Evaluation of Cutting Force Components in Turning of Monel 400 Nickel Based Superalloy and Modeling with Artificial Neural Networks

Barış Özlü; Levent Uğur

Makale detayları

Evaluation of Cutting Force Components in Turning of Monel 400 Nickel Based Superalloy and Modeling with Artificial Neural Networks

Yazarlar:

Barış Özlü | Levent Uğur

Yayıncı:

Adıyaman University

Ana Başlıklar:

Bilgi Sistemleri (Diğer)

Dergi:

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi Vol.10 Issue 21, pp.252-265

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001308663

pISSN:

2149-0309

Anahtar kelimeler:

Özet:

 The study consists of two parts. The first part evaluates the effects of cutting parameters on the cutting force components and the actual cutting force as a result of turning Monel 400 alloy at different cutting parameters. The second part consists of the prediction of the cutting force components measured in turning tests at different cutting parameters by Artificial Neural Networks ANN method. Three different cutting speeds 60-85-110 mmin, three different feed rates 0.05-0.07-0.09 mmrev and three different depths of cut 0.4-0.8-1.2 mm were selected as cutting parameters. The measurement of the cutting force components Fz, Fx and Fy was carried out with a KISTLER 9257 type B piezoelectric dynamometer. ANN method was used to predict the shear force components. As a result, the lowest cutting force components Fz:227.48 N, Fy:154 N and Fx:99.16 N were measured at 0.4 mm depth of cut, 110 mmin cutting speed and 0.05 mmrev feed rate for turning Monel 400 alloy. When the cutting force components predicted by ANN method and experimental results are analyzed, it is seen that the predictions made by ANN method have high accuracy.

 The study consists of two parts. The first part evaluates the effects of cutting parameters on the cutting force components and the actual cutting force as a result of turning Monel 400 alloy at different cutting parameters. The second part consists of the prediction of the cutting force components measured in turning tests at different cutting parameters by Artificial Neural Networks ANN method. Three different cutting speeds 60-85-110 mmin, three different feed rates 0.05-0.07-0.09 mmrev and three different depths of cut 0.4-0.8-1.2 mm were selected as cutting parameters. The measurement of the cutting force components Fz, Fx and Fy was carried out with a KISTLER 9257 type B piezoelectric dynamometer. ANN method was used to predict the shear force components. As a result, the lowest cutting force components Fz:227.48 N, Fy:154 N and Fx:99.16 N were measured at 0.4 mm depth of cut, 110 mmin cutting speed and 0.05 mmrev feed rate for turning Monel 400 alloy. When the cutting force components predicted by ANN method and experimental results are analyzed, it is seen that the predictions made by ANN method have high accuracy.

 The study consists of two parts. The first part evaluates the effects of cutting parameters on the cutting force components and the actual cutting force as a result of turning Monel 400 alloy at different cutting parameters. The second part consists of the prediction of the cutting force components measured in turning tests at different cutting parameters by Artificial Neural Networks ANN method. Three different cutting speeds 60-85-110 mmin, three different feed rates 0.05-0.07-0.09 mmrev and three different depths of cut 0.4-0.8-1.2 mm were selected as cutting parameters. The measurement of the cutting force components Fz, Fx and Fy was carried out with a KISTLER 9257 type B piezoelectric dynamometer. ANN method was used to predict the shear force components. As a result, the lowest cutting force components Fz:227.48 N, Fy:154 N and Fx:99.16 N were measured at 0.4 mm depth of cut, 110 mmin cutting speed and 0.05 mmrev feed rate for turning Monel 400 alloy. When the cutting force components predicted by ANN method and experimental results are analyzed, it is seen that the predictions made by ANN method have high accuracy.

 Yapılan çalışma iki bölümden oluşmaktadır. Birinci Bölüm, Monel 400 alaşımının farklı kesme parametrelerinde tornalanması sonucu kesme parametrelerinin kesme kuvveti bileşenlerine ve esas kesme kuvvetine etkileri değerlendirilmiştir. İkinci bölüm ise farklı kesme parametrelerinde yapılan tornalama deneylerinde ölçülen kesme kuvveti bileşenlerinin Yapay Sinir Ağları YSA yöntemiyle tahmininden oluşmaktadır. Çalışmada kesme parametresi olarak üç farklı kesme hızı 60-85-110 mdak, üç farklı ilerleme miktarı 0,05-0,07-0,09 mmdev ve üç farklı kesme derinliği 0,4-0,8-1,2 mm seçilmiştir. Kesme kuvveti bileşenlerinin Fz, Fx ve Fy ölçümü A KISTLER 9257 B tipi piezoelektrik dinamometre ile gerçekleştirilmiştir. Kesme kuvveti bileşenlerinin tahmininde YSA yöntemi kullanılmıştır. Sonuç olarak, Monel 400 alaşımının tornalanmasında en düşük kesme kuvveti bileşenleri 0,4 mm kesme derinliğinde, 110 mdak kesme hızında ve 0,05 mmdev ilerleme miktarında Fz:227,48 N, Fy:154 N ve Fx:99,16 N ölçülmüştür. YSA yöntemini ile tahmin edilen kesme kuvveti bileşenleri ile deneysel sonuçlar incelendiğinde YSA yöntemini ile yapılan tahminlerin yüksek doğruluk içerdiği görülmüştür.

Makale detayları

Evaluation of Cutting Force Components in Turning of Monel 400 Nickel Based Superalloy and Modeling with Artificial Neural Networks

Yazarlar:

Barış Özlü | Levent Uğur

Yayıncı:

Adıyaman University

Ana Başlıklar:

Bilgi Sistemleri (Diğer)

Dergi:

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi Vol.10 Issue 21, pp.252-265

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001308663

pISSN:

2149-0309

Turning

Monel 400 Alloy

Cutting Force Components

Artificial Neural NetworkTornalama

Monel 400 Alaşımı

Kesme Kuvveti Bileşenleri

Yapay Sinir Ağı.

Sayı

Makale detayları

Yazarlar: Barış Özlü | Levent Uğur

Yayıncı: Adıyaman University

Ana Başlıklar: Bilgi Sistemleri (Diğer)

Dergi: Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi Vol.10 Issue 21, pp.252-265

Belge Tipi: Makale

Yayın Tarihi: 12/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001308663

pISSN: 2149-0309

Sayı

Yazarlar:

Barış Özlü | Levent Uğur

Yayıncı:

Adıyaman University

Ana Başlıklar:

Bilgi Sistemleri (Diğer)

Dergi:

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi Vol.10 Issue 21, pp.252-265

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001308663

pISSN:

2149-0309