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Methodology for the in-process evaluation of software-based process failures in Selective Laser Melting machine tools

Methodology for the in-process evaluation of software-based process failures in Selective Laser Melting machine
 
: Pastl Pontes, Rodrigo
: Krüger, Jörg; Uhlmann, Eckart; Gomes, Oliveira

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Fulltext urn:nbn:de:0011-n-4976134 (4.8 MByte PDF)
MD5 Fingerprint: 87dff9ab612c7414d567a1cda02b0f08
Created on: 26.06.2019


Stuttgart: Fraunhofer Verlag, 2018, VI, 156 pp.
Zugl.: Berlin, TU, Diss., 2017
Berichte aus dem Produktionstechnischen Zentrum Berlin
ISBN: 978-3-8396-1350-4
English
Dissertation, Electronic Publication
Fraunhofer IPK ()
industrial chemistry & manufacturing technologies; other technologies & applied sciences; selective laser melting; condition monitoring; Industrie 4.0; machine quality evaluation; software failures; Maschinenbauingenieur; Fertigungstechniker; Produktionstechniker; Verfahrensingenieur; Qualitätsingenieur

Abstract
The intense use of Additive Manufacturing technologies, especially Selective Laser Melting (SLM), to produce complex functional components by several sectors of the industry evidenced that current machines are not able to ensure the product quality. The results are affected by failures occurred in manufacturing processes caused, for instance, by the controller software. Such failures influence either the time to manufacture a component or its final quality. Current system validation methodologies are not adapted to SLM machines and, therefore, they are not effective. Within this context, this dissertation focused on the development of a methodology to evaluate process failures originated by the software of SLM machines in order to measure the machine quality. For this purpose, a process map for the machine was described, Key Performance Indicators were designed, and the Conformance Timed Automata Effect Analysis (ConTEA) testing methodology was developed. By using ConTEA, models with functioning scenarios are created, the tests can be generated and applied to the SLM machine software, failures can be tracked and the machine quality can be assessed.

: http://publica.fraunhofer.de/documents/N-497613.html