Multiobjective optimization of friction welding of UNS S32205 duplex stainless steel

查看全文 作　　者：[1]P.M.AJITH;Birendra Kumar [1]BARIK;[1]P.SATHIYA;[2]S.ARAVINDAN

机构地区：[1]Department of Production Engineering,National Institute of Technology;[2]Department of Mechanical Engineering,Indian Institute of Technology Delhi

出　　处：《Defence Technology(防务技术)》索引2015年第11卷第2期,共9页

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 design.Process variables,as inputs of the neural network,included friction pressure,upsetting pressure,speed and burn-off length.Tensile strength and microhardness were selected as the outputs of the neural networks.The 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 testing.Due 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 purpose.Five various training algorithms,belonging to three classes,namely gradient descent,genetic algorithm and LevenbergeM arquardt,were used to train artificial neural network.The optimization was carried out by using particle swarm optimization method.Confirmation test was carried out by setting the optimized parameters.In conformation test,maximum tensile strength and maximum hardness obtained are 822 MPa and 322 Hv,respectively.The metallurgical investigations revealed that base metal,partially deformed zone and weld zone maintain austenite/ferrite proportion of 50:50. 关键词：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

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Multiobjective optimization of friction welding of UNS S32205 duplex stainless steel

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