Prediction of Yoshida Uemori model parameters by The Bees Algorithm and Genetic Algorithm for 5xxx series aluminium alloys

Abstract

In sheet metal forming processes, springback is a very important issue in the view of the excellent quality design. Several mathematical models have been developed to estimate the springback more accurately, including various material parameters. In this study, the model parameters of Yoshida-Uemori two surface plasticity model, which can well predict the springback for different loading conditions, have been determined using The Bees Algorithm and Genetic Algorithm which are frequently used recently for optimization of nonlinear problems. In addition, the performances of the algorithms have been determined for the different frequency of experimental data, dense-sparse, sparse-dense, dense-dense and sparse-sparse for elastic and plastic regions. According to the results, although the determined material parameters have different values, the fitting performances are found similar for both The Bees Algorithm and Genetic Algorithm. However, in the view of the data frequency, the more appropriate results are obtained from the dense-dense data set (Case 3).

Keywords

• Yoshida Uemori model parameters
• Optimization
• The Bees Algorithm
• Genetic Algorithm

5xxx serisi alüminyum alaşımları için Yoshida Uemori model parametrelerinin Arı Algoritması ve Genetik Algoritma ile tahmini

Öz

Sac metal şekillendirme işlemlerinde tasarım kalitesinin mükemmelliği açısından geri esneme çok önemli bir yer teşkil etmektedir. Geri esnemelerin tahmini için birçok matematiksel model geliştirilmiş olup bu matematiksel model parametrelerinin belirlenmesi için birçok yöntem kullanılmaktadır. Bu çalışmada farklı yükleme koşulları için geri esnemeyi çok iyi tahmin edebilen Yoshida-Uemori iki yüzeyli plastisite malzeme model parametreleri, son zamanlarda doğrusal olmayan problemlerin optimizasyonu için sıkça kullanılan “Arı Algoritması” ve “Genetik Algoritma” kullanılarak belirlenmiştir. Aynı zamanda deneysel veriler elastik ve plastik bölgede sırasıyla; sık-seyrek, seyrek-sık, sık-sık ve seyrek-seyrek olacak şekilde ayarlanarak veri yoğunluğunun parametre sonuçlarına etkisinin incelenmiştir. Elde edilen sonuçlara göre belirlenen malzeme parametreleri farklı değerlere sahip olmasına rağmen Arı Algoritması ve Genetik Algoritma için uyum performansı yaklaşık olarak benzer çıkmıştır. Ancak sonuçlar data sıklığı açısından incelendiğinde sık-sık (Durum 3) veri kümesi daha iyi sonuçlar vermiştir.

Anahtar Kelimeler

• Yoshida Uemori model parametreleri
• Optimizasyon
• Arı Algoritması
• Genetik Algoritma

Kaynakça

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