An unsupervised online anomaly detection method for metal additive manufacturing processes via a statistical time-frequency domain algorithm

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Alvin Chen, Fotis Kopsaftopoulos, Sandipan Mishra
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引用次数: 0

Abstract

Anomalies often occur in metal additive manufacturing from processing inconsistencies and uncertainty. A robust fault detection system that uses sensor measurements such as melt pool imaging has the potential to improve part quality and save production time by anticipating print failure. Toward this goal, we develop and validate a fault detection technique using melt pool geometry-related measurements from an in situ near-infrared optical camera. This method is unsupervised and is trained on a small dataset, mitigating human error in classifying fault types, and reducing lead times for preparing training datasets. Furthermore, this method uses learned geometry-informed nominal behavior of the melt pool signal to make informed decisions on the process health. There are spatial-temporal characteristics embedded in the melt pool images, caused by the periodicity in the geometry-dependent raster pattern. These characteristics can be captured in the frequency domain using the signal spectrogram, a representation of the frequency content over time. Defects will appear in the spectrogram, disrupting the healthy spectral response. To quantify healthy spectrograms, we use principal component (PC) decomposition to extract the features of these spectrograms as a set of nominal basis vectors. Anomaly detection is then performed by calculating the error between the original and reconstructed spectrogram vector by projection of the spectrogram PCs onto the nominal basis. The reconstruction error for anomalous signals is larger than that from healthy signals, which is then used for fault detection. A one-tailed statistical test is used to determine the fault detection threshold for the reconstruction error signal. This method is tested on three raster patterns and performs better than a comparative time-series thresholding method. We demonstrate that this time-frequency algorithm can detect both temporal faults (which occur at a single time instant) and spatial faults (such as those introduced by an improper sintering), differentiating them from nominal operation.
基于统计时频域算法的金属增材制造过程无监督在线异常检测方法
在金属增材制造中,由于加工不一致和不确定性导致的异常经常发生。使用传感器测量(如熔池成像)的强大故障检测系统有可能通过预测打印故障来提高零件质量并节省生产时间。为了实现这一目标,我们开发并验证了一种故障检测技术,该技术使用来自现场近红外光学相机的熔池几何相关测量。该方法是无监督的,并且在小数据集上进行训练,减少了在分类故障类型时的人为错误,并减少了准备训练数据集的前置时间。此外,该方法使用学习到的几何信息的熔融池信号的标称行为来对过程健康做出明智的决策。由于几何相关光栅模式的周期性,熔池图像中嵌入了时空特征。这些特征可以在频域中使用信号谱图捕获,信号谱图表示频率内容随时间的变化。光谱图中会出现缺陷,破坏正常的光谱响应。为了量化健康频谱图,我们使用主成分(PC)分解来提取这些频谱图的特征作为一组标称基向量。然后,通过将谱图pc投影到标称基上,计算原始和重建谱图矢量之间的误差,进行异常检测。异常信号的重建误差大于正常信号的重建误差,然后将其用于故障检测。采用单侧统计检验确定重构误差信号的故障检测阈值。该方法在三种栅格模式上进行了测试,其性能优于比较时间序列阈值方法。我们证明了这种时频算法可以检测时间故障(发生在单个时间瞬间)和空间故障(例如由不当烧结引入的故障),将它们与名义操作区分开来。
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
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