A Monte Carlo Based Robustness Optimization Method in New Product Design Process: A Case Study

Abstract

Monte Carlo method can analyze, solve and optimize many mathematical or physical problems through generating a large number of statistical random samples to simulating stochastic events. It also can be used to remarkably improve design quality of new product. In new product design process, setting distribution characteristics of the design variables is vital to product quality and production robustness. Firstly, response surface model between output characteristics and design variables in new product design is proposed, and the distribution characteristics of design variables and response output are analyzed; then position error model of response output and standard value and allowed error maximum is presented; and then the differences of position error model and allowed error maximum are counted, and reliability ratio is built and calculated, and design robustness of the new product is increased by adjusting the precision value of random design variables in Monte Carlo experiments. Finally, a case is brought forward to verify the validity of the method.

Probabilistic Assessment of Biodeterioration Effects on Reinforced Concrete Sewers

“…where p f is the probability of failure, Φ(•) is the standard normal probability distribution with an expected value equal to 0 and a standard deviation equal to 1, µ Z is the mean value of Z, and σ Z is the standard deviation of Z. Crude Monte Carlo simulations were used to propagate uncertainties in the deterministic model in order to estimate the probability of failure [98][99][100]. Random values were first generated.…”

“…used to propagate uncertainties in the deterministic model in o ability of failure [98][99][100]. Random values were first generated gated in the mechanical model to evaluate the limit state func was finally counted to estimate the probability of failure.…”

“…The probability distribution, the mean value and coefficient of variation of the variables used in this work were obtained from the literature and are shown in Table 2. The selection of lognormal and uniform distributions was produced based on a literature review [100,[104][105][106][107][108][109][110][111]. The lognormal distributions were appropriate for parameters that cannot be physically negative, while uniform distributions were selected when the range of variation of the parameter was known.…”

Robust Ensemble-Based Evolutionary Calibration of the Numerical Wind Wave Model

“…heights, periods, directions). The SWAN model can be described with the action balance equation (5).…”

“…The robust optimal design approaches have a lot of applications in many fields [27]. They are often based on Monte Carlo methods that allow representing the uncertainty from different sources [5]. The perturbation-based ensemble allows sampling the modelling uncertainty in a more systematic way [20].…”