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Journals / Mühendislik Bilimleri Dergisi / 2007 / Cilt: 13 - Sayı: 3

Cylindirical spur gears design based on artifical neural networks

Yapay sinir ağları tabanlı silindirik düz dişli çark tasarımı

İHSAN TOKTAŞ
Nizami AKTÜRK
Pages
387–395
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It has been shown that, the ANN based mechanism may be used in the design of cylindrical spur gears instead of analytical calculations.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, testing and training data sets of Artificial Neural Networks(ANNs) models have been produced by employing analytical design calculations of cylindrical spur gears.
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Key Findings & Data:The outcomes demonstrated that, the ANN based model have been very successful and the testing data produced very low level of errors.
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Conclusion & Impact:It has been shown that, the ANN based mechanism may be used in the design of cylindrical spur gears instead of analytical calculations.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this study, testing and training data sets of Artificial Neural Networks(ANNs) models have been produced by employing analytical design calculations of cylindrical spur gears. In the input layer, the constraints and requirement values of cylindrical spur gears are used while at the output layer the modules (e.g. the bending and contact stress) and the number of tooths are used. These data have been presented to train a multi layered, single directed, hierarchically connected ANNs using Scaled Conjugate Gradient (SCG) and Levenberg-Marquardt (LM) Back Propagation algorithms with the logistic sigmoid transfer function. The outcomes demonstrated that, the ANN based model have been very successful and the testing data produced very low level of errors. It has been shown that, the ANN based mechanism may be used in the design of cylindrical spur gears instead of analytical calculations.

Özet

Bu çalışmada, silindirik düz dişli çarkların analitik yöntemle tasarım hesaplamaları yapılarak, Yapay Sinir Ağları (YSA) modelinde kullanılmak üzere, eğitim ve test küme verileri oluşturulmuştur. Girdi katmanında silindirik düz dişli çarkların ihtiyaç ve sınırlandırma değişkenleri, çıktı katmanında ise modül (diş dibi gerilmesine ve yüzey basıncına göre) ve dişli sayıları kullanılmıştır. Bu veriler, çok katmanlı, tek yönlü, hiyerarşik bağlantılı, hatayı geriye yayma (Back Propagation) algoritmasının Scaled Conjugate Gradient (SCG) ve Levenberg-Marquardt (LM) versiyonlarının her biri için 'logistic sigmoid' transfer fonksiyonu kullanılarak, değişken sayıda gizli katman ve işlem eleman sayılarında eğitilmiştir. Sonuçta, eğitilen YSA modeli çok başarılı ve test verileri hata değerleri çok düşük bulunmuştur. Böylece, dişli çarkların kavramsal tasarımında kullanılan analitik hesap yöntemi yerine YSA tabanlı sistematik bir yaklaşımın da kullanılabileceği gösterilmiştir.

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Cite This Article

TOKTAŞ, İ., AKTÜRK, N. (2007). Cylindirical spur gears design based on artifical neural networks. Mühendislik Bilimleri Dergisi, Cilt: 13 - Sayı: 3, 387–395. https://ajindex.com/articles/289122

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