Using a one-dimensional convolutional neural network on FTIR spectroscopy to measure the thickness of composite plastic films

Abstract

Multilayer composite films have gained widespread application across a variety of industries and applications due to their unique properties and functionalities. The precise control of the thickness of each layer ensures that every layer contributes optimally to the desired attributes. In this study, a method using a one-dimensional convolutional neural network (1D-CNN) was developed to analyze the spectral data, five kinds of plastic films were assembled with different thickness combinations and measured with FTIR, which provided the data sets for 1D-CNN modeling. Compared to partial least squares (PLS) and fully connected neural network (FCNN) models, the 1D-CNN model performs better in accuracy of the thickness prediction, with respective root mean square error ($\mathrm{RMSE}$) values dropping from < 9.11 μm to < 0.31 μm for film thickness of 13–270 μm. The developed regression model was visualized using the gradient weighted class activation mapping (Grad-CAM) method for detailed analysis and selection of significant wavenumbers in order to build a more compact 1D-CNN model. Altogether, these results show that the proposed spectral 1D-CNN technique can measure the thickness of components in composite plastic films rapidly and accurately, and has the potential to improve efficiency and reduce the cost of instruments and calculations for spectral quantitation applications.