A machine learning approach to characterise fabrication porosity effects on the mechanical properties of additively manufactured thermoplastic composites

Abstract

The investigation of the mechanical properties of additively manufactured (AM) composite has been the focus of several research over the past decades. However, testing constraints of time and cost have encouraged the exploration of more pragmatic methods such as machine learning (ML) for predicting these characteristics. This study builds on experimental investigations of the flexural, tensile, compressive, porosity, and hardness properties of 3D printed carbon fibre-reinforced polyamide (CF-PA) and carbon fibre-reinforced acrylonitrile butadiene styrene (CF-ABS) composites, proposing the application of ML for predicting these mechanical properties. A comprehensive comparative analysis of various machine learning approaches was executed, with a resultant accuracy ranging between 80 and 99%. The results unveiled the superior predictive performance of ensemble tree learners and the K-NN regressor algorithms when temperature and porosity are selected (based on correlation analysis) as predictors for material hardness and strength in tension, compression, and flexion. In particular, the model built on the extra-tree regressor algorithm demonstrated a remarkably robust fit, with R-squared evaluation scores of 0.9993 and 0.9996 for CF-PA and CF-ABS, respectively. This work develops a ML model that relates porosity to the other mechanical properties of AM composites and the prediction models’ exceptional accuracy, along with their precise alignment with experimental data, provide invaluable insights for the autonomous control and data-driven optimization of the structures.