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In this study, AISI 1040 steel is machined on CNC lathes. Taguchi L16 ortogonal array was used as experimental design. Experiments were carried out with selected the three cutting parameters. These parameters were determined as feed rate, cutting speed and cutting depth. Turning operation was carried out in dry conditions with diamond cutting tools. At the end of experiments, the values of surface roughness (Rz) on samples were found. Signal/Noise (S/N) rates were found with using the Taguchi method. According to the results, feed rate had the most significant effect on Rz among three factors. In ANOVA analysis, respectively feed rate, cutting depth and cutting speed are effective at 95% confidence level at Rz value. In repetition experiments carried out for parameters chosen in Taguchi prediction, it was identified that Taguchi works with nearly 94% accuracy.

Bu çalışmada, 46 HRc sertlikteki AISI 1040 çeliği tornalama işlemine tabi tutulmuştur. Kesme hızı, ilerleme ve talaş derinliği parametrelerine göre Taguchi L9 deney tasarımı ile deney listesi oluşturulmuştur. Tornalama deneyleri CNC tornada, KNUX 160405 R11 TT8125 TiCN-Al2O3-TiN kaplamalı karbür uç ile kuru kesme şartlarında gerçekleştirilmiştir. Deneyler sonucunda ortalama yüzey pürüzlülüğü (Ra) değerleri ölçülmüştür. Taguchi tasarımında sinyal-gürültü oranı tespit edilmiş ve deneysel sonuçlara göre, üç faktör arasında Ra'ya en önemli etkiyi ilerlemenin yaptığı elde edilmiştir. ANOVA analizinde ilerlemenin Ra değerinde %95 güven düzeyinde etki ettiği bulunmuştur. Taguchi tahmininde seçilen parametreler için yapılan tekrar deneyinde Taguchi'nin %89 güvenilirlik ile sonuç verdiği tespit edilmiştir.

In this study, Ti 6Al-4 V (grade 5) ELI alloy was machined with minimum energy and optimum surface quality and minimum tool wear. The appropriate cutting tool and suitable cutting parameters have been selected. As a result of the turning process, average surface roughness (Ra), tool wear and energy consumption were measured. The results have been analyzed by normality test, linear regression model, Taguchi analysis, ANOVA, Pareto graphics and multiple optimization method. It has been observed that high tool wear value increases Ra and energy consumption. In multiple optimization, it was concluded that it made predictions with 89,1% accuracy for Ra, 58,33% for tool wear, 96,75% for energy consumption. While the feed rate was the effective parameter for Ra and energy consumption, the effective parameter in tool wear was the cutting speed. Our study has revealed that by controlling energy consumption, surface quality can be maintained and tool wear can be controlled.

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