论文标题
智能主动采样以提高质量保证效率
Smart Active Sampling to enhance Quality Assurance Efficiency
论文作者
论文摘要
我们提出了一种新的抽样策略，称为Smart Active Sapling，以在生产线之外进行质量检查。根据主动学习的原则，机器学习模型决定将哪些样本发送到质量检查。一方面，由于较早发现质量违规行为，这可以最大程度地减少废料零件的产生。另一方面，质量检查成本降低了，以进行平稳运行。
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.
