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Journals / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2014 / Cilt: 29 - Sayı: 1

PREDICTION OF SURFACE ROUGHNESS OF Al/SiC COMPOSITE MATERIAL WITH ARTIFICIAL NEURAL NETWORKS

YAPAY SİNİR AĞLARI İLE Al/SiC KOMPOZİT MALZEMENİN YÜZEY PÜRÜZLÜLÜĞÜNÜN TAHMİNİ

İsmail ŞAHİN
Pages
209–216
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

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

In the face of diffuculty of experimental studies and complexity of the analitical expression, as with many studies, this study also showed that ANN is a usable method for predicting the surface roughness value depending on cutting parameters.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, surface roughness of Al/SiC composite material depending on the cutting parameters were predicted with high accuracy using approach of artifical neural network.
📊
Key Findings & Data:The numbers of neuron in network structure of ANN models are 3-5-6-1, 3-6-4-1, 3-6-6-1, 3-4-3-5-1, 3-4-5-3-1, 3-6-2-3-1, 3- 7-1, 3-8-1 ve 3-9-1.
💡
Conclusion & Impact:In the face of diffuculty of experimental studies and complexity of the analitical expression, as with many studies, this study also showed that ANN is a usable method for predicting the surface roughness value depending on cutting parameters.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this study, surface roughness of Al/SiC composite material depending on the cutting parameters were predicted with high accuracy using approach of artifical neural network. Surface roughness values obtained as experimentally result of machining with TiCN+TiN coated cementide carbide cutting element of Al/SiC composite material are trained in nine different ANN models with feed forward back propogation. The numbers of neuron in network structure of ANN models are 3-5-6-1, 3-6-4-1, 3-6-6-1, 3-4-3-5-1, 3-4-5-3-1, 3-6-2-3-1, 3- 7-1, 3-8-1 ve 3-9-1. The values obtained from the ANN training and testing were evaluated by applying the statistical analyses that are widely used in ANN models. In the face of diffuculty of experimental studies and complexity of the analitical expression, as with many studies, this study also showed that ANN is a usable method for predicting the surface roughness value depending on cutting parameters.

Özet

Bu çalışmada Al/SiC kompozit malzemenin yüzey pürüzlülüğü kesme parametrelerine bağlı olarak yapay sinir ağları yaklaşımı kullanılarak yüksek doğrulukta tahmin edilmiştir. Al/SiC kompozit malzemenin TiCN+TiN kaplamalı cementide carbide kesici takımla işlenmesi sonucu deneysel olarak elde edilen yüzey pürüzlülüğü değerleri ileri beslemeli geriye yayılımlı 9 farklı YSA modelde eğitilmiştir. YSA modellerinin ağ yapılarındaki nöron sayıları: 3-5-6-1, 3-6-4-1, 3-6-6-1, 3-4-3-5-1, 3-4-5-3-1, 3-6-2-3-1, 3-7-1, 3-8-1 ve 3-9-1'dir. YSAnın eğitimi ve testi sonrası elde edilen değerler YSA modellerde yaygın olarak kullanılan istatistiksel analizlere tabi tutularak incelenmiştir. Deneysel çalışmaların zorluğu, analitik ifadelerin karmaşıklığı bir çok çalışmada olduğu gibi, YSA kullanımının avantajı kullanılarak kesme parametrelerine bağlı olarak yüzey pürüzlülüğünün tahmini bu çalışmada da YSAnın kullanılabilirliğini göstermiştir.

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Cite This Article

ŞAHİN, İ. (2022). PREDICTION OF SURFACE ROUGHNESS OF Al/SiC COMPOSITE MATERIAL WITH ARTIFICIAL NEURAL NETWORKS. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 29 - Sayı: 1, 209–216. https://ajindex.com/articles/523684

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