Tİ-6AL-4V ALAŞIMININ FREZELENMESİNDE YÜZEY PÜRÜZLÜLÜĞÜNÜN REGRESYON ANALİZİ İLE MODELLENMESİ

Abstract

Bu çalışmada, Ti-6Al-4V yüksek basınçlı soğutma şartlarında frezelenmiştir. Bağımsız değişken olarak kabul edilen kesme parametreleri; 4 farklı seviyedeki, kesme hızı (Vc: 50-70-90-110 m/dk), ilerleme oranı (f: 0.05-0.1-0.15-0.2 mm/diş) ve soğutma sıvısı basıncından (P: 6-100-200-300 bar) oluşmaktadır. SPSS 20 programı kullanılarak, yüzey pürüzlülüğü için kesme parametrelerine bağlı lineer, ikinci dereceden ve lineer logaritmik regresyon denklemleri elde edilmiştir. En iyi tahmin sonucunu ikinci dereceden çoklu regresyon modeli vermiştir. Modelde, yüzey pürüzlülüğündeki değişimin %95’ i bağımsız değişkenler tarafından açıklanabilmektedir. Deney verileri ve model arasındaki korelasyon 0,975 olarak hesaplanmıştır. Sonuç olarak, ikinci derece regresyon modelinin yüzey pürüzlülüğünü tahmin etmede başarılı olduğu kanıtlanmıştır. Modeller incelendiğinde, yüzey pürüzlülüğüne etki eden en önemli parametrenin, ilerleme oranı olduğu gözlenmiştir. Çalışmanın sonuçları literatürü doğrulamaktadır.

Keywords

• Ti-6Al-4V
• Yüzey Pürüzlülüğü
• Regresyon Analizi
• Frezeleme

MODELING OF SURFACE ROUGHNESS IN MILLING OF TI-6AL-4V ALLOY USING REGRESSION ANALYSIS

Abstract

In this study, Ti-6Al-4V was machined under high pressure cooling conditions. Cutting parameters which were assumed as independent variables are consist of 4 different levels of cutting speed (Vc: 50-70-90-110 m/min), feed rate (f: 0.05-0.1-0.15-0.2 mm/rev) and cutting fluid pressure (P: 6-100-200-300 bar). By using SPSS 20 software, regression equations of surface roughness relative to cutting parameters was obtained as linear, second degree and linear logarithmic. Second degree multiple regression model showed best results of estimation. In the model, 95 percent of the surface roughness alterations can be explained by independent variables. Correlation between experimental data and the model was calculated as 0.975. As a result, second degree regression model proved to be successful in predicting surface roughness. The result of the study confirms the literature. When models are compared the most important parameter that affects surface roughness was observed as the feed rate. The results of the study confirms the literature.

Keywords

• Ti-6Al-4V
• Surface Roughness
• Regression Analysis
• Milling

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