Multiobjective optimization of friction welding of UNS S32205 duplex stainless steel

作     者：P.M.AJITH Birendra Kumar BARIK P.SATHIYA S.ARAVINDAN 

作者机构：Department of Production EngineeringNational Institute of Technology Department of Mechanical EngineeringIndian Institute of Technology Delhi 

出 版 物：《Defence Technology（防务技术）》 (Defence Technology)

年 卷 期：2015年第11卷第2期

页      面：157-165页

核心收录：

学科分类：080503[工学-材料加工工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 

主　　题：多目标优化 不锈钢焊接 摩擦压力 双相 人工神经网络 UNS 拉伸强度 优化工艺参数 

摘      要：The present study is to optimize the process parameters for friction welding of duplex stainless steel(DSS UNS S32205).Experiments were conducted according to central composite *** variables,as inputs of the neural network,included friction pressure,upsetting pressure,speed and burn-off *** strength and microhardness were selected as the outputs of the neural *** weld metals had higher hardness and tensile strength than the base material due to grain refinement which caused failures away from the joint interface during tensile *** to shorter heating time,no secondary phase intermetallic precipitation was observed in the weld joint.A multi-layer perceptron neural network was established for modeling *** various training algorithms,belonging to three classes,namely gradient descent,genetic algorithm and LevenbergeM arquardt,were used to train artificial neural *** optimization was carried out by using particle swarm optimization *** test was carried out by setting the optimized *** conformation test,maximum tensile strength and maximum hardness obtained are 822 MPa and 322 Hv,*** metallurgical investigations revealed that base metal,partially deformed zone and weld zone maintain austenite/ferrite proportion of 50:50.