  We propose a new sampling strategy, called smart active sapling, for quality
inspections outside the production line. Based on the principles of active
learning a machine learning model decides which samples are sent to quality
inspection. On the one hand, this minimizes the production of scrap parts due
to earlier detection of quality violations. On the other hand, quality
inspection costs are reduced for smooth operation.
