Journal article 989 views 443 downloads
An improved methodology of melt pool monitoring of direct energy deposition processes
,
Mark Sutcliffe,
David Carswell,
Carl Hauser,
Josh Barras
Optics & Laser Technology, Volume: 127, Start page: 106194
Swansea University Author:
Robert Lancaster
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DOI (Published version): 10.1016/j.optlastec.2020.106194
Abstract
Additive manufacturing processes have previously benefited from the introduction of melt pool dimensioning systems. These typically measure melt pool width by performing binary thresholds and highlighting edges using common edge detection algorithms. Melt pool monitoring systems have been successful...
| Published in: | Optics & Laser Technology |
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| ISSN: | 0030-3992 |
| Published: |
Elsevier BV
2020
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa53716 |
| Abstract: |
Additive manufacturing processes have previously benefited from the introduction of melt pool dimensioning systems. These typically measure melt pool width by performing binary thresholds and highlighting edges using common edge detection algorithms. Melt pool monitoring systems have been successfully used to develop control systems and enhance process understanding. This paper presents an improved machine vision technique to enhance images in melt pool monitoring systems. Enhanced images contain features that indicate true melt pool edges. The research highlights potential flaws in more established emissivity-based image processing algorithms and a new image processing technique is developed. The new technique produced improved accuracy and performed melt pool measurements independent of emissivity values. |
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| Keywords: |
Direct energy deposition, Melt pool monitoring, Machine vision, Image processing |
| Funders: |
UKRI, EP/H022309/1 |
| Start Page: |
106194 |