Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network
المؤلفون المشاركون
Abbas, Adel Taha
Ragab, Adham Ezzat
Alharthi, Nabeel H.
Bingol, Sedat
Alharbi, Hamad F.
El-Danaf, Ehab A.
المصدر
Advances in Materials Science and Engineering
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-8، 8ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-05-30
دولة النشر
مصر
عدد الصفحات
8
الملخص EN
In this paper artificial neural network (ANN) and regression analysis were used for the prediction of surface roughness.
Five models of neural network were developed and the model that showed best fit with experimental results was with 6 neurons in the hidden layer.
Regression analysis was also used to build a mathematical model representing the surface roughness as a function of the process parameters.
The coefficient of determination was found to be 94.93% and 93.63%, for the best neural network model and regression analysis, respectively, from the comparison of the models with thirteen validation experimental tests.
Optical microscopy was conducted on two machined surfaces with two different values of feed rates while maintaining the spindle speed and depth of cut at the same values.
Examining the surface topology and surface roughness profile for the two surfaces revealed that higher feed rate results in relatively thick roughness markings that are distantly spaced, whereas low values of feed rate result in thin surface roughness markings that are closely spaced giving better surface finish.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Alharthi, Nabeel H.& Bingol, Sedat& Abbas, Adel Taha& Ragab, Adham Ezzat& El-Danaf, Ehab A.& Alharbi, Hamad F.. 2017. Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network. Advances in Materials Science and Engineering،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1124711
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Alharthi, Nabeel H.…[et al.]. Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network. Advances in Materials Science and Engineering No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1124711
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Alharthi, Nabeel H.& Bingol, Sedat& Abbas, Adel Taha& Ragab, Adham Ezzat& El-Danaf, Ehab A.& Alharbi, Hamad F.. Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network. Advances in Materials Science and Engineering. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1124711
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1124711
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