Abstract
The use of composites made of polyamide 6.6 matrix and short-glass-fibers in automobile industry is progressive due to its low density and cost. The injection molded short glass fiber-reinforced (SGFR) thermoplastics structural parts such as intake, manifold and engine mount housing, induce complex fiber orientation distributions (FOD). This microstructure governs the macroscopic properties such as the mechanical stiffness and fatigue resistance. To estimate the FOD on such industrial parts at complex angles and ribs, we rely on simulation results and micro tomography analysis. The interest of this paper is to develop a semi-automated, quick and efficient orientation tensor identification approach from 2D microscopic images, which is capable of observing relatively larger surface compared to micro tomography. We finally conclude by comparing it with micro tomography and simulation results. Furthermore, we investigate its relevance with fatigue service ability.
Highlights
The urge to replace heavy metallic parts had made the car manufacturers look for light weight and environment friendly alternatives
The fiber orientation distributions (FOD) predicted by commercial simulation software are used by the companies in their design loop to improve their parts
The difference between the reality seen on the parts and these simulation results are questionable, thereby creating a demand to clarify the results with experimental analysis
Summary
The urge to replace heavy metallic parts had made the car manufacturers look for light weight and environment friendly alternatives. During the process of injection, the SGFR exhibits specific microstructure It is characterized by a heterogeneous orientation of the fibers (that leads to an anisotropic mechanical behavior) along the structural part, and in thickness due to the well-known skin-core effect [1,2,3]. Even microtomography analysis is seen as a replacement The evaluation of these tensors is clearly a key point for the design loop, representative with the geometric details met on industrial parts. In order to provide a better or faster description, this paper presents a semi-automated approach, which could be used to analyze large areas of the sample This 2D microscopic images stores less space than that of tomography analysis. The orientation tensors identified are compared on one hand, to the ones predicted by Moldflow and on the other hand, to the thermal fields and crack locations observed during fatigue tests
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