No abstract

No abstract

No abstract

The material properties of thermoplastic polymer parts manufactured by the extrusion-based additive manufacturing process are highly dependent on the thermal history. Different numerical models have been proposed to simulate the thermal history of a 3D-printed part. However, they are limited due to limited geometric applicability; low accuracy; or high computational demand. Can the time–temperature history of a 3D-printed part be simulated by a computationally less demanding, fast numerical model without losing accuracy? This paper describes the numerical implementation of a simplified discrete-event simulation model that offers accuracy comparable to a finite element model but is faster by two orders of magnitude. Two polymer systems with distinct thermal properties were selected to highlight differences in the simulation of the orthotropic response and the temperature-dependent material properties. The time–temperature histories from the numerical model were compared to the time–temperature histories from a conventional finite element model and were found to match closely. The proposed highly parallel numerical model was approximately 300–500 times faster in simulating thermal history compared to the conventional finite element model. The model would enable designers to compare the effects of several printing parameters for specific 3D-printed parts and select the most suitable parameters for the part.

The collapse of deposited thermoplastic composite material under self-weight presents a risk in large-format extrusion-based additive manufacturing. Two critical processing parameters, extrusion temperature and deposition rate, govern whether a deposited layer is stable and bonds properly with the previously deposited layer. Currently, the critical parameters are determined via a trial-and-error approach. This research work uses a simplified physics-based numerical simulation to determine a suitable combination of the parameters that will avoid the collapse of the deposited layer under self-weight. The suitability of the processing parameters is determined based on the maximum plastic viscous strains computed using a sequentially coupled thermo-mechanical numerical model. This computational tool can efficiently check if a combination of temperature and extrusion rate causes layer collapse due to self-weight, and hence minimize the manufacturing risk of large-format 3D-printed parts.