Data from: Detection of defects during laser-powder interaction by acoustic emission sensors and signal characteristics
| dc.contributor.author | Song, Jun Young | |
| dc.contributor.author | Dass, Adrita | |
| dc.contributor.author | Moridi, Atieh | |
| dc.contributor.author | McLaskey, Gregory C. | |
| dc.date.accessioned | 2024-11-14T17:45:29Z | |
| dc.date.available | 2024-11-14T17:45:29Z | |
| dc.date.issued | 2024 | |
| dc.description | Please cite as: Jun Young Song, Adrita Dass, Atieh Moridi, and Gregory McLaskey. (2024) Data from: Detection of defects during laser-powder interaction by acoustic emission sensors and signal characteristics. [dataset] Cornell University eCommons Repository. https://doi.org/10.7298/hghq-s136 | |
| dc.description.abstract | These data are from Laboratory of Advanced Materials and Manufacturing at Cornell University Laboratory in support of the following research: Acoustic Emission (AE) sensing is an in-situ real-time nondestructive monitoring method proposed for Additive Manufacturing (AM) to detect defects such as cracks. Previous AE research in AM mainly focused on developing algorithms to automatically detect the defects from AE signals without understanding the physical mechanisms or the signal characteristics that could be used as identifiers. We study AE signals during a laser spot welding on a powder bed to clearly distinguish between different physical mechanisms using their signal characteristics. We identified specific signals associated with 1) tensile cracks from cooling, 2) a powder effect on the substrate, and 3) sudden thermal expansion of the substrate. We used the spectral ratio between high frequency (70–150 kHz) and low frequency (10–40 kHz) spectral amplitudes in the frequency domain to classify and differentiate the source types. We found that porosity due to insufficient energy density did not produce detectable AE signals. Using a ball drop calibration technique, we used AE signals to estimate the absolute sizes of the tensile cracks. Crack sizes ranged from 40 μm to 1 mm and were in general agreement with scanning electron microscope images of the fractures. We performed a line scanning test and successfully validated its potential for the application. Our findings provide a basic understanding of AE signal characteristics in AM, as well as the practical parameters used to separate the signal types. | |
| dc.description.sponsorship | National Science Foundation CAREER Award CMMI2046523, the NASA University Student Research Challenge Award 80NSSC21K0465 Office of Naval Research Young Investigator Award N00014-22-1-2420. Part of this work used the shared facilities at the Cornell Center for Materials Research, which is supported through the NSF MRSEC program (DMR-1719875). | |
| dc.identifier.doi | https://doi.org/10.7298/hghq-s136 | |
| dc.identifier.uri | https://hdl.handle.net/1813/116092 | |
| dc.publisher | Cornell University Library | |
| dc.relation.isreferencedby | Jun Young Song, Adrita Dass, Atieh Moridi, Gregory C. McLaskey, Detection of defects during laser-powder interaction by acoustic emission sensors and signal characteristics, Additive Manufacturing, Volume 82, 2024, 104035, ISSN 2214-8604, https://doi.org/10.1016/j.addma.2024.104035. | |
| dc.relation.isreferencedbyuri | https://doi.org/10.1016/j.addma.2024.104035 | |
| dc.rights | CC0 1.0 Universal | en |
| dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | |
| dc.subject | additive manufacturing | |
| dc.subject | 3D printing | |
| dc.title | Data from: Detection of defects during laser-powder interaction by acoustic emission sensors and signal characteristics | |
| dc.type | dataset |