Seisdedos Gonzalo, Prisbrey Milo G, Vakhlamov Pavel, Fernandez Joshua, De Freitas Riangello, Rockward Tommy, Davis Eric S
Materials Physics and Applications (MPA-11), Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
Mechanical and Materials Engineering Department, Florida International University, Miami, FL 33174, USA.
Polymers (Basel). 2025 Mar 27;17(7):895. doi: 10.3390/polym17070895.
Thermoset polymers are highly demanded for their structural robustness, thermal stability, and chemical resistance. Tailoring the properties of these polymers for high-performance applications is often preferred to designing brand-new polymers. However, the traditional destructive techniques used to characterize their properties as a function of manufacturing parameters are expensive and time-consuming. A novel non-destructive, data-driven method leveraging ultrasonics and machine learning techniques to tailor the properties of thermosets as a function of the manufacturing parameters is demonstrated. Thermoset epoxy samples with varying curing temperatures (15-40 °C) and curing agent amounts (±40%) were manufactured and tested. Their curing kinetics were monitored by determining the sound speed in the material in real time, while the longitudinal modulus of the samples was determined post-cure. Machine learning models were developed using a k-nearest neighbors algorithm. These models were implemented to predict the curing and final elastic properties using the manufacturing parameters, i.e., stoichiometry and curing temperature, and vice versa. Understanding and modeling how these parameters affect the cure kinetics and final properties will allow for efficient and reliable optimization of thermoset tailoring and manufacturing.
热固性聚合物因其结构坚固性、热稳定性和耐化学性而有很高的需求。对于高性能应用而言，调整这些聚合物的性能通常比设计全新的聚合物更受青睐。然而，用于表征其性能随制造参数变化的传统破坏性技术既昂贵又耗时。本文展示了一种新颖的无损、数据驱动方法，该方法利用超声波和机器学习技术来根据制造参数调整热固性材料的性能。制备并测试了具有不同固化温度（15 - 40°C）和固化剂用量（±40%）的热固性环氧样品。通过实时测定材料中的声速来监测其固化动力学，而样品的纵向模量在固化后测定。使用k近邻算法开发了机器学习模型。这些模型用于根据制造参数（即化学计量比和固化温度）预测固化和最终弹性性能，反之亦然。了解并对这些参数如何影响固化动力学和最终性能进行建模，将有助于对热固性材料的定制和制造进行高效且可靠的优化。
