Optimization of Rectangular Section Belleville Spring with Artificial Neural Network

Year 2023, Volume: 21 Issue: 1, 1 - 11, 26.05.2023

Burak AYDOĞDU Necmettin KAYA

https://doi.org/10.56193/matim.1197038

Abstract

Machine learning has a crucial role in significantly reducing the time and costs devoted to analysis in product design. Optimization work with machine learning provides excellent advantages in terms of time and cost compared to finite element analysis. In this study, a machine learning model was trained for Belleville springs and aimed to use the trained model in the size optimization of Belleville springs. To show the applicability of single and multi-purpose optimization methods, the optimization of various Belleville springs with desired spring stiffness is discussed, and Pareto solutions are presented and examined. Finite element analyzes were carried out with the obtained design parameters, and artificial neural network and finite element results were compared. It has been seen that the optimization result can be reached within 4.5 seconds with the artificial neural network, and the accuracy of the obtained results (96.16%) is similar to the neural network success rate (97.67%).

Keywords

Belleville Spring, Finite Element Analysis, Machine Learning, Artificial Neural Network, Optimization, Genetic Algorithm

Dikdörtgen Kesitli Disk Yayların Yapay Sinir Ağı İle Optimizasyonu

Abstract

Ürün tasarımında analizlere ayrılan zamanın ve maliyetlerin önemli ölçüde azaltılmasında makine öğrenmesi kilit rol oynamaktadır. Makine öğrenmesi ile gerçekleştirilen optimizasyon çalışması, sonlu elemanlar analizine kıyasla zaman ve maliyet açısından büyük üstünlükler sağlamaktadır. Bu çalışmada, disk yaylar için bir makine öğrenmesi modeli eğitilmiş, eğitilen modelin disk yayların boyut optimizasyonunda kullanılması amaçlanmıştır. Tek ve çok amaçlı optimizasyon yöntemlerinin uygulanabilirliğini göstermek için istenilen yay rijitliğine sahip çeşitli disk yayların optimizasyonu ele alınmış ve Pareto çözümleri sunularak çözümler incelenmiştir. Elde edilen tasarım parametreleri ile sonlu elemanlar analizleri gerçekleştirilmiş ve yapay sinir ağı ile sonlu elemanlar sonuçları karşılaştırılmıştır. Yapay sinir ağı kullanımı ile optimizasyon sonucuna 4,5 saniye içerisinde ulaşılabildiği ve elde edilen sonuçların doğruluklarının (%96,16) sinir ağı başarı oranı (%97,67) ile benzer olduğu görülmüştür.

Keywords

Disk Yay, Sonlu Elemanlar Analizi, Makine Öğrenmesi, Yapay Sinir Ağı, Optimizasyon, Genetik Algoritma

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