TY  - JOUR
AB  - 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.
VL  - 21
IS  - 5
SN  - 0264-4401
DO  - 10.1108/02644400410543959
UR  - https://doi.org/10.1108/02644400410543959
AU  - Soh Y.C.
AU  - Lam Y.C.
AU  - Wu Zhang
PY  - 2004
Y1  - 2004/01/01
TI  - Efficient algorithm for probabilistic analysis of complex function in engineering applications
T2  - Engineering Computations
PB  - Emerald Group Publishing Limited
SP  - 540
EP  - 559
Y2  - 2024/04/16
ER  -