Multi-Objective Curing Cycle Optimization for Glass Fabric/Epoxy Composites Using Poisson Regression and Genetic Algorithm

In this study, a multi-parameter design of experiments, using Taguchi method, has been conducted in order to investigate the optimum curing conditions for glass fabric/epoxy laminated composites, followed by a statistical analysis and genetic algorithm optimization. Heating rate a, temperature T1 and duration h1 were treated as independent variables in a L25 Taguchi orthogonal array addressing five levels each. Tensile load and flexural strength were examined as pre-selected quality objectives. The results of the analysis of variance performed showed that the significant parameters for both tensile and flexural strength were temperature and duration, at a 95% confidence level. The estimation of the curing parameters for optimum tensile and flexural performance was achieved with an error considerably lower than 1%. The Poisson regression analysis was introduced to achieve a highly accurate regression model, with R2 greater than 97% for both optimization criteria. Finally, these two regression models were converted into a two-fold function for maximizing both criteria, and used as fitness function for a multi-objective optimization genetic algorithm.本研究采用田口方法（Taguchi method）开展多参数实验设计，旨在探究玻璃纤维织物/环氧树脂层压复合材料的最优固化工艺条件，随后开展统计分析与遗传算法优化。本研究将升温速率a、固化温度T1及保温时长h1作为独立变量，采用每个参数设置5个水平的L25田口正交阵列（L25 Taguchi orthogonal array）开展实验。选取拉伸载荷与弯曲强度作为预设质量目标。所开展的方差分析结果表明，在95%置信水平下，对拉伸与弯曲强度均具有显著影响的参数为固化温度与保温时长。针对实现最优拉伸与弯曲性能的固化参数进行预估，其预估误差显著低于1%。本研究引入泊松回归分析（Poisson regression analysis）以构建高精度回归模型，两类优化准则的决定系数R²均大于97%。最终，将上述两类回归模型整合为可同时最大化两项准则的双目标函数，并将其作为适应度函数应用于多目标优化遗传算法。