Anti-rust aluminum is widely used in aviation, aerospace, communications, as well as weapons with non-corrosion, light, and other fine characteristics. In this study, in order to improve the machined surface quality and find the functional relation between cutting parameters and surface roughness, a series of cutting experiments for AlMn1Cu were conducted, and the surface roughness values in high-speed milling were obtained. Firstly, according to the analysis of variance (ANOVA) of factorial experiments, the cutting parameters significantly influencing the surface roughness were presented. Secondly, the mathematical prediction models of surface roughness based on the cutting parameters were established by using the partial least squares regression. Finally, experiments are further designed and carried out to validate the accuracy of the proposed prediction model.

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This article presents an uncertainty analysis method for systems with hybrid stochastic and fuzzy uncertainty parameters based on polynomial chaos expansion (PCE). Parameters in the system are described by probability boxes, interval numbers, and fuzzy sets, respectively, based on the differences in their limited stochastic knowledge. First, this method transforms the uncertain parameters into standard normal distribution and interval variables through equal probability transformation or ‐cut operations. Second, the Legendre and Hermite polynomials are used as the PCE model's primary functions, and the expansion coefficients are calculated by the Galerkin projection method based on sparse grid numerical integration. Then, the system response bounds under the pre‐defined confidence level can be obtained using a genetic algorithm to solve the optimization problem constructed based on PCE models. Finally, the feasibility and effectiveness of the method are illustrated by taking the tank bi‐directional stabilized system and the double‐pendulum‐controlled system as examples. The numerical results show that the system response bounds obtained by the PCE model optimization algorithm are consistent with the Monte Carlo simulation. Still, the computational efficiency is much higher. The proposed method effectively combines fuzzy sets and probability boxes and dramatically simplifies the analysis process of uncertain systems. The method exhibits fine precision even in high‐dimensional uncertainty analysis problems.

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