Effect of annealing on two different niobium-clad stainless steel PEMFC bipolar plate materials

Niobium (Nb)-clad stainless steels(SS) produced via roll bonding are being considered for use in the bipolar plates of polymer electrolyte membrane fuel cell(PEMFC) stacks. Because the roll bonding process induces substantial work hardening in the constituent materials, thermal annealing is used to restore ductility to the clad sheet so that it can be subsequently blanked, stamped and dimpled in forming the final plate component. Two roll bonded materials, niobium clad 340L stainless steel (Nb/340L SS) and niobium clad 434 stainless steel (Nb/434 SS) were annealed under optimized conditions prescribed by the cladding manufacturer. Comparative mechanical testing conducted on each material before and after annealing shows significant improvement in ductility in both cases. However, corresponding microstructural analyses indicate an obvious difference between the two heat treated materials. During annealing, an interlayer with thick less than 1 μm forms between the constituent layers in the Nb/340L SS, whereas no interlayer is found in the annealed Nb/434 SS material. Prior work suggests that internal defects potentially can be generated in such an interlayer during metal forming operations. Thus, Nb/434 SS may be the preferred candidate material for this application.

Propulsive Velocity Optimization of 3-Joint Fish Robot Using Genetic-Hill Climbing Algorithm

Underwater robot is a new research field which is emerging quickly in recent years.Previous researches in this field focus on Remotely Operated Vehicles(ROVs),Autonomous Underwater Vehicles(AUVs),underwater manipulators,etc.Fish robot, which is a new type of underwater biomimetic robot,has attracted great attention because of its silence in moving and energy efficiency compared to conventional propeller-oriented propulsive mechanism. However,most of researches on fish robots have been carried out via empirical or experimental approaches,not based on dynamic optimality.In this paper,we proposed an analytical optimization approach which can guarantee the maximum propulsive velocity of fish robot in the given parametric conditions.First,a dynamic model of 3-joint(4 links)carangiform fish robot is derived,using which the influences of parameters of input torque functions,such as amplitude,frequency and phase difference,on its velocity are investigated by simulation.Second,the maximum velocity of the fish robot is optimized by combining Genetic Algorithm(GA)and Hill Climbing Algorithm(HCA).GA is used to generate the initial optimal parameters of the input functions of the system.Then,the parameters are optimized again by HCA to ensure that the final set of parameters is the'near'global optimization.Finally,both simulations and primitive experiments are carried out to prove the feasibility of the proposed method.

Improved knowledge-based neural network(KBNN)model for predicting spring-back angles in metal sheet bending

We develop an efficiently improved knowledge-based neural network(KBNN)associated with optimization algorithms and finite element analysis(FEA)to accurately predict spring-back angles in metal sheet bending.The well-known V and U prevalent processes of bending are considered.The KBNN predictive results are based on the empirical model and artificial neural network(ANN)modeling.The empirical model is constructed from the FEA results using response surface method,while the multilayer perceptron is employed to create the ANN.The trained KBNN can accurately model the relation-ship between the spring-back angles and process parameters.The obtained results are validated against other existing methods showing a high accuracy.