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<p>Structural, mechanical and machining properties of the as-cast and heat-treated Al-5Si-1Cu-Mg alloy produced by the permanent mold casting were experimentally investigated. Uncoated Ø8 mm high-speed steel drills were used in the drilling tests. Drilling parameters were selected as five different cutting speed (<i>V</i>-m/min), feed rate (<i>f</i>-mm/rev) and constant depth of cut (DoC). The thrust force (<i>Fz</i>) and torque (<i>Mz</i>) parameters resulting from the drilling were evaluated with statistical analyses depending on the <i>V</i> and <i>f</i> variables. In this context, experimental results were analyzed using the CRITIC-WASPAS-COCOSO and Artificial Neural Networks (ANN) methods. Microstructure analyses revealed that as-cast alloy contained α-Al matrix, eutectic Si, acicular β-Fe (β-Al<sub>5</sub>FeSi) and script-like π-Fe(π-Al<sub>8</sub>Mg<sub>3</sub>FeSi<sub>6</sub>) intermetallic phases. In the heat-treated alloy, it was determined that the β phase, apart from these phases, transformed into the Ɵ(Al<sub>7</sub>FeCu<sub>2</sub>) phase under the effect of heat treatment. With CRITIC-WASPAS-COCOSO analysis, optimum drilling parameters were determined as 125&#xa0;m/min and 0.05&#xa0;mm/rev for <i>V</i> and <i>f</i>, respectively. Appropriate network structures were created with the ANN. The results obtained revealed that both CRITIC-WASPAS-COCOSO and ANN method can successfully predict the experimental data.</p>

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Prediction and Optimization of Drilling Outputs in C355 Alloy with ANN Integrated CRITIC-WASPAS-COCOSO Method

  • 期刊论文

  • 发表日期：

    2026年7月7日

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  • Cem Alparslan,
  • Çiğdem Özhabiboğlu,
  • Kavimani Vijayananth,
  • Şenol Bayraktar

摘要

Structural, mechanical and machining properties of the as-cast and heat-treated Al-5Si-1Cu-Mg alloy produced by the permanent mold casting were experimentally investigated. Uncoated Ø8 mm high-speed steel drills were used in the drilling tests. Drilling parameters were selected as five different cutting speed (V-m/min), feed rate (f-mm/rev) and constant depth of cut (DoC). The thrust force (Fz) and torque (Mz) parameters resulting from the drilling were evaluated with statistical analyses depending on the V and f variables. In this context, experimental results were analyzed using the CRITIC-WASPAS-COCOSO and Artificial Neural Networks (ANN) methods. Microstructure analyses revealed that as-cast alloy contained α-Al matrix, eutectic Si, acicular β-Fe (β-Al5FeSi) and script-like π-Fe(π-Al8Mg3FeSi6) intermetallic phases. In the heat-treated alloy, it was determined that the β phase, apart from these phases, transformed into the Ɵ(Al7FeCu2) phase under the effect of heat treatment. With CRITIC-WASPAS-COCOSO analysis, optimum drilling parameters were determined as 125 m/min and 0.05 mm/rev for V and f, respectively. Appropriate network structures were created with the ANN. The results obtained revealed that both CRITIC-WASPAS-COCOSO and ANN method can successfully predict the experimental data.