Monte Carlo simulation study of porcelain clay design

Design of Mino ware porcelain clay is undertaken to solve a multi-objective optimization problem of achieving balance among physical properties such as plasticity, pyroplastic deformation, firing color, linear shrinkage, and the coefficient of linear thermal expansion, and raw material cost. The designer seeks a Pareto solution through repeated empirical trial and error. For this study, we attempted to solve this problem using Monte Carlo simulation (MCS). An MCS computer program was created that uses the pyroplastic deformation number obtained by finite element analysis as an indicator function, implements machine learning, and interprets and executes three commands that convey the designer’s intentions. The MCS effectiveness was confirmed by examining the number of Pareto solutions satisfying the four objectives of plasticity, pyroplastic deformation, firing color, and cost using a porcelain clay design that is actually used in Mino ware. When the number of pseudorandom numbers generated by the MCS was set as 213 per batch and 10 samples of 10 batches were run, the arithmetic means of the Pareto solution numbers were 13–1158, depending on the porcelain clay design and command combination.