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  1. 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. ΔT between the final layer of a void within the embedded feature and the subsequent layer was found to increase as void size decreased. ΔT between the formation layer and the subsequent layer decreased as void size decreased. Additionally, embedded feature geometries registered higher ΔT between 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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    Free, publicly-accessible full text available December 1, 2024
  2. Additive manufacturing (AM) systems such as 3-D printers use inexpensive microcontrollers that rarely feature cybersecurity defenses. This is a risk, especially given the rising threat landscape within the larger digital manufacturing domain. In this work, we demonstrate this risk by presenting the design and study of a malicious Trojan (the FLAW3D bootloader) for AVR-based Marlin-compatible 3-D printers (>100 commercial models). We show that the Trojan can hide from programming tools, and even within tight design constraints (less than 1.7 KB in size), it can compromise the quality of additively manufactured prints and reduce tensile strengths by up to 50%. 
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  3. Kushvaha, V. ; Sanjay, M. R. ; Madhushri, P. ; Siengchin, S. (Ed.)
    The products manufactured by the additive manufacturing (AM) methods have unique signatures in their microstructures due to the layer by layer manufacturing. Machine learning of microstructures of the printed sample can help in interpreting these signatures and the patterns can be used for either determining the authenticity of the product or for reverse engineering. In this work, specimens of 3D printed glass fiber reinforced polymer (GFRP) composite materials are subjected to imaging and machine learning in order to rebuild the tool path information. Since composites require significant research and development effort, the possibility of rebuilding the tool path by ML methods presents a significant vulnerability for intellectual property. The ML methods require a large training dataset and can be efficient in processing tomography datasets. Two kinds of artificial neural networks with three different algorithms are introduced in this work and their results are compared. A 3D printed GFRP specimen is imaged using a micro CT-scan and the images are processed using binarized statistical image features method for compression without compromising the microstructural information. The ML models are trained on this dataset and the results indicate that the ML is able to identify the printing tool path with accuracy. 
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