Predicting the sheet resistance of laser-induced graphitic carbon using machine learning

While laser-induced graphitic carbon (LIGC) has been used to fabricate cost-effective conductive carbon on flexible substrates for applications such as sensors and energy storage devices, predicting the resistance of the component fabricated via LIGC remains challenging. In this study, a two-step machine learning-based modeling framework is developed to predict the sheet resistance of the materials fabricated using LIGC. The two-step modeling framework consists of classification and regression. First, random forest (RF) is used to classify successful and failed trials. Second, extreme gradient boosting (XGBoost), RF, support vector machine with radial basis function, multivariate adaptive spline regression, and multilayer perceptron are used to predict the sheet resistance in each successful trial. In addition, an analysis of the change in sheet resistance with respect to laser energy per unit area is conducted to remove data points with high sheet resistance. XGBoost is also used to determine the importance of each process parameter. We demonstrate the modeling framework on datasets collected from experiments where LIGC lines (1D) and LIGC squares (2D) are engraved. For the 1D dataset, the RF classification model achieves a 95% accuracy. For both 1D and 2D datasets, a comparative study shows that XGBoost outperforms other algorithms. XGBoost predicts the sheet resistance of the LIGC lines and squares with a MAPE of 7.08% and 8.75%, respectively. XGBoost also identifies laser resolution as the most significant parameter. Moreover, experimental results show that models built on the dataset merging the 1D and 2D datasets result in lower prediction accuracy than those built on the 1D and 2D datasets separately. The modeling framework allows one to determine the sheet resistance of LIGC with varying laser processing conditions without conducting expensive and time-consuming experiments.