Multi-material fabrication and compressive strength-optimization of reinforced-thermoset structures for mechanical power transmission

Reinforced polymers structures are widely used to fabricate intricate end-use parts due to their high durability, material-corrosion stability, low cost, and less weight. While extensive studies have focused on optimizing the compressive performance of fibre-reinforced structures; however, only a few have emphasised on compressive strength-based studies for reinforced multi-material structures to improve multi-functionality of end-use products. The study presents the universal compression strength-based predictive model employing material jetting technique which significantly optimizes the compressive performance of reinforced-thermoset structures precisely governing the material distribution of polymers controlling filler shape, filler volume concentration, and filler position considering full factorial Design of Experiments approach. Numerous prefabricating factors and fabricating constraints are considered to improve the accuracy of the model and identify complex correlations that are not apparent through empirical model. The compressive strength-prediction model is developed using machine learning algorithms where the Genetic Algorithm optimizes the compressive strength outcomes. The implementation of elliptical-reinforced filler networks, characterised by a Filler Aspect Ratio of 0.2938 and a Filler Volume Concentration of 30%, enhances the compressive strength by 146.41%. FESEM is employed to characterize the microstructural morphology at the interface of multi-material specimens. This study exhibits the replacement of metal stud gear with an elliptical-reinforced polymer stud gear in the Panther 1650 Series All Geared Lathe Machine, achieving a total of 2 million revolutions. The optical 3D measurement technique is used to visualise and examine residual stresses using displacement fields.