A Study of Melt Flow Analysis of an ABS-Iron Composite in Fused Deposition Modelling Process

Fused deposition modelling (FDM) is a filament based rapid prototyping system which offers the possibility of introducing new composite material for the FDM process as long as the new material can be made in feedstock filament form. Swinburne has been undertaking extensive research in development of new composite materials involving acrylonitrile-butadiene-styrene (ABS) and other materials including metals. In order to predict the behaviour of new ABS based composite materials in the course of FDM process, it is necessary to investigate the flow of the composite material in liquefier head. No such study is available considering the geometry of the liquefier head. This paper presents 2-D and 3-D numerical analysis of melt flow behaviour of a representative ABS-iron composite through the 90-degree bent tube of the liquefier head of the fused deposition modelling process using ANSYS FLOTRAN and CFX finite element packages. Main flow parameters including temperature, velocity, and pressure drop have been investigated. Filaments of the filled ABS have been fabricated and characterized to verify the possibility of prototyping using the new material on the current FDM machine. Results provide promising information in developing the melt flow modelling of metal-plastic composites and in optimising the FDM parameters for better part quality with such composites.     

Extinction risk depends strongly on factors contributing to stochasticity

Extinction risk in natural populations depends on stochastic factors that affect individuals, and is estimated by incorporating such factors into stochastic models. Stochasticity can be divided into four categories, which include the probabilistic nature of birth and death at the level of individuals (demographic stochasticity), variation in population-level birth and death rates among times or locations (environmental stochasticity), the sex of individuals and variation in vital rates among individuals within a population (demographic heterogeneity). Mechanistic stochastic models that include all of these factors have not previously been developed to examine their combined effects on extinction risk. Here we derive a family of stochastic Ricker models using different combinations of all these stochastic factors, and show that extinction risk depends strongly on the combination of factors that contribute to stochasticity. Furthermore, we show that only with the full stochastic model can the relative importance of environmental and demographic variability, and therefore extinction risk, be correctly determined. Using the full model, we find that demographic sources of stochasticity are the prominent cause of variability in a laboratory population of Tribolium castaneum (red flour beetle), whereas using only the standard simpler models would lead to the erroneous conclusion that environmental variability dominates. Our results demonstrate that current estimates of extinction risk for natural populations could be greatly underestimated because variability has been mistakenly attributed to the environment rather than the demographic factors described here that entail much higher extinction risk for the same variability level.