• Cheng, Hongyang
• Snijder, Henk
• Naranjo, Juan Esteban Alvarez
• Weinhart, Thomas
• Vaneker, Tom
• Luding, Stefan

Abstract

To prevent texture defects in powder-based processes, the sintering time needs to be adjusted such that a certain amount of coalescence is achieved. However, predicting the required sintering time is extremely challenging to assess in materials such as polymers because the kinetics exhibit both elastic and viscous characteristics when undergoing deformation. The present work introduces a computational approach to model the viscoelastic effect in the sintering of particles. The model contains three stages, three different mechanisms driven by adhesive inter-surface forces and surface tension, which describes the non-linear sintering behaviour. Experimental data from the binary coalescence of Polystyrene (PS), Polyamide (PA) 12 and PEEK 450PF particles are employed to calibrate the contact model, as implemented in MercuryDPM, an open-source software package. Using machine learning-based Bayesian calibration, good agreement is obtained between the experimental data and the numerical results. The findings will be used in future studies to predict densification rates in powder-based processes.

Topics

• impedance spectroscopy
• surface
• polymer
• texture
• defect
• sintering
• densification
• machine learning