## 6 Discussion  
The prediction of the permeability and tortuosity of Toray GDL was successfully applied to segmented images of the micro-structure of real data. A re-scale of different image resolutions and domain sizes led to consistent results. Applied to real image data with unknown errors in the segmentation, overfitting effects were observed. In contrast to introductory examples [27], where the test dataset is usually of the same image quality as the training dataset, overfitting was not detected in the test data. Additional consistency checks were necessary in the case of the R3 series, where meaningless values (\(\tau<1\)) were predicted for the IP tortuosity. As in previous work [26], the Kozeny-Carman trend on the product \(\kappa\cdot\tau\) of both the permeability and tortuosity characteristics was applied to judge the consistency of the predicted results. Using real data, re-sizing and re-scaling was applied to the micro-structure, because the CNN requires a 3D input geometry of fixed size. For this purpose, two image series, R2 and R3, were available. The re-scaling from 1.797 \(\upmu\)m/px of the micro-structure to 1.5 \(\upmu\)m/px of the training data obviously did not hurt the accuracy; the features of the R2 series were satisfactorily predicted. That was not the case with the R3 series though. One difference between the R2 and R3 series is the small size of the sample in the y direction. The images of both series must be stacked upon each other in the z direction to achieve the training size. Only for the R3 series was this procedure applied also in the y direction. The inaccuracy of the R3 series was observed in both the TP and IP directions. Moreover, the images of R2 were taken from the channel side of the compression device setup. The R3 images were taken from the block side. Although it was observed by [PERSON] [35] that the complete MEA was deformed under the channel, the impact of deformation on the GDL micro-structure is expected to be lower on the block side than on the channel one. The high relevance of the GDL deformation under compressed flow fields was shown by [PERSON] et al. [43], who investigated the GDL compression under misaligned flow fields. The IP characteristics of the training data were only available for one direction [26]. This is absolutely sufficient, given of the stochastic nature of the underlying geometry model [32]. The geometries of the training data were homogeneously compressed. In contrast, the real data of the R2 and R3 series were inhomogeneously compressed [35]; according to Figure 6, there are regions under the channel and under the rib. In the arrangement of Figure 6, the  
Figure 14: Consistency of the predicted \(\kappa\cdot\tau\) values of the R3 data with the Kozeny–Carman (KC) trend. IP characteristics differ, along and perpendicular to the channel. However, this cannot be distinguished by a model that was trained for homogeneous compression. From the application's view, the permeability of the GDL on the flow field side, R2, is expected to be higher than that of the GDL on the block side, R3. This is indeed predicted by the ML model, but it must be conceded that the predictions of the R3 series possibly cannot be trusted because of the KC trend. Nevertheless, it must be noted that imaging errors could have occurred in different degrees of severity in both series. In this work, the KC trend was used to justify the consistency of the ML predictions. It may be that other methods are viable for evaluating the accuracy of predictions in case an application were to suffer from other effects than the training data. This work investigated the limitation of a CNN when the predictions are applied to images that have un-trained features. To justify the consistency of the prediction--once un-trained features have been identified--an additional criterion is to be applied. In this work, it was the KC trend for compressed porous material.