A high-resolution and large field-of-view scanner for in-line characterization of powder bed defects during additive manufacturing
Powder bed defects are irregularities in the powder layer, which alter the energy input during the powder bed fusion process. As a result, they are directly responsible for the formation of flaws in the consolidated material, which cause quality and property variability in additive manufactured part...
Saved in:
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/90033 http://hdl.handle.net/10220/49359 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-90033 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-900332023-03-04T17:17:41Z A high-resolution and large field-of-view scanner for in-line characterization of powder bed defects during additive manufacturing Phuc, Le Tan Seita, Matteo School of Mechanical and Aerospace Engineering Singapore Centre for 3D Printing Engineering::Mechanical engineering Powder Bed Fusion In-situ Monitoring Powder bed defects are irregularities in the powder layer, which alter the energy input during the powder bed fusion process. As a result, they are directly responsible for the formation of flaws in the consolidated material, which cause quality and property variability in additive manufactured parts. Because of their small size and ubiquity across the powder bed, powder bed defects are difficult to detect and correct. In this work, we propose a new method to assess powder bed defects across the entire powder bed at the remarkable spatial resolution of ~5 μm. Our method relies on the integration of a contact image sensor taken from a flatbed document scanner to the powder re-coater module. Owing to the narrow depth-of-field of the sensor, we detect powder bed defects by identifying out-of-focus regions in the acquired scans using numerical image analysis techniques. Moreover, we show that we can assess the defects height (or depth) by quantifying the degree of “blurriness” in such regions. Our “powder bed scanner” is a rapid and cost-effective tool for in-line characterization of the powder bed quality. This technology may be instrumental to develop novel close loop strategies aimed at improving the consistency of additive manufactured parts. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) Published version 2019-07-16T03:45:56Z 2019-12-06T17:39:11Z 2019-07-16T03:45:56Z 2019-12-06T17:39:11Z 2018 Journal Article Phuc, L. T., & Seita, M. (2019). A high-resolution and large field-of-view scanner for in-line characterization of powder bed defects during additive manufacturing. Materials and Design, 164, 107562-. doi:10.1016/j.matdes.2018.107562 0261-3069 https://hdl.handle.net/10356/90033 http://hdl.handle.net/10220/49359 10.1016/j.matdes.2018.107562 en Materials and Design © 2018 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). 11 p. application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Mechanical engineering Powder Bed Fusion In-situ Monitoring |
spellingShingle |
Engineering::Mechanical engineering Powder Bed Fusion In-situ Monitoring Phuc, Le Tan Seita, Matteo A high-resolution and large field-of-view scanner for in-line characterization of powder bed defects during additive manufacturing |
description |
Powder bed defects are irregularities in the powder layer, which alter the energy input during the powder bed fusion process. As a result, they are directly responsible for the formation of flaws in the consolidated material, which cause quality and property variability in additive manufactured parts. Because of their small size and ubiquity across the powder bed, powder bed defects are difficult to detect and correct. In this work, we propose a new method to assess powder bed defects across the entire powder bed at the remarkable spatial resolution of ~5 μm. Our method relies on the integration of a contact image sensor taken from a flatbed document scanner to the powder re-coater module. Owing to the narrow depth-of-field of the sensor, we detect powder bed defects by identifying out-of-focus regions in the acquired scans using numerical image analysis techniques. Moreover, we show that we can assess the defects height (or depth) by quantifying the degree of “blurriness” in such regions. Our “powder bed scanner” is a rapid and cost-effective tool for in-line characterization of the powder bed quality. This technology may be instrumental to develop novel close loop strategies aimed at improving the consistency of additive manufactured parts. |
author2 |
School of Mechanical and Aerospace Engineering |
author_facet |
School of Mechanical and Aerospace Engineering Phuc, Le Tan Seita, Matteo |
format |
Article |
author |
Phuc, Le Tan Seita, Matteo |
author_sort |
Phuc, Le Tan |
title |
A high-resolution and large field-of-view scanner for in-line characterization of powder bed defects during additive manufacturing |
title_short |
A high-resolution and large field-of-view scanner for in-line characterization of powder bed defects during additive manufacturing |
title_full |
A high-resolution and large field-of-view scanner for in-line characterization of powder bed defects during additive manufacturing |
title_fullStr |
A high-resolution and large field-of-view scanner for in-line characterization of powder bed defects during additive manufacturing |
title_full_unstemmed |
A high-resolution and large field-of-view scanner for in-line characterization of powder bed defects during additive manufacturing |
title_sort |
high-resolution and large field-of-view scanner for in-line characterization of powder bed defects during additive manufacturing |
publishDate |
2019 |
url |
https://hdl.handle.net/10356/90033 http://hdl.handle.net/10220/49359 |
_version_ |
1759854540994117632 |