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.

中文翻译：

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使用机器学习预测激光诱导石墨碳的薄层电阻


虽然激光诱导石墨碳 (LIGC) 已用于在柔性基板上制造具有成本效益的导电碳，用于传感器和储能设备等应用，但预测通过 LIGC 制造的组件的电阻仍然具有挑战性。在本研究中，开发了一个基于机器学习的两步建模框架来预测使用 LIGC 制造的材料的薄层电阻。两步建模框架由分类和回归组成。首先，随机森林（RF）用于对成功和失败的试验进行分类。其次，使用极限梯度提升 (XGBoost)、RF、具有径向基函数的支持向量机、多元自适应样条回归和多层感知器来预测每次成功试验中的薄层电阻。此外，还分析了薄层电阻相对于单位面积激光能量的变化，以去除薄层电阻高的数据点。 XGBoost 还用于确定每个工艺参数的重要性。我们在刻有 LIGC 线 (1D) 和 LIGC 方块 (2D) 的实验中收集的数据集上演示了建模框架。对于一维数据集，RF 分类模型的准确率达到 95%。对于一维和二维数据集，比较研究表明 XGBoost 优于其他算法。 XGBoost 预测 LIGC 线和方块的方块电阻，MAPE 分别为 7.08% 和 8.75%。 XGBoost 还将激光分辨率视为最重要的参数。此外，实验结果表明，在合并一维和二维数据集的数据集上构建的模型比分别在一维和二维数据集上构建的模型的预测精度更低。 该建模框架允许人们在不同的激光加工条件下确定 LIGC 的薄层电阻，而无需进行昂贵且耗时的实验。