Yüksek İlerleme ile Frezeleme İşlemi Esnasında Oluşan Kesme Kuvvetinin ve İş Parçası Yüzey Pürüzlülüğünün Yapay Sinir Ağları ile Modellenmesi

Year 2021, Volume: 7 Issue: 1, 58 - 66, 30.04.2021

Ramazan Cakıroglu , Gültekin Uzun

Abstract

Bu çalışma üç aşamadan oluşmaktadır. İlk aşamada vermüküler grafitli dökme demir numunelerine farklı östemperleme işlemi uygulayarak üç farklı sertlikte (42.77, 44.07 ve 45.6 HRC ) malzemeler elde edilmiştir. İkinci aşamada frezeleme deneyleri yüksek ilerleme (High-Feed) (0.6-0.9 ve 1.2 mm/dev) değerlerinde, üç farklı kesme hızında (50-70 ve 98 m/dak) ve 0.5 mm kesme derinliğinde gerçekleştirilerek bileşke kuvvet (Fr) ve ortalama yüzey pürüzlülüğü (Ra) değerleri elde edilmiştir. Deneyler sonucunda Ra değeri 0.461 µm ile 4.635 µm değerleri arasında değişirken, Fr değerleri 1310 N ile 2563 N arasında oluşmuştur. Son aşamada ise elde edilen Fr ve Ra değerleri kullanılarak yapay sinir ağları (YSA) yöntemiyle matematiksel formül geliştirilmiştir. Elde edilen denklem sonucu tahmin edilen değerler ile deney sonuçları arasında uyum olduğu görülmüştür.

Keywords

Östemperleme, yüksek ilerleme (High-Feed), bileşke kuvvet, yüzey pürüzlülüğü, yapay sinir ağları

Modeling of the Cutting Force and Workpiece Surface Roughness During the Milling Process with High Feed Using Artificial Neural Networks

Abstract

This study consists of three stages. In the first stage, materials of three different hardness (42.77, 44.07 and 45.6 HRC) were obtained by applying different austempering process to cast iron samples with vermicular graphite. In the second stage, milling experiments are performed at high feed (0.6-0.9 and 1.2 mm/rev), three different cutting speeds (50-70 and 98 m/min) and 0.5 mm cutting depth, and the resultant force (Fr) and average Surface roughness (Ra) values were obtained. As a result of the experiments, Ra value varied between 0.461 µm and 4.635 µm, while Fr values were between 1310 N and 2563 N. In the last stage, a mathematical formula was developed by using the artificial neural networks (ANN) method using the obtained Fr and Ra values. As a result of the obtained equation, it was observed that there was a harmony between the estimated values and the experimental results.

Keywords

Austempering, High-Feed, cutting force, surface roughness, artificial neural networks

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