Search for: All records

This article presents the first use of shape forming elements (SFEs) to produce architected composites from multiple materials in an extrusion process. Each SFE contains a matrix of flow channels connecting input and output ports, where materials are routed between corresponding ports. The mathematical operations of rotation and shifting are described, and design automation is explored using Bayesian optimization and genetic algorithms to select fifty or more parameters for minimizing two objective functions. The first objective aims to match a target cross-section by minimizing the pixel-by-pixel error, which is weighted with the structural similarity index (SSIM). The second objective seeks to maximize information content by minimizing the SSIM relative to a white image. Satisfactory designs are achieved with better objective function values observed in rectangular rather than square flow channels. Validation extrusion of modeling clay demonstrates that while SFEs impose complex material transformations, they do not achieve the material distributions predicted by the digital model. Using the SSIM for results comparison, initial stages yielded SSIM values near 0.8 between design and simulation, indicating a good initial match. However, the control of material processing tended to decline with successive SFE processing with the SSIM of the extruded output dropping to 0.023 relative to the design intent. Flow simulations more closely replicated the observed structures with SSIM values around 0.4 but also failed to predict the intended cross-sections. The evaluation highlights the need for advanced modeling techniques to enhance the predictive accuracy and functionality of SFEs for biomedical, energy storage, and structural applications.

 

This study aims to establish a systematic approach for characterizing recycled polyolefins of unknown composition, with a specific focus on predicting their performance in film extrusion. We explore various characterization techniques, including differential scanning calorimetry (DSC), Fourier‐transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and rheometry to assess their effectiveness in identifying the polyethylene (PE) fractions within polypropylene (PP) recyclates. By integrating experimental data with modeling techniques, we aim to provide insights into the predictive capabilities of these techniques in determining processing behaviors. The research highlights the superior fidelity of DSC in predicting the relative fraction and type of PE in a PP recyclate. FTIR is also identified as a high‐fidelity approach, albeit requiring application‐specific calibration. TGA, capillary, and oscillatory rheometry are recognized for their ability to distinguish between grades of recycled polyolefins but provide aggregate behaviors rather than detailed constituent information. 3D flow simulation of the cast film extrusion investigated the effect of the viscosity characterization method, non‐isothermal assumption, and process settings but could not fully replicate the observed variations in the cast film processing of two industrial polyolefins with similar melt flow rates and viscosity behaviors. This underscores the practical challenge of predicting processing issues prior to actual processing, necessitating reliance on reliable instrumentation suites and human expertise for diagnosing and remedying variations.

Two industrial recycled polypropylene materials having similar melt flow rates exhibit drastically different cast film processing behaviors.

DSC and FTIR provide reasonable approaches for identifying constituent materials.

Modeling of the melt viscosities characterized by capillary and parallel plate rheology suggests that viscosity variations relative to the power‐law behavior assumed in the coat hanger die design is a predominant driver of cast film instabilities.

 

The geometry of multisegmented thermo-responsive gel robots was manipulated to break symmetry and support locomotion. 

The mechanical behavior of soft collagenous tissues is largely influenced by the reinforcing collagen fiber microstructure. The anisotropic collagen microstructure can remodel in response to changes in mechanical loading, which can dramatically alter the mechanical properties of the tissues and the mechanical environment of the resident cells. It is important to study the remodeling mechanisms of collagen tissues to understand the pathophysiology of various connective tissue diseases. We hypothesize that the collagen structure actively changes in response to mechanical stimuli through concurrent processes of collagen deposition and degradation and that the rates of these processes are altered by collagen mechanochemistry, mechanosensitive collagen production, and cellular contraction. In prior studies, we developed micromechanical models of collagen tissues to investigate the role of collagen mechanochemistry and mechanosensitive collagen production in remodeling the collagen fiber structure and tissue growth.[1,2] We found that stress inhibition of enzymatic degradation and stimulation of collagen production can explain many phenomena, including remodeling the anisotropic collagen structure along the directions of the maximum principal stress and the development of stress homeostasis. The goal of this study is to investigate the effect of mechanical loading on the active behavior of the cells. Our approach uses a model 3D microtissue systems, self-assembled on a magnetically actuated two-pillar system (µTUG), to investigate these cell-collagen interactions and effects of mechanical loading. The micropillar support allows for measurement of the active cellular contraction, while the magnetic tweezer allows for mechanical testing of the microtissue under a controlled stress rate. Digital image analysis is applied to measure the local two-dimensional (2D) strain field. To analyze the mechanical measurements for mechanical properties of the collagen structure and active behavior of the cells, we developed a micromechanical model for the mechanical behavior of the microtissue. The micromechanical model includes the elastic behavior of the anisotropic collagen structure and the anisotropic active behavior of the cells. To describe mechanosensitive cellular contraction, we assume concurrent polymerization/depolymerization of actin filaments, where the polymerization rate increases with the fiber stress. In this paper, we will briefly summarize the model and describe an initial model validation by comparing to µTUG experiments measuring the stress-strain behavior of the microtissue to load-unload tests. 

Abstract This paper presents a method to derive the virtual fields for identifying constitutive model parameters using the Virtual Fields Method (VFM). The VFM is an approach to identify unknown constitutive parameters using deformation fields measured across a given volume of interest. The general principle for solving identification problems with the VFM is first to derive parametric stress field, where the stress components at any point depend on the unknown constitutive parameters, across the volume of interest from the measured deformation fields. Applying the principle of virtual work to the parametric stress fields, one can write scalar equations of the unknown parameters and solve the obtained system of equations to deduce the values of unknown parameters. However, no rules have been proposed to select the virtual fields in identification problems related to nonlinear elasticity and there are multiple strategies possible that can yield different results. In this work, we propose a systematic, robust and automatic approach to reconstruct the systems of scalar equations with the VFM. This approach is well suited to finite-element implementation and can be applied to any problem provided that full-field deformation data are available across a volume of interest. We also successfully demonstrate the feasibility of the novel approach by multiple numerical examples. Potential applications of the proposed approach are numerous in biomedical engineering where imaging techniques are commonly used to observe soft tissues and where alterations of material properties are markers of diseased states.