Machine learning enabled 3D printing parameter settings for desired mechanical properties

Abstract

Additive manufacturing facilitates the production of parts with tailored mechanical properties, yet achieving specific stress–strain responses remains a significant challenge due to the intricate relationship between printing parameters and material behaviour. This study introduces a novel approach utilising long short-term memory (LSTM) models to predict parameter configurations for material extrusion additive manufacturing (MEX) parts, aiming to meet specific stress–strain requirements. The proposed framework transforms raw tensile test data for LSTM compatibility, yielding a coefficient of determination of 0.8648 and a mean square error of 0.1348 in inverse prediction tasks. Additionally, validation with new data resulted in an error percentage below 2%. Our approach enables the efficient design of customised parts with accurate mechanical properties, reducing the need for extensive experimentation and allowing adaptation to various additive manufacturing processes.