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

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.

Keywords:

• additive manufacturing
• machine learning
• 3D-printer
• interference detection
• data processing system
• feature engineering

Acknowledgments

The authors thank Roman Kern (Know-Center) and Alexander Stocker (VIRTUAL VEHICLE Research Center) for their valuable comments and feedback.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 https://www.sparkfun.com/datasheets/Components/ADXL330_0.pdf.

2 https://www.labfacility.com/media/attachment/file/pdfs/data-sheet-pt100-pt1000-thin-film-detectors.pdf.

3 https://www.cs.waikato.ac.nz/ml/weka/.

4 https://www.cs.waikato.ac.nz/ml/weka/arff.html.

5 https://commons.apache.org/proper/commons-math/userguide/stat.html.

6 http://weka.sourceforge.net/doc.dev/weka/attributeSelection/package-summary.html.

7 http://weka.sourceforge.net/doc.dev/weka/classifiers/rules/OneR.html.

8 The library needs to be downloaded separately: https://mvnrepository.com/artifact/nz.ac.waikato.cms.weka/LibLINEAR.

Additional information

Funding

The work was supported by the project Power Semiconductor and Electronics Manufacturing 4.0 (Semi40), under grant agreement number 692466. The project is co-funded by grants from Austria, Germany, Italy, France, Portugal and – Electronic Component System for European Leadership Joint Undertaking (ECSEL JU). VIRTUAL VEHICLE Research Center is funded within the COMET – Competence Centers for Excellent Technologies – program by the Austrian Federal Ministry for Transport, Innovation and Technology (bmvit), the Austrian Federal Ministry of Digital and Economic Affairs (bmdw), the Austrian Research Promotion Agency (FFG), the province of Styria and the Styrian Business Promotion Agency (SFG). The COMET program is administrated by FFG. The Know-Center is funded within the Austrian COMET Program – Competence Centers for Excellent Technologies – under the auspices of the Austrian Federal Ministry of Transport, Innovation and Technology, the Austrian Federal Ministry of Digital and Economic Affairs and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency FFG.