CLC number: TG506
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2010-10-29
Cited: 7
Clicked: 5845
Zahari Taha, Hani-kurniati Lelana, Hideki Aoyama, Raja Ariffin Raja Ghazilla, Julirose Gonzales, Novita Sakundarini, Sugoro B. Sutono. Insert geometry effects on surface roughness in turning process of AISI D2 steel[J]. Journal of Zhejiang University Science A, 2010, 11(12): 966-971.
@article{title="Insert geometry effects on surface roughness in turning process of AISI D2 steel",
author="Zahari Taha, Hani-kurniati Lelana, Hideki Aoyama, Raja Ariffin Raja Ghazilla, Julirose Gonzales, Novita Sakundarini, Sugoro B. Sutono",
journal="Journal of Zhejiang University Science A",
volume="11",
number="12",
pages="966-971",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1001356"
}
%0 Journal Article
%T Insert geometry effects on surface roughness in turning process of AISI D2 steel
%A Zahari Taha
%A Hani-kurniati Lelana
%A Hideki Aoyama
%A Raja Ariffin Raja Ghazilla
%A Julirose Gonzales
%A Novita Sakundarini
%A Sugoro B. Sutono
%J Journal of Zhejiang University SCIENCE A
%V 11
%N 12
%P 966-971
%@ 1673-565X
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1001356
TY - JOUR
T1 - Insert geometry effects on surface roughness in turning process of AISI D2 steel
A1 - Zahari Taha
A1 - Hani-kurniati Lelana
A1 - Hideki Aoyama
A1 - Raja Ariffin Raja Ghazilla
A1 - Julirose Gonzales
A1 - Novita Sakundarini
A1 - Sugoro B. Sutono
J0 - Journal of Zhejiang University Science A
VL - 11
IS - 12
SP - 966
EP - 971
%@ 1673-565X
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1001356
Abstract: surface roughness is an important parameter for ensuring that the dimension of geometry is within the permitted tolerance. The ideal surface roughness is determined by the feed rate and the geometry of the tool. However, several uncontrollable factors including work material factors, tool angle, and machine tool vibration, may also influence surface roughness. The objective of this study was to compare the measured surface roughness (from experiment) to the theoretical surface roughness (from theoretical calculation) and to investigate the surface roughness resulting from two types of insert, ‘C’ type and ‘T’ type. The experiment was focused on the turning process, using a lathe machine Colchester 6000. The feed rate was varied within the recommended feed rate range. We found that there were large deviations between the measured and theoretical surface roughness at a low feed rate (0.05 mm/r) from the application of both inserts. A work material factor of AISI D2 steel that affects the chip character is presumably responsible for this phenomenon. Interestingly, at a high feed rate (0.4 mm/r), the ‘C’ type insert resulted in 40% lower roughness compared to the ‘T’ type due to the difference in insert geometry. This study shows that the geometry of an insert may result in a different surface quality at a particular level of feed rate.
[1]ASSAB XW-42, 2009. Material Specification Sheet. Available from http://assabmalaysia.com/english/1723_ENG_HTML.htm [Accessed on Sept. 14, 2009].
[2]Bhattacharya, A., Das, S., Majumder, P., Batish, A., 2009. Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel using Taguchi design and ANOVA. Production Engineering, 3(1):31-40.
[3]Campatelli, G., 2009. Analysis of the Environmental Impact for a Turning Operation of AISI 1040 Steel. IPROMS Conference.
[4]Carlsson, T., Stjernstoft, T., 2001. A model for calculation of the geometrical shape of the cutting tool-work piece interface. CIRP Annals-Manufacturing Technology, 50(1):41-44.
[5]Groover, M.P., 2007. Fundamentals of Modern Manufacturing, Materials, Processes, and Systems (Student Edition). John Wiley & Sons, Singapore, p 578-580.
[6]Kadirgama, K., Abou-El-Hossein, K.A., 2005. Power prediction model for milling 618 stainless steel using response surface methodology. American Journal of Applied Sciences, 2(7):1182-1187.
[7]Lou, M.S., Chen, J.C., Li, C.M., 1999. Surface roughness prediction technique for CNC end-milling. Journal of Industrial Technology, 15(1):1-6.
[8]Mahardika, M., 2005. Neural Networks Prediction of Cutting Tool Wear during Turning Operations. MS Thesis, University of Malaya, Malaysia.
[9]Puertas Arbizu, I., Perez Luis, C.J., 2003. Surface roughness prediction by factorial design of experiments in turning processes. Journal of Materials Processing Technology, 143-144:390-396.
[10]Sandvik Metal Cutting Technological Guide. Available from http://www2.coromant.sandvik.com/coromant/pdf/Metalworking_Products_061/tech_a_5.pdf [Accessed on Sept. 23, 2009].
[11]Sharma, V.S., Dhiman, S., Sehgal, R., Sharma, S.K., 2008. Estimation of cutting forces and surface roughness for hard turning using neural networks. Journal of Intelligent Manufacturing, 19(4):473-483.
Open peer comments: Debate/Discuss/Question/Opinion
<1>