YAPAY ZEKA KULLANILARAK TREN TEKERLEKLERİNİN YORULMA ÖZELLİKLERİNİN VEKİL MODELLENMESİ

Year 2024, Volume: 12 Issue: 2, 277 - 284, 30.06.2024

Mehran Mahouti , Mehmet Sinan Komek , Suat Yılmaz

https://doi.org/10.21923/jesd.1434972

Abstract

Sonlu elemanlar yöntemi (FEM), tren tekerlekleri gibi karmaşık yapıların analiz edilmesi ve tasarlanması için mühendislikte hayati bir araçtır. Tren tekerlekleri, işletme ömürleri boyunca karşılaştıkları aşırı ve değişken yükler nedeniyle yorulmaya maruz kalmaktadır ve bu durum, ömür süresi ve güvenlik üzerindeki etkileri nedeniyle tren tekerleği tasarımında kritik bir endişe kaynağıdır. Ancak, özellikle tren tekerlekleri gibi karmaşık geometrilere sahip büyük ölçekli yapıların modellenmesinde FEM'in geniş hesaplama ihtiyaçları önemli zorluklar sunmaktadır. Doğru yorgunluk analizi için gereken detaylı modelleme, genellikle büyük hesaplama yükleri ve uzun zaman dilimleri ile sonuçlanmakta ve bu durum, hızlı karar verilmesi gereken durumlarda daha az uygulanabilir bir seçenek haline gelmektedir. Bu sınırlamaları ele almak için, Yapay Zeka (AI), yenilikçi bir çözüm olarak ortaya çıkmıştır. FEM simülasyonlarından elde edilen veri setleri üzerinde eğitilen YZ modelleri, geleneksel hesaplama maliyeti ve zamanının bir kısmında yorgunluk ömrünü tahmin ederek etkin bir alternatif sunmaktadır. Bu vekil modeller, mühendislik tasarım optimizasyonu süreçleri için gerekli olan hızlı ve doğru tahmini sağlamaktadır. Bu çalışmada YZ tabanlı vekil modelleme yaklaşımı ile tren tekerlekleri optimizasyon problemini geleneksel FEM yaklaşımına kıyas ile nerdeyse %90 oranında hızlandırma başarısına erişilmiştir.

Keywords

Yapay Zeka, Optimizasyon, Vekil modelleme, Sonlu elemanlar yöntemi

SURROGATE MODELLING OF TRAIN WHEELS FATIGUE CHARACTERISTICS USING ARTIFICIAL INTELLIGENCE

Abstract

The finite element method (FEM) is a vital tool in engineering for analyzing and designing complex structures such as train wheels. Train wheels are subject to fatigue due to the extreme and variable loads they encounter throughout their operating life, and this is a critical concern in train wheel design due to its effects on lifespan and safety. However, the large computational needs of FEM present significant challenges, especially in modeling large-scale structures with complex geometries such as train wheels. The detailed modeling required for accurate fatigue analysis often results in large computational loads and long time periods, making it a less feasible option in situations where rapid decisions must be made. To address these limitations, Artificial Intelligence (AI) has emerged as an innovative solution. AI models trained on data sets obtained from FEM simulations offer an effective alternative by predicting fatigue life at a fraction of the traditional computational cost and time. These surrogate models provide the fast and accurate prediction required for engineering design optimization processes. In this study, the AI-based surrogate modeling approach succeeded in accelerating the train wheels optimization problem by almost 90% compared to the traditional FEM approach.

Keywords

Artificial intelligence, Optimization, Surrogate modeling, Finite element method.

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