  With the rise of deep learning models in the field of computer vision, new
possibilities for their application in industrial processes proves to return
great benefits. Nevertheless, the actual fit of machine learning for highly
standardised industrial processes is still under debate. This paper addresses
the challenges on the industrial realization of the AI tools, considering the
use case of Laser Beam Welding quality control as an example. We use object
detection algorithms from the TensorFlow object detection API and adapt them to
our use case using transfer learning. The baseline models we develop are used
as benchmarks and evaluated and compared to models that undergo dataset scaling
and hyperparameter tuning. We find that moderate scaling of the dataset via
image augmentation leads to improvements in intersection over union (IoU) and
recall, whereas high levels of augmentation and scaling may lead to
deterioration of results. Finally, we put our results into perspective of the
underlying use case and evaluate their fit.
