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An integrated manifold learning approach for high-dimensional data feature extractions and its applications to online process monitoring of additive manufacturing
IISE Transactions ( IF 2.0 ) Pub Date : 2021-01-11 , DOI: 10.1080/24725854.2020.1849876
Chenang Liu 1 , Zhenyu (James) Kong 2 , Suresh Babu 3, 4 , Chase Joslin 4 , James Ferguson 5
Affiliation  

Abstract

As an effective dimension reduction and feature extraction technique, manifold learning has been successfully applied to high-dimensional data analysis. With the rapid development of sensor technology, a large amount of high-dimensional data such as image streams can be easily available. Thus, a promising application of manifold learning is in the field of sensor signal analysis, particular for the applications of online process monitoring and control using high-dimensional data. The objective of this study is to develop a manifold learning-based feature extraction method for process monitoring of Additive Manufacturing (AM) using online sensor data. Due to the non-parametric nature of most existing manifold learning methods, their performance in terms of computational efficiency, as well as noise resistance has yet to be improved. To address this issue, this study proposes an integrated manifold learning approach termed multi-kernel metric learning embedded isometric feature mapping (MKML-ISOMAP) for dimension reduction and feature extraction of online high-dimensional sensor data such as images. Based on the extracted features with the utilization of supervised classification and regression methods, an online process monitoring methodology for AM is implemented to identify the actual process quality status. In the numerical simulation and real-world case studies, the proposed method demonstrates excellent performance in both prediction accuracy and computational efficiency.



中文翻译:

一种用于高维数据特征提取的集成流形学习方法及其在增材制造在线过程监控中的应用

摘要

作为一种有效的降维和特征提取技术,流形学习已成功应用于高维数据分析。随着传感器技术的飞速发展,图像流等海量高维数据可以轻松获取。因此,流形学习的一个有前途的应用是在传感器信号分析领域,特别是对于使用高维数据的在线过程监测和控制的应用。本研究的目的是开发一种基于流形学习的特征提取方法,用于使用在线传感器数据的增材制造 (AM) 过程监控。由于大多数现有流形学习方法的非参数性质,它们在计算效率和抗噪性方面的性能还有待提高。为了解决这个问题,本研究提出了一种称为多核度量学习嵌入式等距特征映射(MKML-ISOMAP)的集成流形学习方法,用于在线高维传感器数据(如图像)的降维和特征提取。基于提取的特征,利用监督分类和回归方法,实施了一种用于 AM 的在线过程监控方法,以识别实际的过程质量状态。在数值模拟和实际案例研究中,所提出的方法在预测精度和计算效率方面都表现出优异的性能。本研究提出了一种称为多核度量学习嵌入式等距特征映射 (MKML-ISOMAP) 的集成流形学习方法,用于在线高维传感器数据(如图像)的降维和特征提取。基于利用监督分类和回归方法提取的特征,实施了一种用于 AM 的在线过程监控方法,以识别实际的过程质量状态。在数值模拟和实际案例研究中,所提出的方法在预测精度和计算效率方面都表现出优异的性能。本研究提出了一种称为多核度量学习嵌入式等距特征映射 (MKML-ISOMAP) 的集成流形学习方法,用于在线高维传感器数据(如图像)的降维和特征提取。基于利用监督分类和回归方法提取的特征,实施了一种用于 AM 的在线过程监控方法,以识别实际的过程质量状态。在数值模拟和实际案例研究中,所提出的方法在预测精度和计算效率方面都表现出优异的性能。实施了 AM 的在线过程监控方法,以识别实际过程质量状态。在数值模拟和实际案例研究中,所提出的方法在预测精度和计算效率方面都表现出优异的性能。实施了 AM 的在线过程监控方法,以识别实际过程质量状态。在数值模拟和实际案例研究中,所提出的方法在预测精度和计算效率方面都表现出优异的性能。

更新日期:2021-01-11
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