Automated quality detection of resource efficient 3D printing
by Purvee Bhatia; Donald McCleeary; Nancy Diaz-Elsayed
International Journal of Sustainable Manufacturing (IJSM), Vol. 5, No. 2/3/4, 2022

Abstract: Image processing and machine learning were applied to detect production quality characteristics of parts printed via fused deposition modelling. The influence of the nozzle temperature, infill density, and feed rate on the surface roughness and energy consumption of the printed parts was analysed. The surface roughness of the printed parts was predicted using a fine tree machine learning model; the infill density and feed rate were positively correlated to energy consumption, while temperature had little effect on energy consumption. Process parameters for 3D printing are recommended to achieve the desired surface quality, while avoiding print failure and excess energy consumption.

Online publication date: Fri, 27-Oct-2023

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