Anizotropi ve gerilme tabanlı hibrit Hill48 denklem önerisi ve katsayı optimizasyonu

Yıl 2026, Cilt: 32 Sayı: 1, 1 - 9, 01.02.2026

Süleyman Kılıç

https://doi.org/10.5505/pajes.2025.58534

https://izlik.org/JA86MK36ZN

Öz

Sonlu elemanlar analizlerinde sıklıkla kullanılan Hill48 akma kriteri hata oranı yüksek bir modeldir. Bu çalışmada, Hill48 akma kriterindeki model parametrelerinin belirlenmesi için yeni bir hibrit denklem önerisi sunulmaktadır. Önerilen yeni denklem, anizotropi ve gerilme değerlerini birleştirerek model katsayılarını optimize eden bir fonksiyon oluşturmaktadır. Deneysel verilerle uyumlu hale getirmek amacıyla, iki farklı optimizasyon algoritması (Genetik Algoritma ve Parçacık Sürü Optimizasyonu) kullanılarak karşılaştırması yapılmıştır. Bu algoritmalar, deneysel ve teorik değerler arasındaki farkların karesinin toplamını minimize eden bir hata fonksiyonunu optimize ederek en uygun model katsayılarını belirlemektedir. Elde edilen sonuçlara göre yeni denklem önerisi, geleneksel Hill48 denklemine göre hata payını düşürmektedir. Bu çalışma, Hill48 akma kriterinin daha hassas ve doğru bir şekilde uygulanmasını sağlamayı hedeflemekte olup, mühendislik uygulamalarında önemli katkılar sunacaktır.

Anahtar Kelimeler

Hill48 akma kriteri , Optimizasyon teknikleri , Genetik algoritma , Parçacık sürü optimizasyonu

Anisotropy and stress-based hybrid Hill48 equation proposal and coefficient optimization

https://izlik.org/JA86MK36ZN

Öz

Hill48 yield criterion, which is frequently used in finite element analyses, is a model with a high error margin. In this study, a new hybrid equation proposal is presented to determine the model parameters in the Hill48 yield criterion. The proposed new equation combines the values of anisotropy and stress values to form a function that optimizes the model coefficients. In order to be compatible with experimental data, a comparison was made using two different optimization algorithms (Genetic Algorithm and Particle Swarm Optimization). These algorithms determine the optimal model coefficients by optimizing an error function that minimizes the sum of the squares of the differences between experimental and theoretical values. According to the results obtained, the new equation proposal reduces the margin of error compared to the traditional Hill48 equation. This study aims to ensure a more precise and accurate application of the Hill48 yield criterion and will make significant contributions to engineering applications.

Anahtar Kelimeler

Hill48 yield criterion , Optimization techniques , Genetic algorithm , Particle swarm optimization

Kaynakça

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