Optimization of Cutting Parameters for Sustainable Machining of Titanium Ti-5553 Alloy using Genetic Algorithm

Abstract

Çok yüksek takım aşınması, yüksek kesme kuvvetleri, yüksek sıcaklık ve işlenmiş parçaların düşük yüzey kalitesi nedeniyle, Titanyum Ti-5553 alaşımları işlenmesi zor malzemelerden biri olarak kabul edilmiştir. Malzemenin işleme performansını artırmak ve bu arada üretim maliyetini düşürmek için proses parametrelerinin optimize edilmesi en önemli araştırmaların başında gelir. Bu çalışma, bu yeni nesil Titanyum Ti-5553 alaşımı için sürdürülebilir bir işleme süreci önermektedir. Takım aşınmasını, enerji tüketimini ve yüzey pürüzlülüğünü azaltmak ve bu sırada talaş kaldırma oranını artırmak için kesme derinliği, kesme hızı ve ilerleme hızı gibi proses parametreleri optimize edilirken kesme hızı, ilerleme oranı ve kesme derinliği dikkate alınmıştır. Proses parametrelerinin optimize edilmesi için genetik algoritma kullanılmıştır. Genetik algoritma kullanarak yapılan optimizasyon sonucu elde edilen değerler, bu alaşımın işleme performansını büyük ölçüde iyileştirmek ve işleme sürecinin enerji tüketimini, üretim maliyetini düşürmekle birlikte yeni nesil titanyum alaşımının talaşlı imalat sürecindeki talaş kaldırma oranını artırarak daha sürdürülebilir hale getirmek için çok etkili bir yaklaşım olduğunu göstermiştir.

Keywords

• Ti-5333 Alaşımı,Optimizasyon,Genetik Algoritma,İşleme Performansı

Optimization of Cutting Parameters for Sustainable Machining of Titanium Ti-5553 Alloy using Genetic Algorithm

Abstract

Titanium Ti-5553 alloys have been considered as difficult-to-machine materials due to the extremely high tool wear, high cutting forces, high temperature, and poor surface quality of machined parts. Process parameters needs to be optimized in order to improve machining performance and in the meantime reducing manufacturing cost. This study proposes sustainable machining process for this new generation Titanium Ti-5553 alloy. Process parameters including depth of cut, cutting speed, and feed rate were taken into account to optimize parameters for reducing tool wear, energy consumption, and surface roughness, and in the meantime increase material removal rate. Genetic algorithm was utilized for optimizing the process parameters. Obtained results illustrated that optimization using genetic algorithm is a very effective approach to substantially improve machining performance of this alloy and make machining process of this alloy more sustainable by reducing energy consumption, manufacturing cost and increasing material removal rate in machining process of new generation titanium alloy.

Keywords

• Ti-5553 Alloy,Optimization,Genetic Algorithm,Machining Performance

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