Prediction the influence of machining parameters for CNC turning of aluminumalloy using RSM and ANN
المؤلفون المشاركون
Abd al-Rida, Hind H.
Hliyil, Asil J.
Durubi, Ahmad A. A.
المصدر
Engineering and Technology Journal
العدد
المجلد 38، العدد 6A (30 يونيو/حزيران 2020)، ص ص. 887-895، 9ص.
الناشر
تاريخ النشر
2020-06-30
دولة النشر
العراق
عدد الصفحات
9
التخصصات الرئيسية
الكيمياء
تكنولوجيا المعلومات وعلم الحاسوب
الموضوعات
الملخص EN
The main objective of this paper is to develop a prediction model using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) for the turning process of Aluminum alloy 6061 round rod.
The turning experiments carried out based on the Central Composite Design (CCD) of Response Surface Methodology.
The influence of three independent variables such as Cutting speed (150, 175 and 200 mm/ min), depth of cut (0.5, 1 and 1.5 mm) and feed rate (0.1, 0.2 and 0.3 mm/rev) on the Surface Roughness (Ra) wereanalyzed through analysis of variance (ANOVA).
The response graphs from the Analysis of Variance (ANOVA) present that feed-rate has the strongest influence on Ra dependent on cutting speed and depth of cut.
Surface response methodology developed between the machining parameters and response and confirmation experiments revealsthat the good agreement with the regression models.
The coefficient of determination value for RSM model is found to be high (R2 = 0.961).
It indicates the goodness of fit for the model and high significance of the model.
From the result, the maximum error between the experimental value and ANN model is less than the RSM model significantly.
However, if the test patterns number will be increased then this error can be further minimized.
The proposed RSM and ANN prediction model sufficiently predict Ra accurately.
However, ANN prediction model is found to be better compared to RSM model.
The artificial neutral network is applied to experimental results to find prediction results fortwo response parameters.
The predicted results taken from ANN show a good agreement between experimental and predicted values with the mean squared error of training indices equal to (0.000) which producesflexibility to the manufacturing industries to select the best setting based on The main objective of this paper is to develop a prediction model using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) for the turning process of Aluminum alloy 6061 round rod.
The turning experiments carried out based on the Central Composite Design (CCD) of Response Surface Methodology.
The influence of three independent variables such as Cutting speed (150, 175 and 200 mm/ min), depth of cut (0.5, 1 and 1.5 mm) and feed rate (0.1, 0.2 and 0.3 mm/rev) on the Surface Roughness (Ra) wereanalyzed through analysis of variance (ANOVA).
The response graphs from the Analysis of Variance (ANOVA) present that feed-rate has the strongest influence on Ra dependent on cutting speed and depth of cut.
Surface response methodology developed between the machining parameters and response and confirmation experiments revealsthat the good agreement with the regression models.
The coefficient of determination value for RSM model is found to be high (R2 = 0.961).
It indicates the goodness of fit for the model and high significance of the model.
From the result, the maximum error between the experimental value and ANN model is less than the RSM model significantly.
However, if the test patterns number will be increased then this error can be further minimized.
The proposed RSM and ANN prediction model sufficiently predict Ra accurately.
However, ANN prediction model is found to be better compared to RSM model.
The artificial neutral network is applied to experimental results to find prediction results fortwo response parameters.
The predicted results taken from ANN show a good agreement between experimental and predicted values with the mean squared error of training indices equal to (0.000) which producesflexibility to the manufacturing industries to select the best setting based on applications
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Abd al-Rida, Hind H.& Hliyil, Asil J.& Durubi, Ahmad A. A.. 2020. Prediction the influence of machining parameters for CNC turning of aluminumalloy using RSM and ANN. Engineering and Technology Journal،Vol. 38, no. 6A, pp.887-895.
https://search.emarefa.net/detail/BIM-1236504
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Abd al-Rida, Hind H.…[et al.]. Prediction the influence of machining parameters for CNC turning of aluminumalloy using RSM and ANN. Engineering and Technology Journal Vol. 38, no. 6A (2020), pp.887-895.
https://search.emarefa.net/detail/BIM-1236504
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Abd al-Rida, Hind H.& Hliyil, Asil J.& Durubi, Ahmad A. A.. Prediction the influence of machining parameters for CNC turning of aluminumalloy using RSM and ANN. Engineering and Technology Journal. 2020. Vol. 38, no. 6A, pp.887-895.
https://search.emarefa.net/detail/BIM-1236504
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 895
رقم السجل
BIM-1236504
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