Search

This article presents a novel approach for assessing the effects of residual stresses in laser-directed energy deposition (L-DED). The approach focuses on exploiting the potential of rapidly growing tools such as machine learning and polynomial chaos expansion for handling full-field data for measurements and predictions. In particular, the thermal expansion coefficient of thin-wall L-DED steel specimens is measured and then used to predict the displacement fields around the drilling hole in incremental hole-drilling tests. The incremental hole-drilling test is performed on cubic L-DED steel specimens and the displacement fields are visualized using a 3D micro-digital image correlation setup. A good agreement is achieved between predictions and experimental measurements.

This article presents the development and implementation of the Delamination Plug-in, an open-source tool for modeling delamination tests in the ABAQUS software. Specifically designed for stochastic modeling of 3D printed composites, the plug-in combines the benefits of the graphical user interface (GUI) and the programming of commercial finite element (FE) software. The Delamination Plug-in offers an effortless alternative to the time-consuming analytical modeling and GUI work involved in delamination tests and includes algorithms for several tests, such as the double cantilever beam, end-loaded split, end-notched flexure, and modified end-loaded split tests, solved using the virtual crack closure technique and the cohesive zone method. It enables the user to develop simulations for both simple symmetric laminates and generally layered laminates with additional thermal stresses. The applicability of the tool is demonstrated through its use in two distinct delamination problems, one for conventional and one for 3D printed composite laminates, and its results are compared to analytical models and experimental data from the open literature. The results demonstrate that the Delamination Plug-in is efficient and applicable for such materials. This establishes the tool as an important means of automating delamination analysis and for the development and testing of 3D printed composites, making it a valuable tool for both researchers and industry professionals.

The voids in 3D printed materials produced by the material extrusion technique are mainly located between the printed filaments. Their shape depends on material and printing parameters and can be both convex and concave. This shape influences the accuracy of the predictions of common homogenization schemes that consider spheroidal shapes. A novel approach is introduced in this study for converting real voids to ideal supercylindrical ones that approximate better the shape of voids and can be described using only one parameter. This fact leads to a fast implementation in stochastic modeling schemes where fewer samples are required. The novel approach is implemented using the polynomial chaos expansion technique to predict the elastic properties of acrylonitrile butadiene styrene. The results are compared with previously performed tensile tests and both simple and computationally intensive models to provide a holistic understanding of the advantages of the new approach.

The combination of recycled fiber reinforcement with recycled polymer as a feedstock material for extrusion-based additive manufacturing creates an opportunity for a more sustainable material use. In this study, recycled short carbon fibers were combined with recycled poly(ethylene terephthalate) (PET) to obtain carbon fiber-reinforced PET filaments via melt extrusion. The carbon fiber content of the extruded filaments ranged from 0.4 to 40.7 wt%. The molar mass and the degree of crystallinity after processing were determined to evaluate the influence of the extrusion process on the physico-chemical and mechanical properties of the reinforced PET filaments. Furthermore, pressure-volume-temperature measurements were carried out to investigate the influence of the carbon fibers on the shrinkage of the semi-crystalline PET. Samples were printed and their superior mechanical properties, including a 390% increase in tensile modulus, were confirmed via tensile testing. Analysis via X-ray micro-computed tomography indicated that the fiber length reduced with increasing fiber content. The high degree of fiber alignment that was observed in the extruded filaments, was slightly reduced after deposition. Scanning electron microscopy data showed that fiber pull-out was the governing failure mechanism, indicating a weak interface between the carbon fibers and the matrix. The results show the potential of extrusion-based additive manufacturing to valorize recycled PET and recycled carbon fibers.

Most of the mechnoregulatory computational models appearing so far in tissue engineering for bone healing predictions, utilize as regulators for cell differentiation mainly the octahedral volume strains and the interstitial fluid velocity calculated at any point of the fractured bone area and controlled by empirical constants concerning these two parameters. Other stimuli like the electrical and chemical signaling of bone constituents are covered by those two regulatory fields. It is apparent that the application of the same mechnoregulatory computational models for bone healing predictions in scaffold-aided regeneration is questionable since the material of a scaffold disturbs the signaling pathways developed in the environment of bone fracture. Thus, the goal of the present work is to evaluate numerically two fields developed in the body of two different compressed scaffolds, which seem to be proper for facilitating cell sensing and improving cell viability and cell seeding efficiency. These two fields concern the surface octahedral strains that the cells attached to the scaffold can experience and the internal strain gradients that create electrical pathways due to flexoelectric phenomenon. Both fields are evaluated with the aid of the Boundary Element Method (BEM), which is ideal for evaluating with high accuracy surface strains and stresses as well as strain gradients appearing throughout the analyzed elastic domain.