How to characterize a NDT method for weld inspection in battery cell manufacturing using deep learning

Battery cells are central components of electric vehicles. It is important for automotive original equipment manufacturer (OEM) to utilize high-quality battery cells to ensure high performance and safety of their vehicles. This results in the high demand for quality control measures and inspection methods in battery cell manufacturing. Particular relevant features of battery cells are welds for the internal electrical contact. Failures of these welds are often the cause for battery defects in the field and scrap during production. Consequently, there is a strong need to evaluate all welds during manufacturing. However, there is no established method that allows a quick, comprehensive, and cheap inline measurement of the weld quality. This paper presents a new eddy current-based method for nondestructive testing of seam welds as well as a machine learning approach for its validation. A deep learning model has been trained on eddy current measurements to predict results from a reference inspection method, in this case computer tomography. The results prove that eddy current measurements can be used to replicate data acquired by computer tomography, which means that eddy current measurements could be a suitable candidate for nondestructive 100 % inline inspection. More general, this study demonstrates how machine learning may help to get deeper insights into measurement results and to validate new nondestructive testing techniques whose detailed features are yet unknown. The presented evaluation method enables understanding the capabilities and the limits of a new technique and to extract hidden features from the data. In terms of defect segmentation, the trained model applied to an eddy current test data set achieves an accuracy of 93.7 %. Furthermore, the usage of machine learning allows to perform evaluations on artificial product samples with specific defects and features, which avoids the costly production physical samples.