Detection of interferences in an additive manufacturing process: an experimental study integrating methods of feature selection and machine learning

Darko Stanisavljevic, David Cemernek, Heimo Gursch, Günter Christoph Urak, Gernot Christian Lechner

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Additive manufacturing becomes a more and more important technology for production, mainly driven by the ability to realise extremely complex structures using multiple materials but without assembly or excessive waste. Nevertheless, like any high-precision technology additive manufacturing responds to interferences during the manufacturing process. These interferences – like vibrations – might lead to deviations in product quality, becoming manifest for instance in a reduced lifetime of a product or application issues. This study targets the issue of detecting such interferences during a manufacturing process in an exemplary experimental setup. Collection of data using current sensor technology directly on a 3D-printer enables a quantitative detection of interferences. The evaluation provides insights into the effectiveness of the realised application-oriented setup, the effort required for equipping a manufacturing system with sensors, and the effort for acquisition and processing the data. These insights are of practical utility for organisations dealing with additive manufacturing: the chosen approach for detecting interferences shows promising results, reaching interference detection rates of up to 100% depending on the applied data processing configuration.
Original languageEnglish
Number of pages23
JournalInternational Journal of Production Research
Volume2019
Issue number57
DOIs
Publication statusE-pub ahead of print - 27 Nov 2019

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