Ağır Vasıta Fren Sistemlerinde Kullanılan Acil Durum Valf Dinamiğinin Deneysel Olarak Araştırılması ve YSA Tabanlı Modellenmesi

Yıl 2025, Cilt: 13 Sayı: 4, 1791 - 1805, 31.12.2025

İhsan Çetin , Muhammed Arif Şen

https://doi.org/10.29109/gujsc.1825420

Öz

Acil durum valfinin, ağır vasıta araçlarında römorkların manuel veya yarı otomatik şekilde ani frenleme yapabilmesi amacıyla kullanılan pnömatik bir fren valfi olması sebebiyle, dinamik performansı ve tepki süresi kritik öneme sahiptir. Bu çalışma kapsamında, öncelikle konu ile ilgili literatürdeki örnek çalışmalar ve kapsamlı endüstriyel uygulamalar detaylı bir şekilde incelenmiş ve frenleme süresine etki etmesi beklenen parametreler araştırılmıştır. Sistemin giriş parametreleri olarak emniyet yay katsayısı, çek valf yay katsayısı ve hortum çapı belirlenmiş, deneysel tasarım yöntemleri kullanılarak, sistem parametrelerinin frenleme tepki süresi üzerindeki etkisi incelenmiş ve tam faktöriyel bir deney tasarım oluşturulmuştur. Sistemin frenleme tepki süresini ölçmek amacıyla bir deney düzeneğini oluşturulmuş ve kapsamlı deneysel çalışmalar yürütülmüştür. Elde edilen deneysel verilerin regresyon analizi ile fren tepki süresine ait bir matematiksel model elde edilmiştir. Sistemin modelleme performansını geliştirmek amacıyla, deneysel verilerin yapay sinir ağı tabanlı modellenmesine yönelik çalışmalar yapılmıştır. Deneysel veri setinin farklı nöron sayıları, öğrenme algoritmaları ve eğitim-test-doğrulama yüzdeleri belirlenerek eğitilmesine yönelik modelleme çalışmaları gerçekleştirilmiş, daha az hata değerine sahip daha yüksek doğrulukta bir model araştırılmıştır. Elde edilen matematiksel ve yapay sinir ağı modellerinin, giriş parametrelerine bağlı olarak valf tepki süresini modelleme ve tahminleme performansları karşılaştırmalı olarak incelenmiş ve sayısal sonuçlar sunulmuştur. Yapılan deneysel ve modelleme çalışmaları, YSA modelinin matematiksel modele göre daha başarılı tahmin performansı sağlayabileceğini göstermiştir.

Anahtar Kelimeler

Acil Durum Valfi , Frenleme Tepki Süresi , Havalı Fren Sistemi , Matematiksel Modelleme , Yapay Sinir Ağı

Experimental Investigation and ANN-Based Modelling of an Emergency Valve Dynamics Used in Heavy Vehicle Brake Systems

Öz

Since the emergency valve is a pneumatic brake valve used in heavy vehicles to perform sudden braking of trailers manually or semi-automatically, its dynamic performance and response time are of critical importance. In this study, relevant literature and comprehensive industrial applications were thoroughly reviewed, and the parameters which are expected to affect the braking time were investigated. The safety spring coefficient, check valve spring coefficient, and hose diameter were determined as system input parameters. Using experimental design methods, the effects of system parameters on braking response time were investigated, and a full factorial experimental design was developed. An experimental setup was created to measure the braking response time of the system, and comprehensive experimental studies were conducted. A mathematical model of the braking response time was derived through regression analysis of the obtained experimental data. To improve the system's modelling performance, studies were performed on the artificial neural network-based modelling of the experimental data. Modelling studies were carried out to train the experimental data set by determining different neuron numbers, learning algorithms, and training-test-validation percentages, and a higher accuracy model with fewer error values was investigated. The performance of the obtained mathematical and artificial neural network models in predicting the valve response time depending on the input parameters was comparatively examined, and numerical results were presented. The experimental and modelling studies have shown that the ANN model can provide more successful prediction performance compared to the mathematical model.

Anahtar Kelimeler

Emergency Valve , Braking Response Time , Air Brake System , Mathematical Modelling , Artificial Neural Network

Teşekkür

The authors would like to thank the R&D Center of Vaden Automotive Industry Trade Inc. and its staff for their support in establishing the experimental setup and conducting the experiments.

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