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An improved methodology of melt pool monitoring of direct energy deposition processes

Robert Sampson, Robert Lancaster Orcid Logo, Mark Sutcliffe, David Carswell, Carl Hauser, Josh Barras

Optics & Laser Technology, Volume: 127, Start page: 106194

Swansea University Author: Robert Lancaster Orcid Logo

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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...

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Published in: Optics & Laser Technology
ISSN: 0030-3992
Published: Elsevier BV 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa53716
first_indexed 2020-03-03T19:37:42Z
last_indexed 2020-07-05T19:16:06Z
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spelling 2020-07-05T15:10:10.8780154 v2 53716 2020-03-03 An improved methodology of melt pool monitoring of direct energy deposition processes e1a1b126acd3e4ff734691ec34967f29 0000-0002-1365-6944 Robert Lancaster Robert Lancaster true false 2020-03-03 EAAS 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. Journal Article Optics & Laser Technology 127 106194 Elsevier BV 0030-3992 Direct energy deposition, Melt pool monitoring, Machine vision, Image processing 1 7 2020 2020-07-01 10.1016/j.optlastec.2020.106194 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University UKRI, EP/H022309/1 2020-07-05T15:10:10.8780154 2020-03-03T15:05:20.2404690 Robert Sampson 1 Robert Lancaster 0000-0002-1365-6944 2 Mark Sutcliffe 3 David Carswell 4 Carl Hauser 5 Josh Barras 6 53716__16873__e2416804e49b4033989947670d7e0527.pdf 53716.pdf 2020-03-19T08:45:51.0917617 Output 1007061 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution License (CC-BY). true eng http://creativecommons.org/licenses/BY/4.0/
title An improved methodology of melt pool monitoring of direct energy deposition processes
spellingShingle An improved methodology of melt pool monitoring of direct energy deposition processes
Robert Lancaster
title_short An improved methodology of melt pool monitoring of direct energy deposition processes
title_full An improved methodology of melt pool monitoring of direct energy deposition processes
title_fullStr An improved methodology of melt pool monitoring of direct energy deposition processes
title_full_unstemmed An improved methodology of melt pool monitoring of direct energy deposition processes
title_sort An improved methodology of melt pool monitoring of direct energy deposition processes
author_id_str_mv e1a1b126acd3e4ff734691ec34967f29
author_id_fullname_str_mv e1a1b126acd3e4ff734691ec34967f29_***_Robert Lancaster
author Robert Lancaster
author2 Robert Sampson
Robert Lancaster
Mark Sutcliffe
David Carswell
Carl Hauser
Josh Barras
format Journal article
container_title Optics & Laser Technology
container_volume 127
container_start_page 106194
publishDate 2020
institution Swansea University
issn 0030-3992
doi_str_mv 10.1016/j.optlastec.2020.106194
publisher Elsevier BV
document_store_str 1
active_str 0
description 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.
published_date 2020-07-01T05:11:04Z
_version_ 1821471580800155648
score 11.0583515

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