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Prediction of surface roughness and optimization of cutting parameters in CNC turning of rotational features
Joint Authors
Shunia, Yusif K.
Shawish, Raid R.
Abbas, Tahsin Fadil
Source
Engineering and Technology Journal
Issue
Vol. 38, Issue 8A (31 Aug. 2020), pp.1143-1153, 11 p.
Publisher
Publication Date
2020-08-31
Country of Publication
Iraq
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200.
The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.
p.
m and feed rate (60, 70, 80, 90 and 100) mm/min.
A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data.
According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and samespindle speed and feed rate pervious which gives the error of 3.23% at evolution terms of process parameters in turning aluminum alloy 1200.
The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.
p.
m and feed rate (60, 70, 80, 90 and 100) mm/min.
A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data.
According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and samespindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.
American Psychological Association (APA)
Shunia, Yusif K.& Abbas, Tahsin Fadil& Shawish, Raid R.. 2020. Prediction of surface roughness and optimization of cutting parameters in CNC turning of rotational features. Engineering and Technology Journal،Vol. 38, no. 8A, pp.1143-1153.
https://search.emarefa.net/detail/BIM-1236379
Modern Language Association (MLA)
Shunia, Yusif K.…[et al.]. Prediction of surface roughness and optimization of cutting parameters in CNC turning of rotational features. Engineering and Technology Journal Vol. 38, no. 8A (2020), pp.1143-1153.
https://search.emarefa.net/detail/BIM-1236379
American Medical Association (AMA)
Shunia, Yusif K.& Abbas, Tahsin Fadil& Shawish, Raid R.. Prediction of surface roughness and optimization of cutting parameters in CNC turning of rotational features. Engineering and Technology Journal. 2020. Vol. 38, no. 8A, pp.1143-1153.
https://search.emarefa.net/detail/BIM-1236379
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 1152-1153
Record ID
BIM-1236379