Should I Sample it or Not? Improving Quality Assurance Efficiency Through Smart Active Sampling

The digital transformation provides industries with unparalleled opportunities for value creation. AI and Machine learning (AI/ML)-driven approaches for data analysis applied to the massive amounts of data steaming from industrial processes can lead to enhanced operation, costs reduction, and powerful decision-making strategies. In this paper we address the problem of Quality Assurance (QA) in industrial manufacturing. We propose Smart Active Sampling (SAS), a new QA sampling strategy for quality inspection outside the production line. Based on the principles of active learning, an AI/ML model trained for quality prediction decides which produced pieces or samples are sent to quality inspection, to further improve its own prediction accuracy. SAS reduces the production of scrap parts due to earlier detection of quality violations. By inspecting a much lower number of samples as compared to traditional random sampling approaches, SAS improves QA efficiency and cuts down quality inspection costs, resulting in an overall smoother operation. We elaborate on some of the challenges faced in smart sampling strategies for quality inspection, describe the main concepts behind SAS, and showcase its application in a real-world manufacturing QA use case, training an AI/ML model for product defect prediction. Compared to a standard random sampling strategy, widely applied today in industrial QA applications, SAS improves model prediction accuracy requiring a significantly lower number of inspected samples, up to five time less samples in the analyzed dataset.