Application of Machine Learning Tools for the Improvement of Reactive Extrusion Simulation,Macromolecular Materials and Engineering

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Application of Machine Learning Tools for the Improvement of Reactive Extrusion Simulation
Macromolecular Materials and Engineering ( IF 4.2 ) Pub Date : 2020-11-25 , DOI: 10.1002/mame.202000375
Fanny Castéran ₁ , Ruben Ibanez ₂ , Clara Argerich ₂ , Karim Delage ₁ , Francisco Chinesta ₂ , Philippe Cassagnau ₁

Affiliation

The purpose of this paper is to combine a classical 1D twin‐screw extrusion model with machine learning techniques to obtain accurate predictions of a complex system despite few data. Systems involving reactive polyethylene oligomer dispersed in situ in a polypropylene matrix by reactive twin‐screw extrusion are studied for this purpose. The twin‐screw extrusion simulation software LUDOVIC is used and machine learning techniques dealing with low data limit are used as a correction of the simulation.

中文翻译：

机器学习工具在改进反应挤出模拟中的应用

本文的目的是将经典的一维双螺杆挤压模型与机器学习技术相结合，以获取尽管数据很少但仍能对复杂系统进行准确预测的功能。为此，研究了通过反应性双螺杆挤出将反应性聚乙烯低聚物原位分散在聚丙烯基质中的体系。使用双螺杆挤出模拟软件LUDOVIC，并且使用处理低数据限制的机器学习技术作为模拟的校正。

更新日期：2020-12-15

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