基于机器学习评价硝化纤维素塑化工艺的可靠性研究OA

To evaluate the plasticization behavior of nitrocellulose,machine learning was employed with impact strength select-ed as the performance index.Plasticization temperature,nitrogen content,plasticization time,solvation ratio,and alcohol-ether ratio were used as independent variables to build a multi-factor quadratic regression model.Response surface methodology analyzed the main effects and interactions among these factors.Significant interaction effects are observed among the five vari-ables.To address the limited performance of traditional linear models under small-sample and nonlinear conditions,a random forest model was combined with a nonlinear correction layer.Gaussian-noise data augmentation improved the robustness of the training set.The combined RF+GBR model achieves an R2 of 0.98 and an MSE of 0.0341(kJ·m-2)2 on the training data.Five-fold cross-validation yields an average R2 of 0.95 and an MSE of 0.63(kJ·m-2)2.These results indicate high fitting accuracy and strong generalization capability.Feature-importance analysis identifies nitrogen content as the dominant factor affecting impact strength,followed by solvation ratio.The study provides a quantitative basis for evaluating plasticization reliability and optimiz-ing process parameters.