In present study, BP neural network model was proposed for the prediction of ultimate compressive strength of Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting. The inputs of the BP neural network mo...In present study, BP neural network model was proposed for the prediction of ultimate compressive strength of Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting. The inputs of the BP neural network model were the applied load on the epispastic polystyrene template （F）, centrifugal acceleration （v） and sintering temperature （T）, while the only output was the ultimate compressive strength （（7）. According to the registered BP model, the effects of F, v, T on 0 were analyzed. The predicted results agree with the actual data within reasonable experimental error, indicating that the BP model is practically a very useful tool in property prediction and process parameter design of the Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting.展开更多
基金financially supported by the Innovation Research Team Program of the Ministry of Education(IRT0713)the Key Laboratory of New Materials in Automobile of Liaoning Province(grant No.201016201)Doctoral Initiating Project of Liaoning Province Foundation for Natural Sciences,China
文摘In present study, BP neural network model was proposed for the prediction of ultimate compressive strength of Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting. The inputs of the BP neural network model were the applied load on the epispastic polystyrene template （F）, centrifugal acceleration （v） and sintering temperature （T）, while the only output was the ultimate compressive strength （（7）. According to the registered BP model, the effects of F, v, T on 0 were analyzed. The predicted results agree with the actual data within reasonable experimental error, indicating that the BP model is practically a very useful tool in property prediction and process parameter design of the Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting.
