Smart Active Sampling to enhance Quality Assurance Efficiency
This addresses quality assurance efficiency for manufacturing industries, but appears incremental as it applies existing active learning principles to a specific domain.
The paper tackles the problem of inefficient quality inspections by proposing a smart active sampling strategy that uses active learning to select samples for inspection, resulting in minimized scrap production and reduced inspection costs.
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.