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<p>Optimizing the machining parameters during the machining process is important to achieve certain desired machined surface quality. Optimizing cutting parameters to get a high surface finish that reduces mechanical failures caused by wear and corrosion in the same way increases the productivity of mechanical parts. In this research, double tool turning on AISI 1050 steel material was conducted using a ceramic-inserted cutting tool. Taguchi L16 orthogonal array has been selected for the design of the experiments, signal-to-noise, regression analysis for each response, and grey relational analysis-based optimization was used. Analysis of variance (ANOVA) was used to determine the influences of cutting parameters, and a mathematical model was done using regression analysis on surface roughness. The results of variance presented the influence of cutting speed, feed rate, first depth of cut, and second depth of cut were 8.83%, 16.92%, 28.41%, and 40.99%, respectively, on surface roughness. The minimum surface roughness attained for the optimum cutting parameter was 1.12 µm. The results revealed that the developed predictive models provide a close relationship between the predicted values and experimental values for all response values. The moderate tooltip temperatures of 72.6 °C and 70.2 °C were measured for both single and double tool turning, respectively. The material removal rate was high due to double tool turning used and a maximum of 112 mm<sup>3</sup>/s.</p>

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Experimental investigation and parametric optimization of double cutting tool turning during dry condition for AISI 1050 steel

  • 期刊论文

  • 发表日期：

    2025年9月26日

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  • Kasahun Nuguse Alemu,
  • Moera Gutu Jiru,
  • Balkeshwar Singh,
  • Yalew Dessalegn

摘要

Optimizing the machining parameters during the machining process is important to achieve certain desired machined surface quality. Optimizing cutting parameters to get a high surface finish that reduces mechanical failures caused by wear and corrosion in the same way increases the productivity of mechanical parts. In this research, double tool turning on AISI 1050 steel material was conducted using a ceramic-inserted cutting tool. Taguchi L16 orthogonal array has been selected for the design of the experiments, signal-to-noise, regression analysis for each response, and grey relational analysis-based optimization was used. Analysis of variance (ANOVA) was used to determine the influences of cutting parameters, and a mathematical model was done using regression analysis on surface roughness. The results of variance presented the influence of cutting speed, feed rate, first depth of cut, and second depth of cut were 8.83%, 16.92%, 28.41%, and 40.99%, respectively, on surface roughness. The minimum surface roughness attained for the optimum cutting parameter was 1.12 µm. The results revealed that the developed predictive models provide a close relationship between the predicted values and experimental values for all response values. The moderate tooltip temperatures of 72.6 °C and 70.2 °C were measured for both single and double tool turning, respectively. The material removal rate was high due to double tool turning used and a maximum of 112 mm3/s.