Modeling of the Vibration Characteristics of Leaf Spring Systems using Radial Based Artificial Neural Networks

Abstract

In this work, a Radial Basis Artificial Neural Network (RBANN) structure was proposed to model the acceleration effects occurring on leaf spring systems. In the experimental studies, pressure values of 25, 31.25, 37.5, 43.75 and 50 bar were applied to the steel leaf spring system by a hydraulic piston for 4 and 22 seconds and the acceleration effects that occur have been measured by a uniaxial accelerometer. From the experimental results, it was observed that the magnitude of acceleration was increasing at high pressure values. After the experimental studies, the acceleration data measured from the leaf spring system under the relevant working conditions were analyzed with RBANN structures having spread constant values of 0.5 and 1.0. From the simulation results, it was observed that the RBANN structure with a spread constant value of 0.5 predicts the real-time accelerations in the leaf spring system with higher accuracy. Consequently, it was observed that the real-time acceleration effects occurring on a leaf spring system can successfully be predicted with the proposed RBANN structure.

Keywords

• Radial Basis Artificial Neural Networks
• Vibration Analysis
• Leaf Spring System

Project Number

FYL-2021-11142

Radyal Tabanlı Yapay Sinir Ağları Kullanılarak Yaprak Yay Sistemi Titreşim Karakteristiğinin Modellenmesi

Abstract

Bu çalışmada, yaprak yay sistemleri üzerinde oluşan ivme etkilerini modellemek amacıyla Radyal Tabanlı Yapay Sinir Ağı (RTYSA) yapısı önerilmiştir. Deneysel çalışmalarda, bir hidrolik piston tarafından çelik yaprak yay sistemine 4 ve 22 saniyelik sürelerde 25, 31.25, 37.5, 43.75 ve 50 bar basınç değerleri uygulanmış ve ardından oluşan ivme etkileri tek eksenli ivme sensörü kullanılarak ölçülmüştür. Deneysel sonuçlardan, yüksek basınç değerlerinde ivme genliklerinin arttığı gözlemlenmiştir. Deneysel çalışmalardan sonra, yaprak yay sisteminden ilgili çalışma şartları altında ölçülen ivme verileri yayılma sabiti 0.5 ve 1.0 olan RTYSA yapıları ile analiz edilmiştir. Simülasyon sonuçlarından, 0.5 yayılma sabitine sahip RTYSA yapısının yaprak yay sisteminde meydana gelen gerçek zamanlı ivme değerlerini daha yüksek doğrulukla tahmin edebildiği gözlemlenmiştir. Sonuç olarak, önerilen RTYSA yapısı ile bir yaprak yay sisteminde meydana gelen gerçek zamanlı ivme etkilerinin başarılı bir şekilde tahmin edilebildiği görülmüştür.

Keywords

• Radyal Tabanlı Yapay Sinir Ağları
• Titreşim Analizi
• Yaprak Yay Sistemi

Supporting Institution

Erciyes Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Project Number

FYL-2021-11142

Thanks

Bu çalışma, Erciyes Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından “FYL-2021-11142” kodlu proje ile desteklenmiştir.

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