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1. On numerical modeling of equal channel angular extrusion of ultra high molecular weight polyethylene

   Vasylevskyi, Kostiantyn ; Miroshnichenko, Kateryna ; Buklovskyi, Stanislav ; Tsukrov, Igor ; Grover, Hannah ; Van Citters, Douglas ( October 2020 , Proceedings of the ASME 2020 International Mechanical Engineering Congress and Exposition, 2020)

   null (Ed.)

   Ultra high molecular weight polyethylene (UHMWPE) is widely used in biomedical applications, e.g. as a bearing surface in total joint arthroplasty. Recently, equal channel angular extrusion (ECAE) was proposed as a processing method to achieve higher molec ular entanglement and superior mechanical properties of this material. Numerical modeling can be utilized to evaluate the influence of such important manufacturing parameters as the extrusion rate, temperature, geometry of the die, back pressure and fricti on effects in the ECAE of polyethylenes. In this paper we focus on the development of efficient FE models of ECAE for UHMWPE. We study the applicability of the available constitutive models traditionally used in polymer mechanics for UHMWPE, evaluate the importance of the proper choice of the friction parameters between the billet and the die, and compare the accuracy of predictions between 2D (plane strain) and 3D models. Our studies demonstrate that the choice of the constitutive model is extremely important for the accuracy of numerical modeling predictions. It is also shown that the friction coefficient significantly influences the punch force and that 2D plane strain assumption can become inaccurate in the presence of friction between the billet and the extrusion channel. 

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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. 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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4. Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading

   https://doi.org/10.1007/s00466-023-02343-6  

   Lu, Minglei ; Mohammadi, Ali ; Meng, Zhaoxu ; Meng, Xuhui ; Li, Gang ; Li, Zhen ( May 2023 , Computational Mechanics)

   Additive manufacturing has been recognized as an industrial technological revolution for manufacturing, which allows fabrication of materials with complex three-dimensional (3D) structures directly from computer-aided design models. Using two or more constituent materials with different physical and mechanical properties, it becomes possible to construct interpenetrating phase composites (IPCs) with 3D interconnected structures to provide superior mechanical properties as compared to the conventional reinforced composites with discrete particles or fibers. The mechanical properties of IPCs, especially response to dynamic loading, highly depend on their 3D structures. In general, for each specified structural design, it could take hours or days to perform either finite element analysis (FEA) or experiments to test the mechanical response of IPCs to a given dynamic load. To accelerate the physics-based prediction of mechanical properties of IPCs for various structural designs, we employ a deep neural operator (DNO) to learn the transient response of IPCs under dynamic loading as surrogate of physics-based FEA models. We consider a 3D IPC beam formed by two metals with a ratio of Young’s modulus of 2.7, wherein random blocks of constituent materials are used to demonstrate the generality and robustness of the DNO model. To obtain FEA results of IPC properties, 5000 random time-dependent strain loads generated by a Gaussian process kennel are applied to the 3D IPC beam, and the reaction forces and stress fields inside the IPC beam under various loading are collected. Subsequently, the DNO model is trained using an incremental learning method with sequence-to-sequence training implemented in JAX, leading to a 100X speedup compared to widely used vanilla deep operator network models. After an offline training, the DNO model can act as surrogate of physics-based FEA to predict the transient mechanical response in terms of reaction force and stress distribution of the IPCs to various strain loads in one second at an accuracy of 98%. Also, the learned operator is able to provide extended prediction of the IPC beam subject to longer random strain loads at a reasonably well accuracy. Such superfast and accurate prediction of mechanical properties of IPCs could significantly accelerate the IPC structural design and related composite designs for desired mechanical properties. 

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5. 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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