Efficient algorithm for probabilistic analysis of complex function in engineering applications

Monte Carlo simulation (MCS) has been widely used in probabilistic analysis of the input‐output relationship of a complex function in various applications where probability distributions of the inputs are known. However, MCS is time‐consuming for complex analysis. In addition, it is not straightforward in revealing the relationship between the output variables and the input variables. This paper proposes an alternative analytical approach of statistical estimation, namely the integration of piecewise approach and cell technique that will significantly reduce the number of observations to be taken in a simulation process. The development of the algorithm has included estimation of two statistical parameters, which are the mean and standard deviation. The algorithm has been tested with an engineering example, which is the volumetric shrinkage analysis of a plastic injection molded part.