含能材料2025，Vol.33Issue(6)：625-634,10.DOI:10.11943/CJEM2025060
基于机器学习的超细HNS固相熟化预测模型
Solid-Phase Ripening Prediction Model for Ultrafine HNS based on Machine Learning
摘要
Abstract
Ultrafine hexanitrostilbene(HNS)is widely used in explosion foil initiators and related applications due to its outstand-ing thermal stability and excellent high-voltage short-pulse performance.However,its high surface energy during service process leads to solid-phase ripening.Previous studies have explored the effects of temperature,residual solvents,and time on the solid-phase ripening of ultrafine HNS,but these investigations primarily focused on isolated or narrowly factors.Currently,no multivariate predictive model has been established.In this study,a predictive model was developed based on previously ob-tained small angle X-ray scattering(SAXS)data,including specific surface area(SSA)and relative specific surface area(RSSA),obtained under varying temperatures and residual dimethylformamide(DMF)contents.The model was constructed using ma-chine learning algorithms and optimized empirical models.It comprehensively accounts for time,temperature,and residual DMF content in its predictions.The results show that on the training dataset,the random forest(RF)model achieved an R² of 0.9989 in predictions,while the polynomial regression(PR)model and optimized empirical model attained R² values of 0.9091 and 0.9129,respectively.By comparing the prediction performance of these three models,the most suitable model for predict-ing the solid-phase ripening process of ultrafine HNS was identified.Furthermore,purity tests and scanning electron microscopy(SEM)characterization revealed that particle characteristic variations exert significantly influence on the extent of solid-phase rip-ening in ultrafine HNS.A predictive method was established for the solid-phase ripening process of ultrafine HNS,laying a foun-dation for investigating its aging mechanisms and optimizing storage stability.关键词
超细HNS/SAXS/固相熟化/机器学习/颗粒特性Key words
ultrafine HNS/SAXS/solid-phase ripening/machine learning/particle characteristics分类
武器工业引用本文复制引用
朱金灿,王超,曹洪滔,王敦举,张浩斌,李诗纯,金波,刘渝..基于机器学习的超细HNS固相熟化预测模型[J].含能材料,2025,33(6):625-634,10.基金项目
国家自然科学基金(22375191) （22375191）
CAEP院长基金(YZJJ-ZQ2023005) National Natural Science Foundation of China(No.22375191) （YZJJ-ZQ2023005）
Presidential Foundation of CAEP(No.YZJJ-ZQ2023005) （No.YZJJ-ZQ2023005）
