CS</sub> dominated MH (74.51%) and TTZ (49.36%); D<sub>OC</sub> was the strongest contributor to PC (29.55%). Artificial neural network (ANN) models were used as local-response surrogates, and Non-dominated sorting genetic algorithm (NSGA-II) identified an optimum at 149.84&#xa0;m/min, 0.3&#xa0;mm/rev, 0.997&#xa0;mm, and Xcel insert. The study shows that the Xcel geometry is beneficial for increasing productivity in dry turning of AISI D2 without a disproportionate increase in thermal and energy penalties.</p>

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Comparative machinability and neural network-driven optimization of AISI D2 steel using chamfered and conventional tooling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月14日

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  • Muhammad Sana,
  • Muhammad Umar Farooq,
  • Saqib Anwar

摘要

Dry turning of AISI D2 steel requires a balance between productivity, surface integrity, thermal loading, and energy demand. This study compares the machinability of a chamfered Xcel insert and a conventional carbide insert during dry turning of AISI D2 steel using a 24 full-factorial design with cutting speed (VCS: 100–150 m/min), feed rate (FR: 0.2–0.3 mm/rev), and depth of cut (DOC: 0.5–1.0 mm). Volumetric material removal rate (VMR), microhardness (MH), turning-zone temperature (TTZ), and power consumption (PC) were measured and analysed using ANOVA, ANN modelling, and NSGA-II optimization. The results showed that the Xcel insert consistently outperformed the conventional carbide insert within the investigated range. The highest measured VMR and MH were 1341.57 cm3 and 177.78 HV, respectively, whereas the lowest TTZ and PC were 272 °C and 420.28 W under conservative cutting conditions. ANOVA showed that FR was the dominant factor for VMR (27.69%), while VCS dominated MH (74.51%) and TTZ (49.36%); DOC was the strongest contributor to PC (29.55%). Artificial neural network (ANN) models were used as local-response surrogates, and Non-dominated sorting genetic algorithm (NSGA-II) identified an optimum at 149.84 m/min, 0.3 mm/rev, 0.997 mm, and Xcel insert. The study shows that the Xcel geometry is beneficial for increasing productivity in dry turning of AISI D2 without a disproportionate increase in thermal and energy penalties.