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1. Finite Element Model of Equal Channel Angular Extrusion of Ultra High Molecular Weight Polyethylene

   https://doi.org/10.1115/1.4051189  

   Vasylevskyi, Kostiantyn ; Tsukrov, Igor ; Miroshnichenko, Kateryna ; Buklovskyi, Stanislav ; Grover, Hannah ; Van Citters, Douglas W. ( May 2021 , Journal of Manufacturing Science and Engineering)

   null (Ed.)

   Ultra-high molecular weight polyethylene (UHMWPE) used in biomedical applications, e.g. as a bearing surface in total joint arthroplasty, has to possess superior tribological properties, high mechanical strength, and toughness. Recently, equal channel angular extrusion (ECAE) was proposed as a processing method to introduce large shear strains to achieve higher molecular entanglement and superior mechanical properties of this material. Finite element analysis (FEA) can be utilized to evaluate the influence of important manufacturing parameters such as the extrusion rate, temperature, geometry of the die, back pressure, and friction effects. In this paper we present efficient FEA models of ECAE for UHMWPE. Our studies demonstrate that the choice of the constitutive model is extremely important for the accuracy of numerical modeling predictions. Three considered material models (J2-plasticity, Bergstrom-Boyce, and the Three Network Model) predict different extrusion loads, deformed shapes and accumulated shear strain distributions. The work has also shown that the friction coefficient significantly influences the punch force and that the 2D plane strain assumption can become inaccurate in the presence of friction between the billet and the extrusion channel. Additionally, a sharp corner in the die can lead to the formation of the so-called “dead zone” due to a portion of the material lodging into the corner and separating from the billet. Our study shows that the presence of this material in the corner substantially affects the extrusion force and the resulting distribution of accumulated shear strain within the billet 

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2. Modeling the strength of aluminum extrusion transverse welds using the film theory of solid-state welding

   Oberhausen, Gregory ; Cooper, Daniel ( December 2023 , Journal of materials processing technology)

   Reducing production scrap is vital for decarbonizing the aluminum industry. In extrusion, the greatest source of scrap stems from removing profile sections containing transverse (charge) welds that are deemed too weak for their intended purpose. However, until now, there has been no predictive transverse weld strength model. This article establishes a transverse weld strength model as a function of billet properties and extrusion parameters. It extends the film theory of solid-state welding by enhancing Cooper and Allwood’s plane strain model to consider non-plane strain deformations at the billet-billet interface. These enhancements are informed by analyzing oxide fragmentation patterns through shear lag modeling and microscopy of profiles extruded from anodized billets. Model predictions are assessed through shear tests on welds from single and two-piece billets, extruded into rod, bar, and multi-hollow profiles. The experiments reveal that negative surface expansions at the weld nose cause interface buckling and weaker welds, but both surface expansions and weld strengths increase with distance from the nose. In non-axisymmetric profiles, deformation conditions and strengths vary across, as well as along, the weld. Two-piece billet welds are longer but reach bulk strength long before weld termination. The model predicts these trends and shows that die pressures are sufficient for micro-extrusion of any exposed substrate through interface oxide cracks. This underscores the significance of interface strains in exposing substrate and determining the weld strength. The model can help increase process yields by determining minimum lengths of weak profile to scrap and aiding process optimization for increased weld strength. 

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3. Prediction of Effective Elastic Properties of Carbon/UHMWPE Nanocomposites by Combination of Numerical and Analytical Modeling

   https://doi.org/10.12783/asc38/36636  

   BUKLOVSKYI, STANISLAV ; MIROSHNICHENKO, KATERYNA ; TSUKROV, IGOR ; THOMSON, REBECCA J. ; SOLBERG, PEDER C. ; CITTERS, DOUGLAS W. ; Miroshnichenko, Kateryna ; Tsukrov, Igor ; Thomson, Rebecca J. ; Solberg, Peder C. ; et al ( September 2023 , Destech Publications, Inc.)

   In this paper, we investigate ultra-high-molecular-weight-polyethylene (UHMWPE) doped with conductive carbon black (CCB) nanoparticles. This nanocomposite is considered a candidate for biomedical applications such as orthopedics. Micro-computed tomography (μCT) and scanning electron microscopy studies show that the composite has a complex microstructure consisting of larger particles of UHMWPE surrounded by a thin layer containing a high concentration of CCB nano inclusions. The overall mechanical properties of these composites depend on the volume fraction of CCB and the manufacturing procedures e.g., compression molding or equal channel angular extrusion. To predict the effective elastic properties of the CCB/UHMWPE nanocomposite, we propose a multiscale modeling framework based on a combined analytical-numerical approach. μCT images are processed to extract the size, shape, and orientation distributions of UHMWPE particles as well as the volume fractions and spatial distribution of CCB containing layer. These distributions are used to develop multiscale numerical models of the composite including finite element analysis of representative volume elements on the mesoscale, and micromechanical predictions of CCB containing layer on the microscale. The predictive ability of the models is confirmed by comparison with the experimental measurements obtained by dynamic mechanical analysis. 

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4. Characterization and modeling of carbon black/ultra- high-molecular-weight-polyethylene nanocomposites manufactured with equal channel angular extrusion

   https://doi.org/10.1063/5.0145202  

   Miroshnichenko, Kateryna ; Buklovskyi, Stanislav ; Vasylevskyi, Kostiantyn ; Tsukrov, Igor ; Favreau, Hannah J. ; Thomson, Rebecca J. ; Solberg, Peder C. ; Van Citters, Douglas W. ( January 2023 , AIP Conference Proceedings)

   Ultra-High Molecular Weight Polyethylene (UHMWPE) is widely used as a bearing surface in total and partial joint arthroplasty. In addition to medical applications, this polymer is utilized in the fields of ballistic protection, sports, and industrial tribology. The addition of carbon allotropes, such as nanographite or carbon black powders, to UHMWPE offers potential benefits including added conductivity, increased wear resistance, and introduction of micro-tracers for understanding microstructural behavior and monitoring damage [1]. The mechanical properties of these Carbon/UHWPE nanocomposites can be enhanced by subjecting them to equal channel angular extrusion (ECAE) as a way to introduce large shear strains to achieve higher molecular entanglement of UHMWPE and better distribution of carbon nanoparticles [2, 3]. In this paper, micro-computed tomography (µCT) is used to characterize carbon black (CB) and nanographite (N27SG) reinforced UHMWPE polymers. It is shown that the procedure described in [1] results in almost uniform distribution of carbon inclusions around UHMWPE particles with both compression molding (CM), and ECAE processes. Multiscale numerical models of the composite are developed based on the µCT images, including mesoscale finite element (FE) models of representative volume element (RVE) on the mesoscale, and micromechanical predictions for carbon-rich interphase layers on the microscale. 

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5. A rheologist's guideline to data-driven recovery of complex fluids' parameters from constitutive models

   https://doi.org/10.1039/D3DD00036B  

   Saadat, Milad ; Mangal, Deepak ; Jamali, Safa ( August 2023 , Digital Discovery)

   Rheology-informed neural networks (RhINNs) have recently been popularized as data-driven platforms for solving rheologically relevant differential equations. While RhINNs can be employed to solve different constitutive equations of interest in a forward or inverse manner, their ability to do so strictly depends on the type of data and the choice of models embedded within their structure. Here, the applicability of RhINNs in general, and the interplay between the choice of models, parameters of the neural network itself, and the type of data at hand are studied. To do so, a RhINN is informed by a series of thixotropic elasto-visco-plastic (TEVP) constitutive models, and its ability to accurately recover model parameters from stress growth and oscillatory shear flow protocols is investigated. We observed that by simplifying the constitutive model, RhINN convergence is improved in terms of parameter recovery accuracy and computation speed while over-simplifying the model is detrimental to accuracy. Moreover, several hyperparameters, e.g., the learning rate, activation function, initial conditions for the fitting parameters, and error heuristics, should be at the top of the checklist when aiming to improve parameter recovery using RhINNs. Finally, the given data form plays a pivotal role, and no convergence is observed when one set of experiments is used as the given data for either of the flow protocols. The range of parameters is also a limiting factor when employing RhINNs for parameter recovery, and ad hoc modifications to the constitutive model can be trivial remedies to guarantee convergence when recovering fitting parameters with large values.

    

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