Recent developments in in-situ monitoring and process control in Additive Manufacturing (AM), also known as 3D-printing, allows the collection of large amounts of emission data during the build process of the parts being manufactured. This data can be used as input into 3D and 2D representations of the 3D-printed parts. However the analysis and use, as well as the characterization of this data still remains a manual process. The aim of this paper is to propose an adaptive human-in-the-loop approach using Machine Learning techniques that automatically inspect and annotate the emissions data generated during the AM process. More specifically, this paper will look at two scenarios: firstly, using convolutional neural networks (CNNs) to automatically inspect and classify emission data collected by in-situ monitoring and secondly, applying Active Learning techniques to the developed classification model to construct a human-in-the-loop mechanism in order to accelerate the labeling process of the emission data. The CNN-based approach relies on transfer learning and fine-tuning, which makes the approach applicable to other industrial image patterns. The adaptive nature of the approach is enabled by uncertainty sampling strategy to automatic selection of samples to be presented to human experts for annotation.
翻译:Additive Manument (AM) 原地监测和工艺控制(Appitive Manument,又称3D打印)的近期发展,使得能够在制造部件的制造过程中收集大量排放数据,这些数据可以用作3D和2D对3D印刷部件的表示材料。然而,对这些数据的分析、使用和定性仍是一个人工过程。本文件的目的是建议使用机械学习技术来自动检查和注解AM过程中产生的排放数据,采用适应性人到流的方法。更具体地说,本文件将审视两种情况:第一,利用进化神经网络自动检查和分类现场监测所收集的排放数据;第二,将积极学习技术应用于已开发的分类模型,以构建一个人类在轨机制,以加快排放数据的标签过程。CNN方法依靠转移学习和微调,使方法适用于其他工业图像模式。这一方法的适应性在于采用不确定性取样战略,以便自动选择人类专家。