2022/09/23 by Clemens Heistracher, Stefan A. Stricker, Heistracher, Clemens +7
Decision Sciences · Engineering · #Advanced Statistical Process Monitoring #FOS: Computer and information sciences #Industrial Vision Systems and Defect Detection #Machine Learning (cs.LG) #Mineral Processing and Grinding
paper · pdf · doi:10.48550/arxiv.2209.11464
openalex publication_date 2022/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.