Quality control is a crucial activity performed by manufacturing enterprises to ensure that their products meet quality standards and avoid potential damage to the brand's reputation. The decreased cost of sensors and connectivity enabled increasing digitalization of manufacturing. In addition, artificial intelligence enables higher degrees of automation, reducing overall costs and time required for defect inspection. This research compares three active learning approaches (with single and multiple oracles) to visual inspection. We propose a novel approach to probabilities calibration of classification models and two new metrics to assess the performance of the calibration without the need for ground truth. We performed experiments on real-world data provided by Philips Consumer Lifestyle BV. Our results show that explored active learning settings can reduce the data labeling effort by between three and four percent without detriment to the overall quality goals, considering a threshold of p=0.95. Furthermore, we show that the proposed metrics successfully capture relevant information otherwise available to metrics used up to date only through ground truth data. Therefore, the proposed metrics can be used to estimate the quality of models' probability calibration without committing to a labeling effort to obtain ground truth data.
翻译:质量控制是制造业企业为确保产品达到质量标准并避免对品牌声誉造成潜在损害而开展的一项关键活动。传感器和连通成本的降低使得制造业的数字化程度不断提高。此外,人工智能可以提高自动化程度,降低缺陷检查所需的总体成本和时间。这项研究将三种积极的学习方法(单一和多种神器)与目视检查相比较。我们提出了一种新颖的方法,用于对分类模型进行概率校准,以及两个用于评估校准性能的新指标,而不需要地面真相。我们进行了实际世界数据实验,我们探索了积极的学习环境,这可以在不影响总体质量目标的情况下,将数据标记工作减少3%至4%。此外,我们指出,拟议的指标成功地收集了可用于仅通过地面真相数据使用的最新指标的相关资料。因此,拟议的指标可以用来评估模型概率校准的质量,而不必承诺为获得地面真相数据作标记。