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Abstract Additive manufacturing (AM) is now widely used for research and industrial production. The benchmark data for mechanical properties of additively manufactured specimens is very useful for many communities. This data article presents a tensile testing dataset of ASTM D638 size specimens without and with embedded internal geometrical features printed using polylactic acid (PLA) in a Fused Filament Fabrication (FFF) additive manufacturing process. The added features can mimic defects of various shapes and sizes. This work is a supplement to the published research articleAssisted defect detection by in-process monitoring of additive manufacturing using optical imaging and infrared thermography(Additive Manufacturing, 2023, 103483). The printed specimens were tensile tested. Stress-strain graphs were developed and used to calculate the mechanical properties such as ultimate tensile strength (UTS) and strain at UTS. The mechanical properties, the correlations between mechanical properties and size, shape and location of geometrical features (defects), and the trends in mechanical properties can be useful in benchmarking the results of other researchers. 

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.