Machine Learning Approach towards Quality Control of Aerosol-Jet Printed Polymer Optical Waveguides Material

Hamjah MK, Thielen N, Hagelloch JE, Franke J (2021)

Publication Type: Conference contribution

Publication year: 2021

Publisher: Institute of Electrical and Electronics Engineers Inc.

Conference Proceedings Title: TENSYMP 2021 - 2021 IEEE Region 10 Symposium

Event location: Jeju KR

ISBN: 9781665400268

DOI: 10.1109/TENSYMP52854.2021.9550899

Abstract

In this work, a Machine Learning approach to predict the quality of the aerosol jet printed polymer optical waveguides material is presented. For the acquisition of necessary data, a video camera system was integrated into the aerosol jet printing. An image processing tool was developed to extract printed line width and to map to the process data of the printing. For modeling, an Automated Machine Learning framework was utilized to reduce development time for a proof of concept. The final ML model reached R2 score of 74 % in predicting line width and surpassed the statistical approaches.

Authors with CRIS profile

Mohd Khairulamzari Hamjah Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Nils Thielen Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Jörg Franke Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik

How to cite

APA:

Hamjah, M.K., Thielen, N., Hagelloch, J.E., & Franke, J. (2021). Machine Learning Approach towards Quality Control of Aerosol-Jet Printed Polymer Optical Waveguides Material. In TENSYMP 2021 - 2021 IEEE Region 10 Symposium. Jeju, KR: Institute of Electrical and Electronics Engineers Inc..

MLA:

Hamjah, Mohd Khairulamzari, et al. "Machine Learning Approach towards Quality Control of Aerosol-Jet Printed Polymer Optical Waveguides Material." Proceedings of the 2021 IEEE Region 10 Symposium, TENSYMP 2021, Jeju Institute of Electrical and Electronics Engineers Inc., 2021.

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