Prediction of Coating Adhesion on Laser-Cleaned Metal Surfaces of Battery Cells Using Hyperspectral Imaging and Machine Learning

Abstract

Electric vehicles are shaping the future of the automotive industry. The traction battery is one of the most important components of electric cars. To ensure that the battery operates safely, it is essential to physically and electrically separate the cells facing each other. Coating a cell with varnish helps achieve this goal. Current studies use a destructive method on a sampling basis, the cross-cut test, to investigate the coating quality. In this paper, we present a fast, nondestructive and inline alternative based on hyperspectral imaging and artificial intelligence. Therefore, battery cells are measured with hyperspectral cameras in the visible and near-infrared (VNIR and NIR) parts of the electromagnetic spectrum before and after cleaning then coated and finally subjected to cross-cut test to estimate coating adhesion. During the cross-cut test, the cell coating is destroyed. This work aims to replace cross-cut tests with hyperspectral imaging (HSI) and machine learning to achieve continuous quality control, protect the environment, and save costs. Therefore, machine learning models (logistic regression, random forest, and support vector machines) are used to predict cross-cut test results based on hyperspectral data. We show that it is possible to predict with an accuracy of ~75% whether problems with coating adhesion will occur. Hyperspectral measurements in the near-infrared part of the spectrum yielded the best results. The results show that the method is suitable for automated quality control and process control in battery cell coating, but still needs to be improved to achieve higher accuracies.

Prediction of the Quality of Thermally Sprayed Copper Coatings on Laser-Structured CFRP Surfaces Using Hyperspectral Imaging

“…Thus, the combination of laser technology, hyperspectral imaging, in conjunction with machine learning enables an environmentally friendly and sustainable manufacturing process. Beyond the application of thermal coating of laser-processed CFRP samples, the present study shows that hyperspectral imaging in combination with machine learning methods makes relevant and otherwise difficult-to-access surface properties obtainable, which has also been shown in other studies (see [31]). This approach opens up completely new approaches for industrial quality control.…”

“…In addition, a long-pass filter with a cut-off wavelength of 532 nm (LP03-532RE, Semrock, USA) was placed in front of the camera to block the laser light. A detailed description of the Laser HSI measurement system can be found in Gruber et al [31]. The movement of the samples was controlled by a linear stage (VT 80, PI Micos, Eschbach, Germany) in both systems.…”

“…Machine learning for the evaluation of hyperspectral data is used, for example, for agricultural and medical applications [29,30]. For the prediction of adhesion properties of coatings using hyperspectral data and machine learning, there are currently very few publications [31].…”

Raman spectroscopy and hyper-spectral imaging for quality control of CIGS solar cells

“…The movement of the samples in both systems is controlled using a linear stage from PI Micos (VT 80, PI Micos, Eschbach, Germany). A detailed description of the Laser-HSI measurement system can be found in Gruber et al 14 All measurements were carried out at a distance of 300 mm. The VNIR HSI measurement had a field of view of about 100 mm and a pixel resolution of approximately 400 µm.…”