Farklı kesme parametreleri ve MQL debilerinde elde edilen deneysel değerlerin S/N oranları ve YSA ile analizi
Yükleniyor...
Tarih
2021-09-21
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Gazi Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-ShareAlike 3.0 United States
Attribution-NonCommercial-ShareAlike 3.0 United States
Özet
Bu çalışmada, AISI 4140 çeliğinin tornalanması işleminde kesme hızı, ilerleme oranı ve MQL debisinin esas kesme kuvvetleri (Fc)
ve ortalama yüzey pürüzlülüğüne (Ra) etkisi hem deneysel hem de istatiksel olarak incelenmiştir. Bu doğrultuda deney sonuçlarının
değerlendirilmesinde sinyal/gürültü (S/N) oranları ve yapay sinir ağları (YSA) kullanılmıştır. İşleme deneylerinde, kesme
parametreleri olarak üç farklı kesme hızı (75, 100, 125 m/dk), üç farklı ilerleme oranı (0,16 - 0,25 – 0,5 mm/dev), üç farklı MQL
debisi (0,35 - 0,8 - 1,7 ml/dk) ve sabit kesme derinliği (2,5 mm) seçilmiştir. İşleme deneylerinde MQL debi artışının Fc üzerinde
Ra’ya göre daha etkili olduğu tespit edilmiştir. Ayrıca tüm MQL debi uygulamalarında hem Fc hem de Ra’nın ilerleme oranı ile
arttığı ve kesme hızı ile genel olarak azaldığı görülmüştür. Fc ve Ra için S/N oranları ve YSA ile elde edilen R2 değerleri R2
S/N(Fc)=
0,9996, R2
S/N(Ra)= 0,9984, R2
YSA(Fc)=0,9990 ve R2
YSA(Ra)=0,9884 bulunmuştur. S/N oranlarına göre Fc ve Ra üzerindeki en etkili
kontrol faktörlerinin sırasıyla; ilerleme oranı, kesme hızı ve MQL debi olduğu belirlenmiştir. Elde edilen regresyon değerlerine
bağlı olarak S/N oranlarının ve YSA’nın deneysel verileri yüksek güven aralığında tahmin etmede geçerli olduğu tespit edilmiştir
In this study, the effect of cutting speed, feed rate and MQL flow rate on main cutting forces (Fc) and average surface roughness (Ra) in the turning process of AISI 4140 steel was investigated both experimentally and statistically. Accordingly, signal/noise (S/N) ratios and artificial neural networks (ANN) were used to evaluate the experimental results. As cutting parameters in machining experiments, three different cutting speeds (75, 100, 125 m/min), three different feed rates (0.16 - 0.25 - 0.5 mm/rev), three different MQL flow rates (0.35 - 0.8 - 1.7 ml/min) and a constant depth of cut (2.5 mm) were selected. In machining experiments, it was determined that the increase in MQL flow rate is more effective on Fc than Ra. It was also seen that both Fc and Ra increased with the feed, and generally decreased with the cutting speed in all MQL flow rate applications. R2 values obtained through S/N ratios and ANN for Fc and Ra were found to be R2 S/N(Fc)= 0.9996, R2 S/N(Ra)= 0.9984, R2 YSA(Fc)= 0.9990 and R2 YSA(Ra)= 0.9884. According to S/N ratios, it was determined that the most effective control factors on Fc and Ra are feed rate, cutting speed and MQL flow rate, respectively. Depending on the regression values obtained, it was determined that S/N ratios and ANN are valid in predicting experimental data at high confidence intervals.
In this study, the effect of cutting speed, feed rate and MQL flow rate on main cutting forces (Fc) and average surface roughness (Ra) in the turning process of AISI 4140 steel was investigated both experimentally and statistically. Accordingly, signal/noise (S/N) ratios and artificial neural networks (ANN) were used to evaluate the experimental results. As cutting parameters in machining experiments, three different cutting speeds (75, 100, 125 m/min), three different feed rates (0.16 - 0.25 - 0.5 mm/rev), three different MQL flow rates (0.35 - 0.8 - 1.7 ml/min) and a constant depth of cut (2.5 mm) were selected. In machining experiments, it was determined that the increase in MQL flow rate is more effective on Fc than Ra. It was also seen that both Fc and Ra increased with the feed, and generally decreased with the cutting speed in all MQL flow rate applications. R2 values obtained through S/N ratios and ANN for Fc and Ra were found to be R2 S/N(Fc)= 0.9996, R2 S/N(Ra)= 0.9984, R2 YSA(Fc)= 0.9990 and R2 YSA(Ra)= 0.9884. According to S/N ratios, it was determined that the most effective control factors on Fc and Ra are feed rate, cutting speed and MQL flow rate, respectively. Depending on the regression values obtained, it was determined that S/N ratios and ANN are valid in predicting experimental data at high confidence intervals.
Açıklama
Anahtar Kelimeler
YSA, S/N Oranı, Minimum Miktarda Yağlama (MQL), Esas Kesme Kuvveti, Yüzey Pürüzlülüğü, ANN, S/N Ratio, Minimum Quantity Lubrication (MQL), Main Cutting Force, Surface Roughness
Kaynak
WoS Q Değeri
N/A
Scopus Q Değeri
Cilt
24
Sayı
3
Künye
Gürbüz, H., Gönülaçar, Y.E. (2021). Farklı kesme parametreleri ve MQL debilerinde elde edilen deneysel değerlerin S/N oranları ve YSA ile analizi. Politeknik Dergisi, 24 (3), ss.1093-1107. DOI:https://dx.doi.org/10.2339/politeknik.833833
