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Composite materials have become widely used in engineering applications, in order to reduce the overall weight of structures while retaining their required strength. In this work, a composite material consisting of discontinuous glass fibers in a polypropylene matrix is studied at the microstructural level through coupled experiments and simulations, in order to uncover the mechanisms that cause damage to initiate in the microstructure under macroscopic tension. Specifically, we show how hydrostatic stresses in the matrix can be used as a metric to explain and predict the exact location of microvoid nucleation that occurs during damage initiation within the composite’s microstructure. Furthermore, this work provides evidence that hydrostatic stresses in the matrix can lead to coupled microvoid nucleation and early fiber breakage, and that small fragments of fibers can play an important role in the process of microvoid nucleation. These results significantly improve our understanding of the mechanics that drive the initiation of damage in the complex microstructures of discontinuous fiber reinforced thermoplastics, while also allowing scientists and engineers to predict the microstructural damage behavior of these composites at sub-fiber resolution and with high accuracy.

 

Material and biological sciences frequently generate large amounts of microscope data that require 3D object level segmentation. Often, the objects of interest have a common geometry, for example spherical, ellipsoidal, or cylindrical shapes. Neural networks have became a popular approach for object detection but they are often limited by their training dataset and have difficulties adapting to new data. In this paper, we propose a volumetric object detection approach for microscopy volumes comprised of fibrous structures by using deep centroid regression and geometric regularization. To this end, we train encoder-decoder networks for segmentation and centroid regression. We use the regression information combined with prior system knowledge to propose cylindrical objects and enforce geometric regularization in the segmentation. We train our networks on synthetic data and then test the trained networks in several experimental datasets. Our approach shows competitive results against other 3D segmentation methods when tested on the synthetic data and outperforms those other methods across different datasets. 

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Computational Imaging proceedings. 

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Computational Imaging proceedings. 

Polymer matrix composites are popular in the aerospace industry due to their high strength to weight ratio. While they have become popular, understanding and predicting their specific damage evolution mechanisms remains a challenge especially in designing with damage tolerance criteria. One challenge often faced is the presence of surface damage either induced during manufacturing, machining, or service of a composite part. While many studies have investigated how quasi-static, low-velocity, and ballistic impact results in damage in the material, there remains a need to further understand the reduction in performance that results from such surface damage. In this work, micro-indentation was conducted on a unidirectional IM7/8552 laminate composite specimen to induce quasi-static impact damage that results in surface damage. The specimen was then loaded in tension to 33% of its expected failure load and imaged using synchrotron X-ray micro-computed tomography to qualitatively investigate the progression of surface damage into sub-surface damage. This work shows that at 33% of tensile failure load, surface damage propagates into delamination and fiber breakage of plies directly sub-surface. This work sheds light on the progression of surface damage at loads less than 50% of the ultimate strength of a unidirectional laminate composite.