A Comparative Study of Artificial Neural Network and Response Surface Methodology for Optimization of Friction Welding of Incoloy 800 H

作     者：K.Anand Rishabh Shrivastava K.Tamilmannan P.Sathiya 

作者机构：School of Engineering and TechnologyIndira Gandhi National Open University Department of Production EngineeringNational Institute of Technology 

出 版 物：《Acta Metallurgica Sinica(English Letters)》 (金属学报（英文版）)

年 卷 期：2015年第28卷第7期

页      面：892-902页

核心收录：

学科分类：12[管理学] 080503[工学-材料加工工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 080201[工学-机械制造及其自动化] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Artificial neural network Burn-off length Response surface methodology Tensile strength 

摘      要：This article deals with the optimization of process parameters for friction welding of Incoloy 800 H rod and compares the results obtained by response surface methodology（RSM） and artificial neural network（ANN）.The experiments were carried out on the basis of a five-level,four-variable central composite *** output parameters were the tensile strength and burn-off length（BOL）.They were considered as a function of four independent input variables,namely heating pressure（HP）,heating time,upsetting pressure（UP）,and upsetting *** RSM results showed that the quadratic polynomial model depicted the interconnection between individual element and *** optimizing the process parameters,ANN analysis was used,and the optimal configuration of the ANN model was found to be 4–9–*** modeling aspect,a requisite trained multilayer perceptron neural network was rooted,and a quick propagation training algorithm was used to train *** purpose of optimization was to decide the maximum tensile strength and minimum burn-off length of the welded joint which was done by varying the friction welding process *** order of importance of input parameters for friction welding of Incoloy 800 H was HP〉 UP〉 N〉*** predicting the model using RSM and ANN,a comparison was made for predicting the effectiveness of two *** analyzing the results,it was observed that as compared to RSM,ANN model was more specific.