GÜLTEKİN BASMACI, İSMAİL KIRBAŞ, MUSTAFA AY, Musa PEKER

Karma Taguchi ve yüzey yanıt yöntemi kullanılarak astm b574 (hastelloy c-22)'in tornalanması esnasındaki işleme parametrelerinin yüzey pürüzlülüğüne etkisinin incelenmesi ve kesme parametrelerinin optimizasyonu

Bu çalışmada ASTM B574 (Hastelloy C-22) malzemesi üzerinde tornalama işlemi sonrasında kesme parametrelerinin (kesme derinliği, kesme hızı, radius, ilerleme hızı, debi, talaş açısı, yanaşma açısı) yüzey pürüzlüğü ve sıcaklık üzerindeki etkileri incelenmiştir. Kesme parametrelerini etkileyen faktörleri belirlemek için varyans analizi (ANOVA) uygulanırken, yüzey pürüzlülüğünü etkileyen parametrelerin optimizasyonu Taguchi ortogonal deney tasarımına dayanan Tepki Yüzeyi Metodolojisi (RSM) ile elde edilmiştir. Geliştirilen modellerin yüzey pürüzlülüğü ve sıcaklık tahmini için gerekli olan doğruluğu oldukça başarılıdır. Ölçüt olarak ?? değerinin kullanıldığı çalışmada ortalama Ra yüzey pürüzlüğü için %93.85, doğruluk değeri elde edilmiştir. ANOVA analizleri sonucunda %95 güven aralığında, Ra için en etkili parametreler sırasıyla kesme hızı, yanaşma açısı, talaş açısı ve debi olarak tespit edilmiş ve en düşük yüzey pürüzlüğü oranı için en uygun kesme parametre değerleri belirlenmiştir.

Optimisation and influence of cutting parameters on surface roughness during turning of astm b574 (hastelloy c-22) using a hybrid of Taguchi and rsm methods

In this study, the impact of cutting parameters (depth-of-cut, cutting speed, feed, flow, rake angle, lead angle) on surface roughness in the turning process with regard to ASTM B574 (Hastelloy C-22) material has been investigated. Variance analysis (ANOVA) has been applied in order to determine the factors affecting the surface roughness. The optimization of the parameters affecting the surface roughness has been obtained using Response Surface Methodology (RSM) based on the Taguchi orthogonal experimental design. The accuracy required for surface roughness and temperature estimation of the developed models is quite successful. As a criterion, when the value of R2 is used, 93.85% accuracy value is obtained for the average Ra surface roughness. As a result of ANOVA analysis, the most effective parameters for Ra were determined as cutting speed, lead angle, rake angle and flow rate, respectively, at 95% confidence interval and optimal cut parameter values were determined for the lowest surface roughness ratio.

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