# Identification of Loading Location and Amplitude in Conductive Composite Materials via Deep Learning Method

## TL;DR

This paper introduces a deep learning method to accurately detect where and how strong a force is applied on conductive composite materials using simple electrodes.

## Contribution

A novel deep learning-based self-sensing method is proposed for identifying load location and amplitude in conductive composites with minimal electrode complexity.

## Key findings

- A CNN model with residual learning accurately identifies load location and amplitude in conductive composites.
- The method achieves a localization error of 0.91 mm and an amplitude error of 0.13 N.
- The approach outperforms previous methods in terms of accuracy and simplicity of electrode layout.

## Abstract

Current electrical self-sensing methods for composite structural health monitoring face significant limitations. Firstly, they often require complicated electrode layouts. Secondly, accurately determining both the location and amplitude of external loads remains a significant challenge. In this study, a deep learning-based self-sensing method is developed to identify the location and amplitude of external mechanical loads in resin-based conductive composites with a simple electrode layout. First, conductive filler-filled resin composites are prepared, and three-dimensional conductive networks are constructed within them. Subsequently, four electrodes are installed at the edges of the composite plate, and boundary electrical resistance responses are collected when applying mechanical loads at various positions on the composite plate. Finally, a residual learning-based CNN model is proposed for the accurate localization and amplitude identification of the applied loads. Research results demonstrate that the trained CNN model can accurately and effectively determine both the load amplitude and position. The obtained localization error and amplitude error are 0.91 mm and 0.13 N, respectively, surpassing the reported error values in previous studies. The research presented here opens a new avenue for achieving highly accurate and efficient prediction of load location and amplitude, which can be widely applied in composite structural health monitoring.