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  4. Self-learning calculation for selective laser melting
 
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Self-learning calculation for selective laser melting

Citation Link: https://doi.org/10.15480/882.1741
Publikationstyp
Journal Article
Date Issued
2018-03-21
Sprache
English
Author(s)
Rudolph, Jan-Peer  
Emmelmann, Claus  orcid-logo
Institut
Laser- und Anlagensystemtechnik G-2  
TORE-DOI
10.15480/882.1741
TORE-URI
http://tubdok.tub.tuhh.de/handle/11420/1744
Journal
Procedia CIRP  
Volume
67
Start Page
185
End Page
190
Citation
Procedia CIRP (67): 185-190 (2018)
Contribution to Conference
11th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME '17  
Publisher DOI
10.1016/j.procir.2017.12.197
Scopus ID
2-s2.0-85044654136
Publisher
Elsevier
Selective laser melting (SLM) is increasingly used in the industrial production of metallic parts. This creates the need for an efficient and
accurate quotation costing. The manufacturing costs of a part mainly result from the machine running time for coating and exposure. At the
time of the offer calculation the final orientation of the part in the build chamber and the composition of the build job are typically not known.
Addressing this need, this paper presents and evaluates different statistical based methods for an automated and self-learning calculation for SLM given a part’s CAD data.
Subjects
Calculation
Quotation costing
Self-learning
Selective laser melting (SLM)
Additive manufacturing (AM)
DDC Class
600: Technik
Lizenz
https://creativecommons.org/licenses/by-nc-nd/4.0/
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