Yield and quality improvement is of paramount importance to any manufacturing company. One of the ways of improving yield is through discovery of the root causal factors affecting yield. We propose the use of data-driven interpretable causal models to identify key factors affecting yield. We focus on factors that are measured in different stages of production and testing in the manufacturing cycle of a product. We apply causal structure learning techniques on real data collected from this line. Specifically, the goal of this work is to learn interpretable causal models from observational data produced by manufacturing lines. Emphasis has been given to the interpretability of the models to make them actionable in the field of manufacturing. We highlight the challenges presented by assembly line data and propose ways to alleviate them.We also identify unique characteristics of data originating from assembly lines and how to leverage them in order to improve causal discovery. Standard evaluation techniques for causal structure learning shows that the learned causal models seem to closely represent the underlying latent causal relationship between different factors in the production process. These results were also validated by manufacturing domain experts who found them promising. This work demonstrates how data mining and knowledge discovery can be used for root cause analysis in the domain of manufacturing and connected industry.
翻译:提高产量的方法之一是发现影响产量的根本原因因果因素。我们提议使用数据驱动的可解释因果模型,以确定影响产量的关键因素。我们注重在产品制造周期的不同生产和测试阶段衡量的因素。我们根据从该行收集到的真实数据采用因果结构学习技术。具体地说,这项工作的目标是从制造线产生的观察数据中学习可解释的因果模型。强调模型的可解释性,使其在制造领域可操作。我们强调组装线数据带来的挑战,并提出减轻挑战的方法。我们还查明组装线数据的独特特点,以及如何利用这些特点来改进因果发现。因果结构学习的标准评价技术表明,所学的因果模型似乎密切体现了生产过程中不同因素之间的潜在因果关系。这些结果也得到了制造领域专家的验证,他们认为这些结果很有希望。这项工作表明,数据挖掘和知识发现如何用于制造业和关联工业领域的根源原因分析。