Optimization of composition of as-cast chromium white cast iron based on wear-resistant performance

The wear-resistant performance of chromium white cast iron was performed by an L9 (34) orthogonal experiment. The differences between orthogonal design and radial base function artificial neural network (RBFANN) were investigated. The results show that Cu significantly influences the wear-resistant performance. The optimum compositions are 5.5%Cr, 2%Si, 3%Mn and 2%Cu. The predicted and simulated results indicate that the RBFANN can not only be used to establish robust model for the orthogonal experiment data but also be rather better than the quadratic regression.