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

Taegeon Kil,H. N. Yoon,D. I. Jang,Beomjoo Yang

doi:10.32604/cmc.2022.020940

Abstract

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.

Highlights

Conductive filler-incorporated polymeric composites (CPCs) are widely used as engineering materials owing to their good formability and useful mechanical properties as well as their improved electrical conductivity [1–3]
As 6 V of input voltage was applied to the specimen, the carbon nanotube (CNT)-CF0 and CNTCF0.1 specimens scarcely showed an increase in the terminal surface temperature
These results prove that the proposed artificial neural network (ANN) model is suitable to predict the surface temperature and electrical resistance in nanocomposites incorporating CNT and carbon fiber (CF)

Summary

Introduction

Conductive filler-incorporated polymeric composites (CPCs) are widely used as engineering materials owing to their good formability and useful mechanical properties as well as their improved electrical conductivity [1–3]. The major issue when applying CPCs as a self-heating element is the negative temperature coefficient (NTC) effect, referring to a decrease in their resistance levels with an increase in the temperature [6,13,17] This NTC effect can cause the conductive fillers to overheat due to the reduction of electrical resistance, which leads to a thermal shock in the composites [11,18]. They found how the incorporation of different scales of fillers can affect the effective thermal behavior of such composites by using machine learning [21] Against this backdrop, the present study proposes a machine learning-based approach to the prediction of the self-heating characteristics and to the detection of the NTC effect in nanocomposites incorporating CNT and CF. The present predictions were compared with experimental values to verify the applicability of the proposed models

Experimental Program

ANN Model

Experimental Results

Prediction Results and Analysis of Surface Temperature and Electrical Resistance

Detection Results and Analysis of Detecting a Severe NTC Effect

Concluding Remarks