Tırnaklı Birleştirmelerde Sıkma/Çözme Kuvvetinin Malzeme Türü ve Sürtünme Katsayısına Göre Yapay Sinir Ağları Metodu ile Modellenmesi

Year 2018, Volume: 4 Issue: 3, 207 - 215, 24.12.2018

Fulya Erdemir , Murat Tolga Özkan

Abstract

Bu çalışmada Tırnaklı
Birleştirmelerin (Snap-Fit) sıkma/çözme kuvvetlerinin hesaplanması için bir
Yapay Sinir Ağı Modeli geliştirilmiştir. Bu hesaplamanın Bilgisayar Destekli
Tasarım metodolojisi kullanılarak sıkma/çözme kuvvetinin belirlenmesi için,
tırnaklı birleştirme bağlantı tasarım modelinin uç açısı (α) ve malzeme
türlerinin sürtünme katsayıları referans alınmıştır.  Bu amaç ile bir Yapay Sinir Ağı (YSA) modeli
geliştirilmiştir.  Bu sayede malzeme türü
ve tırnaklı bağlantının uç açısı verilerek herhangi bir mühendislik hesabına
gerek kalmaksızın, Bilgisayar destekli sonuç hesap edebilen bir yazılım
geliştirilmiştir. Elde edilen modelin MEP% = 0.624073, RMSE= 0.008977 ve R²=
0.99999 olarak bulunmuştur.  Böyle hem
güvenilir hem de hızlı bir yöntem ile malzeme türü ve bağlantı tırnak ucunun açısal
değerlerine göre sonuç üretebilen bir Tasarım yöntemi geliştirilmiştir. 

Keywords

I Snap-Fit, Makina Tasarımı

Modeling of Mating / Seperating Force in Snap-Fit Joints by Artificial Neural Networks Method by Material Type and Friction Coefficient

Abstract

In this study, an Artificial
Neural Network Model has been developed to calculate the mating / separating
forces of Snap-Fit joints. In order to determine the mating / separating force
of this calculation using Computer Aided Design methodology, the tip angle (α)
and friction coefficients of material types are taken as reference. For this
purpose, an Artificial Neural Network (ANN) model has been developed. In this
way, the material type and the end angle of the claw connection is given,
without the need for any engineering account, a software capable of calculating
computer-aided results has been developed. MEP% = 0.624073, RMSE = 0.008977 and
R² = 0.99999. With such a reliable and fast method, a Design method
has been developed which can produce results according to the material type and
the angular values of the connection snap-fit type.

Keywords

I-Type Snap-fits, Plastic Material Design, Artificial Neural Network

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