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 Urak and Gernot Lechner

International Journal of Production Research, 2020, vol. 58, issue 9, 2862-2884

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.

Date: 2020
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DOI: 10.1080/00207543.2019.1694719

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