Machine learning for additive manufacturing of electronics

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