Matthias Hirsch
Assessing the capability of in-situ nondestructive analysis during layer based additive manufacture
Hirsch, Matthias; Patel, Rikesh; Li, Wenqi; Guan, Guangying; Leach, Richard K.; Sharples, Steve D.; Clare, Adam T.
Authors
RIKESH PATEL RIKESH.PATEL@NOTTINGHAM.AC.UK
Assistant Professor
WENQI LI Wenqi.Li@nottingham.ac.uk
Senior Research Fellow
Guangying Guan
RICHARD LEACH RICHARD.LEACH@NOTTINGHAM.AC.UK
Chair in Metrology
Steve D. Sharples
ADAM CLARE adam.clare@nottingham.ac.uk
Professor of Manufacturing Engineering
Abstract
Unlike more established subtractive or constant volume manufacturing technologies, additive manufacturing methods suffer from a lack of in-situ monitoring methodologies which can provide informationrelating to process performance and the formation of defects. In-process evaluation for additive manufacturing is becoming increasingly important in order to assure the integrity of parts produced in this way. This paper addresses the generic performance of inspection methods suitable for additive manufacturing. Key process and measurement parameters are explored and the impacts these have upon production rates are defined. Essential working parameters are highlighted, within which the spatial opportunity and temporal penalty for measurement allow for comparison of the suitability of different nondestructive evaluation techniques. A new method of benchmarking in-situ inspection instruments and characterising their suitability for additive manufacturing processes is presented to act as a design tool to accommodate end user requirements. Two inspection examples are presented: spatially resolved acoustic spectroscopy and optical coherence tomography for scanning selective laser melting and selective laser sintering parts, respectively. Observations made from the analyses presented show that the spatial capability arising from scanning parameters affects the temporal penalty and hence impact upon production rates. A case study, created from simulated data, has been used to outline the spatial performance of a generic nondestructive evaluation method and to show how a decrease in data capture resolution reduces the accuracy of measurement.
Citation
Hirsch, M., Patel, R., Li, W., Guan, G., Leach, R. K., Sharples, S. D., & Clare, A. T. (2017). Assessing the capability of in-situ nondestructive analysis during layer based additive manufacture. Additive Manufacturing, 13, 135-142. https://doi.org/10.1016/j.addma.2016.10.004
Journal Article Type | Article |
---|---|
Acceptance Date | Oct 17, 2016 |
Online Publication Date | Oct 20, 2016 |
Publication Date | Jan 30, 2017 |
Deposit Date | Nov 29, 2016 |
Publicly Available Date | Nov 29, 2016 |
Journal | Additive Manufacturing |
Print ISSN | 2214-8604 |
Electronic ISSN | 2214-8604 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 13 |
Pages | 135-142 |
DOI | https://doi.org/10.1016/j.addma.2016.10.004 |
Keywords | Nondestructive evaluation, Additive manufacturing, Process control, In-situ analysis |
Public URL | https://nottingham-repository.worktribe.com/output/823156 |
Publisher URL | https://www.sciencedirect.com/science/article/pii/S2214860416301877 |
Files
1-s2.0-S2214860416301877-main - capability.pdf
(1.9 Mb)
PDF
Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0
You might also like
Stochastic design for additive manufacture of true biomimetic populations
(2022)
Journal Article
On the thermomechanical aging of LPBF alloy 718
(2022)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: digital-library-support@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search