In-process thermal imaging to detect internal features and defects in fused filament fabrication
In-process thermal imaging to detect internal features and defects in fused filament fabrication
复制标题
过程中热成像可检测熔丝制造中的内部特征和缺陷
DOI：
10.1007/s00170-023-12535-2
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Gupta, Nikhil
中科院分区：
文献类型：
--
作者：
AbouelNour, Youssef;Gupta, Nikhil
这项工作的目的是识别和测量原位嵌入功能的部件制造与熔丝制造（FFF）3D打印机。在实施了由光学和热成像相机组成的监控系统后，通过数据分析确定了系统的效率，即自动缺陷检测的效率。与我们以前的工作，其中涉及大量的随机嵌入的表面下的缺陷的检测，本研究确定了各种尺寸，几何形状和深度的缺陷印刷在一个矩形条。评估某些层之间的温差或ΔT，以确定其对检测嵌入特征和内部空隙的重要性。发现嵌入特征内的空隙的最终层与后续层之间的Δ T随着空隙尺寸的减小而增大。形成层和后续层之间的Δ T随着空隙尺寸的减小而减小。此外，当嵌入特征几何结构由3层空隙组成时，它们在形成层和后续层之间记录了更高的Δ T，这表明嵌入特征内的更大空隙或多层缺陷导致更高的形成层温度。总体而言，实时图像采集、图像处理和数据关联被证明可以有效地检测大型数据集中的异常。
The objective of this work is to identify and measure in situ the embedded features in parts manufactured with a fused filament fabrication (FFF) 3D printer. After implementing the monitoring system consisting of optical and thermal cameras, the efficiency of the system is determined in terms of efficacy for automated defect detection through data analysis. In contrast to our previous work, which involved the detection of a large number of randomly embedded sub-surface defects, this study identifies defects of various sizes, geometries, and depths printed in a rectangular strip. Temperature differences, or ΔT, between certain layers are evaluated to determine their significance to the detection of embedded features and internal voids. ΔTbetween the final layer of a void within the embedded feature and the subsequent layer was found to increase as void size decreased. ΔTbetween the formation layer and the subsequent layer decreased as void size decreased. Additionally, embedded feature geometries registered higher ΔTbetween formation layer and the subsequent layer when they consisted of 3-layer voids, which indicates that larger voids, or multilayer defects, within embedded features led to higher formation layer temperatures. Overall, real-time image acquisition, image processing, and data correlation was demonstrated to effectively detect abnormalities in large datasets.
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