Prediction of surface roughness and optimization of cutting parameters in CNC turning of rotational features

المؤلفون المشاركون

Shunia, Yusif K.
Shawish, Raid R.
Abbas, Tahsin Fadil

المصدر

Engineering and Technology Journal

العدد

المجلد 38، العدد 8A (31 أغسطس/آب 2020)، ص ص. 1143-1153، 11ص.

الناشر

الجامعة التكنولوجية

تاريخ النشر

2020-08-31

دولة النشر

العراق

عدد الصفحات

11

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الموضوعات

الملخص 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.

نمط استشهاد جمعية علماء النفس الأمريكية (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

نمط استشهاد الجمعية الأمريكية للغات الحديثة (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

نمط استشهاد الجمعية الطبية الأمريكية (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

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references : p. 1152-1153

رقم السجل

BIM-1236379