| 1 | Multiobjective optimization of friction welding of UNS S32205 duplex stainless steel显示文摘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. | P.M.AJITH Birendra Kumar BARIK P.SATHIYA S.ARAVINDAN | 2015 | Defence Technology(防务技术)2015，11，2： | 3 |
| 2 | Multi-objective Optimization of Continuous Drive Friction Welding Process Parameters Using Response Surface Methodology with Intelligent Optimization Algorithm显示文摘The optimum friction welding(FW) parameters of duplex stainless steel(DSS) UNS S32205 joint was determined.The experiment was carried out as the central composite array of 30 experiments.The selected input parameters were friction pressure(F),upset pressure(U),speed(S) and burn-off length(B),and responses were hardness and ultimate tensile strength.To achieve the quality of the welded joint,the ultimate tensile strength and hardness were maximized,and response surface methodology(RSM) was applied to create separate regression equations of tensile strength and hardness.Intelligent optimization technique such as genetic algorithm was used to predict the Pareto optimal solutions.Depending upon the application,preferred suitable welding parameters were selected.It was inferred that the changing hardness and tensile strength of the friction welded joint influenced the upset pressure,friction pressure and speed of rotation. | P.M.AJITH T.M.AFSAL HUSAIN P.SATHIYA S.ARAVINDAN | 2015 | Journal of Iron and Steel Research(International)2015，22，10： | 2 |
您所在的机构暂未开通此部分的功能，请联系管理员