A convolutional neural network (CNN) guided Bayesian optimisation framework is introduced to strategically maximise the surface to volume ratio of 3D printed lattice supercapacitors. We applied Bayesian optimisation on printing parameters to exploit regions where uniform and narrow lines are printed. A line shape classifying CNN model guided the optimiser’s search space to straight-line printed regions, minimising optimisation time and cost. An automatic scoring method allowed each iteration to be conducted within two minutes with accurate and precise measurements. The optimisation process has been demonstrated with graphene oxide (GO) and poly(3,4-ethylenedioxythiophene):polystyrene sulphonate (PEDOT:PSS) inks. The results were compared to the parameters that follow the conventional methodologies of direct ink writing (DIW) 3D printing. For each printed line of GO and PEDOT:PSS inks, irregularities decreased by 61.8% and 18.9% and average widths decreased by 39.0% and 28.6%. PEDOT:PSS lattice supercapacitor printed using optimised result showed a 151.0% increase in specific capacitance.
|Journal||Virtual and Physical Prototyping|
|Publication status||Published - 2023|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea Government(MSIT) (No. 2020R1A2C3013158) and the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE)(No. 20193310100030, Development of high efficient F-class gas turbine hot component by controlling and applying Design for Additive Manufacturing).
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Modelling and Simulation
- Computer Graphics and Computer-Aided Design
- Industrial and Manufacturing Engineering