论文标题
基于深度学习的管道，用于工业绑定过程中的异常检测和质量增强
Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes
论文作者
论文摘要
异常检测描述了发现与正常值空间不同的异常状态，实例或数据点的方法。工业过程是一个领域，需要在其中找到异常的数据实例以提高质量。但是，主要的挑战是在这种环境中没有标签。本文有助于以数据为中心的工业生产中人工智能的方式。使用用于汽车组件的添加剂制造的用例，我们提出了基于深度学习的图像处理管道。此外，我们将域随机化和合成数据的概念整合在循环中，这显示了深度学习进展及其在现实世界中的工业生产过程中的桥接结果。
Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space. Industrial processes are a domain where predicitve models are needed for finding anomalous data instances for quality enhancement. A main challenge, however, is absence of labels in this environment. This paper contributes to a data-centric way of approaching artificial intelligence in industrial production. With a use case from additive manufacturing for automotive components we present a deep-learning-based image processing pipeline. Additionally, we integrate the concept of domain randomisation and synthetic data in the loop that shows promising results for bridging advances in deep learning and its application to real-world, industrial production processes.
