Machine Learning-Based Predictions on the Self-Heating Characteristics of Nanocomposites with Hybrid Fillers  

作　　者：Taegeon Kil D.I.Jang H.N.Yoon Beomjoo Yang 

机构地区：[1]Department of Civil and Environmental Engineering,Korea Advanced Institute of Science and Technology(KAIST),Daejeon,34141,Korea [2]School of Civil Engineering,Chungbuk National University,Cheongju,28644,Korea

出　　处：《Computers, Materials & Continua》2022年第6期4487-4502,共16页计算机、材料和连续体（英文）

基　　金：This research was supported by Chungbuk National University Korea National University Development Project(2021).

摘　　要：A machine learning-based prediction of the self-heating characteristics and the negative temperature coefficient(NTC)effect detection of nanocomposites incorporating carbon nanotube(CNT)and carbon fiber(CF)is proposed.The CNT content was fixed at 4.0 wt.%,and CFs having three different lengths(0.1,3 and 6 mm)at dosage of 1.0 wt.%were added to fabricate the specimens.The self-heating properties of the specimens were evaluated via self-heating tests.Based on the experiment results,two types of artificial neural network(ANN)models were constructed to predict the surface temperature and electrical resistance,and to detect a severe NTC effect.The present predictions were compared with experimental values to verify the applicability of the proposed ANN models.The ANN model for data prediction was able to predict the surface temperature and electrical resistance closely,with corresponding R-squared value of 0.91 and 0.97,respectively.The ANN model for data detection could detect the severe NTC effect occurred in the nanocomposites under the self-heating condition,as evidenced by the accuracy and sensitivity values exceeding 0.7 in all criteria.

关 键 词：Machine learning NANOCOMPOSITES carbon fillers SELF-HEATING negative temperature coefficient 

分 类 号：TB33[一般工业技术—材料科学与工程] TP181[自动化与计算机技术—控制理论与控制工程]