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.
Highlights
Industries have shown interest in powder-based processes because complex objects can be created
The neck growth kinetics requires to be estimated with a good agreement
This study provided an approach to evaluate the neck growth kinetics based on three different mechanisms, for polymer powders
Summary
Industries have shown interest in powder-based processes because complex objects can be created. In order to prevent texture defects in powder-based processes, the neck growth kinetics needs to be model accurately. The literature provides different contact models for the prediction of sintering neck growth [1, 2], the complexity of the viscoelastic response in materials such as polymers hinders the correct estimation [3]. The reason is that a simple power-law modestly describes the complex viscoelastic behaviour in polymer sintering It was demonstrated by Fuchs et al [4] that additional driving mechanisms should be included in the discrete contact model in order to predict the sintering time accurately. Due to the micro-mechanical calibration remains a tremendous challenge because the diversity of granular materials, this work utilizes a Bayesian calibration technique proposed by Cheng et al [12]
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