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
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
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر