Melt spinning of semi-crystalline compound fibers
(5573-5586) Polymer 52 #24 (2011)
Blanco-and Ramos of the Universidad de Málaga, Spain, developed a two-dimensional model of the fluid dynamics, heat transfer, molecular orientation and crystallization of the melt spinning of semi-crystalline compound fibers.  The model employs a Newtonian rheology, includes the effects of both temperature and flow-induced crystallization, and accounts for the effects of the molecular orientation on both the stress tensor and the crystallization through a Doi–Edwards formulation and the Avrami–Kolmogorov kinetics, respectively.  It is shown that, even at moderately low Biot numbers, the temperature across the compound fiber is not uniform owing to heat losses; for the cases studied here, the temperature non-uniformities are mainly a function of the Biot number, the thermal conductivity and the pre-exponential factor and activation energy of the viscosity law for the cladding. It is also shown that the cross-sectional averaged temperature predicted by the two-dimensional model exhibits the same qualitative trends as those of an asymptotic one-dimensional model which does not account for axial conduction and which is only valid for slender fibers at low Reynolds and Biot numbers.  (RDC 11/3/2011)

Preparation of poly(ε-caprolactone)/poly(ε-caprolactone-co-lactide) (PCL/PLCL) blend filament by melt spinning
(1944–1950)Journal of Applied Polymer  Science 123 #4 (2012)
Gupta, Ray and Ray of the Indian Institute of Technology, India, prepared poly(ε-caprolactone)/poly(ε-caprolactone-co-lactide) (PCL/PLCL) blend filaments with various ratios of PCL and PLCL by melt spinning.  Crystallinity of blend decreased with the addition of PLCL and the fracture surface becomes rougher at higher PLCL content. (RDC 11/2/2011)

Optimization of acrylic dry spinning production line by using artificial neural network and genetic algorithm
(735–744)Journal of Applied Polymer  Science 120 #2 (2011)
Vadood, Semnani and Morshed  of the Isfahan University of Technology, Iran  showed that  an artificial neural network (ANN) using the back propagation training algorithm could predict the color index. ANN parameters including the number of hidden layers, number of neurons in each layer, adaptive learning rate, activation functions and numberr of max fail epochs.  (RDC 1/11/2011)

recent journal

12/3/2010
No-flow temperature in injection molding simulation
( 3382–3392)Journal of Applied Polymer  Science 119 #6 (2010)
Mannella  et al proposed a simple correlation for no flow temperature estimation, derived from the Cross–Williams–Landel–Ferry equation for both amorphous and semicrystalline polymers.   The no flow temperature has a large influence on the thickness of the frozen layer, but does not appreciably affect the filling pressure.  Because the it affects the frozen layer, an effect on the estimation of shrinkage and warpage is expected.  (RDC 12/9/2010)