A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing
Additive manufacturing has entered the phase of industrial adoption, for which its quality repeatability is of vital importance to industries where functional parts with consistent mechanical properties are desired. This concern will manifest with large scale implementation of such technology, affec...
Saved in:
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/154118 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-154118 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1541182021-12-18T20:12:11Z A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing Huang, De Jun Li, Hua School of Mechanical and Aerospace Engineering Singapore Centre for 3D Printing Engineering::Mechanical engineering Additive Manufacturing Powder Bed Fusion Additive manufacturing has entered the phase of industrial adoption, for which its quality repeatability is of vital importance to industries where functional parts with consistent mechanical properties are desired. This concern will manifest with large scale implementation of such technology, affecting not only the reliability of products but the reputation and profitability of a business. The root cause to this problem is obscure demanding a systematic approach to identify potential influencing parameters for better process control. In this article, the quality repeatability of laser powder bed fusion (L-PBF) technology, in terms of static mechanical properties of printed parts, was quantified using relative standard deviation, and a machine learning approach for root cause analysis was demonstrated. While most of the prior work focused on the effect of laser-related process parameters to part properties, this research emphasises on the downstream production parameters while keeping laser-related parameters fixed. It was found that the combinational effect of part location and post-chamber pressure drop heavily influences the quality of printed parts. A follow-up experiment with the new process control was able to produce parts with improved quality repeatability. This proves the effectiveness of the proposed approach for process control of L-PBF at large scale implementation. Economic Development Board (EDB) Nanyang Technological University Published version This work was supported by the Economic Development Board Singapore, Nanyang Technological University and Emerson Electric Co. 2021-12-15T08:09:41Z 2021-12-15T08:09:41Z 2021 Journal Article Huang, D. J. & Li, H. (2021). A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing. Materials and Design, 203, 109606-. https://dx.doi.org/10.1016/j.matdes.2021.109606 0261-3069 https://hdl.handle.net/10356/154118 10.1016/j.matdes.2021.109606 2-s2.0-85102040511 203 109606 en Materials and Design /© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). 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 Additive Manufacturing Powder Bed Fusion |
spellingShingle |
Engineering::Mechanical engineering Additive Manufacturing Powder Bed Fusion Huang, De Jun Li, Hua A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing |
description |
Additive manufacturing has entered the phase of industrial adoption, for which its quality repeatability is of vital importance to industries where functional parts with consistent mechanical properties are desired. This concern will manifest with large scale implementation of such technology, affecting not only the reliability of products but the reputation and profitability of a business. The root cause to this problem is obscure demanding a systematic approach to identify potential influencing parameters for better process control. In this article, the quality repeatability of laser powder bed fusion (L-PBF) technology, in terms of static mechanical properties of printed parts, was quantified using relative standard deviation, and a machine learning approach for root cause analysis was demonstrated. While most of the prior work focused on the effect of laser-related process parameters to part properties, this research emphasises on the downstream production parameters while keeping laser-related parameters fixed. It was found that the combinational effect of part location and post-chamber pressure drop heavily influences the quality of printed parts. A follow-up experiment with the new process control was able to produce parts with improved quality repeatability. This proves the effectiveness of the proposed approach for process control of L-PBF at large scale implementation. |
author2 |
School of Mechanical and Aerospace Engineering |
author_facet |
School of Mechanical and Aerospace Engineering Huang, De Jun Li, Hua |
format |
Article |
author |
Huang, De Jun Li, Hua |
author_sort |
Huang, De Jun |
title |
A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing |
title_short |
A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing |
title_full |
A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing |
title_fullStr |
A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing |
title_full_unstemmed |
A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing |
title_sort |
machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/154118 |
_version_ |
1720447119120138240 |