Skip navigation

Machine learning for additive manufacturing of electronics

Machine learning for additive manufacturing of electronics

Stoyanov, Stoyan ORCID: 0000-0001-6091-1226 and Bailey, Christopher ORCID: 0000-0002-9438-3879 (2017) Machine learning for additive manufacturing of electronics. In: Proceedings 2017 40th International Spring Seminar on Electronics Technology (ISSE). IEEE. ISBN 978-1-5386-0583-7 ISSN 2161-2536 (Online) (doi:https://doi.org/10.1109/ISSE.2017.8000936)

[img]
Preview
PDF (Author Accepted Manuscript)
20232 STOYANOV_Machine_Learning_for_Additive_Manufacturing_2017.pdf - Accepted Version

Download (866kB) | Preview

Abstract

Quality of electronic products fabricated with additive manufacturing (AM) techniques such as 3D inkjet printing can be assured by adopting pro-active predictive models for process condition monitoring instead of using conventional post-manufacture assessment techniques. This paper details a model-based approach, and associated machine learning algorithms, which can be used to achieve and maintain optimal product quality during production runs and to realise model predictive process control (MPC). The investigated data-driven prognostics based on state-space modelling of the dynamic behaviour of 3D inkjet printing for electronics manufacturing is new and makes it an original contribution. 3D printing of conductive lines for electronic circuits is a main targeted application, and is used to demonstrate and validate the prognostics capability of machine learning models developed from measured process data. The results show that, for moderately non-linear dynamics of the 3D-Printing process, state-space models can inform on the expected process trends (states) and related product quality characteristics even over large prediction horizons. The models can also support the realisation of model predictive process control for optimal target performance.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings 2017 40th International Spring Seminar on Electronics Technology (ISSE)
Additional Information: Conference held from 10-14 May 2017, Sofia, Bulgaria.
Uncontrolled Keywords: 3D printing; electronics manufacture; machine learning; condition based monitoring; prognostics
Subjects: Q Science > QA Mathematics
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Centre for Numerical Modelling & Process Analysis (CNMPA)
Faculty of Architecture, Computing & Humanities > Centre for Numerical Modelling & Process Analysis (CNMPA) > Computational Mechanics & Reliability Group (CMRG)
Faculty of Architecture, Computing & Humanities > Department of Mathematical Sciences
Last Modified: 13 Mar 2019 11:35
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/20232

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics