CA method with machine learning for simulating the grain and pore growth of aluminum alloys

A cellular automata (CA) method assisted by a back-propagation neural network (BPNN), named CA-BPNN, is proposed to simulate grain and pore growth. First, CA-BPNN uses the BPNN to detect the relations between porosity and solidification parameters and then uses the relations to establish extra transformation rules of pore growth, which are finally implemented on A356 alloy directly. Compared with the computational results, the shapes and volume fraction of pores from experimental observation are consistent. CA-BPNN can reduce the difficulty of simulating the whole solidification process of casting without solving the high-dimensional continuous governing equations of porosity. This work can be further extended to other useful industrial alloys, such as aluminum alloys and various grades of industrial steels, if the experimental data sets are improved and other machine learning algorithms are introduced. (C) 2017 Elsevier B.V. All rights reserved.