COMPREHENSIVE PROPERTY DETERMINATION FOR FIBER-REINFORCED POLYMER COMPOSITES IN EXTRUSION DEPOSITION ADDITIVE MANUFACTURING—BAYESIAN VS DETERMINISTIC This work introduces both deterministic and Bayesian methodologies to simultaneously determine the elastic constants of the constituent polymer and the fiber orientation state in a short fiber-reinforced polymer (SFRP) composite based on a small number of experimental measurements of the composite properties. The ability of the Bayesian approach to calibrate uncertainties makes it a promising tool for enabling a probabilistic framework for composites manufacturing digital twins. The two methods that enable the reverse engineering of the orientation of the fibers and the in-situ polymer properties are compared. For the extrusion deposition additive manufacturing (EDAM) process and other SFRP composites processes (e.g. injection molding), extensive characterization efforts are currently required to develop composites manufacturing digital twins. To circumvent the extensive characterization required, Digimat© provides a suite of tools to reverse engineer material properties of SFRPs. However, Digimat© lacks a methodology to inversely determine the fiber orientation state and the constituent polymer properties simultaneously. To that end, this work presents both a deterministic and hierarchical Bayesian approaches to determine the polymer properties and the fiber orientation state simultaneously. The results indicate that both approaches provide a reliable framework for the reverse engineering process. The deterministic approach provides a more rapid, point estimate methodology, whereas the Bayesian approach provides a more comprehensive methodology that includes uncertainties in the reverse engineering process. This work introduces both deterministic and Bayesian methodologies to simultaneously determine the elastic constants of the constituent polymer and the fiber orientation state in a short fiber-reinforced polymer (SFRP) composite based on a small number of experimental measurements of the composite properties. The ability of the Bayesian approach to calibrate uncertainties makes it a promising tool for enabling a probabilistic framework for composites manufacturing digital twins. The two methods that enable the reverse engineering of the orientation of the fibers and the in-situ polymer properties are compared. For the extrusion deposition additive manufacturing (EDAM) process and other SFRP composites processes (e.g. injection molding), extensive characterization efforts are currently required to develop composites manufacturing digital twins. To circumvent the extensive characterization required, Digimat© provides a suite of tools to reverse engineer material properties of SFRPs. However, Digimat© lacks a methodology to inversely determine the fiber orientation state and the constituent polymer properties simultaneously. To that end, this work presents both a deterministic and hierarchical Bayesian approaches to determine the polymer properties and the fiber orientation state simultaneously. The results indicate that both approaches provide a reliable framework for the reverse engineering process. The deterministic approach provides a more rapid, point estimate methodology, whereas the Bayesian approach provides a more comprehensive methodology that includes uncertainties in the reverse engineering process.

This work introduces both deterministic and Bayesian methodologies to simultaneously determine the elastic constants of the constituent polymer and the fiber orientation state in a short fiber-reinforced polymer (SFRP) composite based on a small number of experimental measurements of the composite properties. The ability of the Bayesian approach to calibrate uncertainties makes it a promising tool for enabling a probabilistic framework for composites manufacturing digital twins. The two methods that enable the reverse engineering of the orientation of the fibers and the in-situ polymer properties are compared. For the extrusion deposition additive manufacturing (EDAM) process and other SFRP composites processes (e.g. injection molding), extensive characterization efforts are currently required to develop composites manufacturing digital twins. To circumvent the extensive characterization required, Digimat© provides a suite of tools to reverse engineer material properties of SFRPs. However, Digimat© lacks a methodology to inversely determine the fiber orientation state and the constituent polymer properties simultaneously. To that end, this work presents both a deterministic and hierarchical Bayesian approaches to determine the polymer properties and the fiber orientation state simultaneously. The results indicate that both approaches provide a reliable framework for the reverse engineering process. The deterministic approach provides a more rapid, point estimate methodology, whereas the Bayesian approach provides a more comprehensive methodology that includes uncertainties in the reverse engineering process.

Structure-property-processing relationships in extruded liquid crystal polymer film

Liquid crystal polymers (LCPs) derive favorable mechanical, chemical, and electrical behavior from long-range molecular ordering. The microstructure gives rise to anisotropic bulk properties that are problematic for industrial applications, and thus the ability to model the polymer directionality is essential to the design of isotropic material manufacturing processes. This investigation proposes a modeling methodology to simulate the 3D director field in full-scale film extrusion geometries. Wide-angle x-ray scattering (WAXS) is used to validate the predicted orientation for a standard coat-hanger die, and is compared with macroscopic mechanical, thermal, and dielectric testing of LCP film to illustrate the morphological dependence of the polymer properties. The highly anisotropic orientation state resulting from cast film extrusion is both predicted by the model and confirmed experimentally, and this preferred orientation is shown to correlate with observed anisotropy in the bulk properties. Additionally, a practical implementation of the modeling tool is presented to simulate directionality in two alternative die geometries designed to improve bulk isotropy, and it is demonstrated that the model is capable of simulating the resulting order for large, irregular domains typical of industrial processing.

A plausible mechanism for longitudinal lock-in of the plant cortical microtubule array after light-induced reorientation

The light-induced reorientation of the cortical microtubule array in dark-grown Arabidopsis thaliana hypocotyl cells is a striking example of the dynamical plasticity of the microtubule cytoskeleton. A consensus model, based on katanin-mediated severing at microtubule crossovers, has been developed that successfully describes the onset of the observed switch between a transverse and longitudinal array orientation. However, we currently lack an understanding of why the newly populated longitudinal array direction remains stable for longer times and re-equilibration effects would tend to drive the system back to a mixed orientation state. Using both simulations and analytical calculations, we show that the assumption of a small orientation-dependent shift in microtubule dynamics is sufficient to explain the long-term lock-in of the longitudinal array orientation. Furthermore, we show that the natural alternative hypothesis that there is a selective advantage in severing longitudinal microtubules, is neither necessary nor sufficient to achieve cortical array reorientation, but is able to accelerate this process significantly.

A plausible mechanism for longitudinal lock-in of cortical microtubule array after light-induced reorientation

The light-induced reorientation of the cortical microtubule array in dark-grown A. thaliana hypocotyl cells is a striking example of the dynamical plasticity of the microtubule cytoskeleton. A consensus model, based on katanin-mediated severing at microtubule crossovers, has been developed that successfully describes the onset of the observed switch between a transverse and longitudinal array orientation. However, we currently lack an understanding of of why the newly populated longitudinal array direction remains stable for longer times, when the initial trigger for the reorientation has died out, and re-equilibration effects would tend to drive the system back to a mixed orientation state. Using both simulations and analytical calculations, we show that the assumption of a small orientation-dependent shift in microtubule dynamics is sufficient to explain the long term lock-in of the longitudinal array orientation. Furthermore, we show that the natural alternative hypothesis that there is a selective advantage in severing longitudinal microtubules, is neither necessary nor sufficient to achieve cortical array reorientation, but is able to accelerate this process significantly.

Peran Pernyataan Orientasi Tujuan Kinerja dan Pernyataan Orientasi Tujuan Pembelajaran terhadap Kemampuan Mahasiswa

Objective orientation is a mental framework as individuals give an impression and respond to situations or events that they face. Typically there are two kinds of goal orientation, namely orientation of performance goals (performance orientation) and orientation of learning goals (learning goal orientation). The phenomenon that there are still students who are extension or extension of time in doing the Final Project are all caused by the ability possessed by the individual. There are three factors that underlie the ability; self-efficacy, goals and performance. The researcher followed up the existence problems in the 2015 Accounting Study Program with four samples of Higher Education in Jember Regency, there were 82 respondents to answer the questionnaire given. Therefore an analytical tool is needed to determine the abilities possessed by students of the Accounting Study Program in Jember Regency. In this study researchers will test performance goal orientation statements (state performance goal orientation) and statement of learning goal orientation (state learning goal orientation) on self-efficacy (self-efficacy), goals (goals) and performance (performance) that each individual has. Keywords: Goal Orientation, State Goal Orientation Learning, State Performance Goal Orientation, Self-Efficacy, Goals and Performance.

Learning the Macroscopic Flow Model of Short Fiber Suspensions from Fine-Scale Simulated Data

Fiber–fiber interaction plays an important role in the evolution of fiber orientation in semi-concentrated suspensions. Flow induced orientation in short-fiber reinforced composites determines the anisotropic properties of manufactured parts and consequently their performances. In the case of dilute suspensions, the orientation evolution can be accurately described by using the Jeffery model; however, as soon as the fiber concentration increases, fiber–fiber interactions cannot be ignored anymore and the final orientation state strongly depends on the modeling of those interactions. First modeling frameworks described these interactions from a diffusion mechanism; however, it was necessary to consider richer descriptions (anisotropic diffusion, etc.) to address experimental observations. Even if different proposals were considered, none of them seem general and accurate enough. In this paper we do not address a new proposal of a fiber interaction model, but a data-driven methodology able to enrich existing models from data, that in our case comes from a direct numerical simulation of well resolved microscopic physics.

Multi-Scale Modeling and Simulation of Thermoplastic Automated Tape Placement: Effects of Metallic Particles Reinforcement on Part Consolidation

This paper concerns engineered composites integrating metallic particles to enhance thermal and electrical properties. However, these properties are strongly dependent on the forming process itself that determines the particle distribution and orientation. At the same time, the resulting enhanced thermal properties affect the reinforced resin viscosity whose flow is involved in the intimate contact evolution. Thus, a subtle and intricate coupling appears, and the process cannot be defined by ignoring it. In this paper, we analyze the effects of particle concentration and orientation on the process and processability. For this purpose, three main models are combined: (i) a multi-scale surface representation and its evolution, by using an appropriate numerical model; (ii) flow-induced orientation, and (iii) the impact of the orientation state on the homogenized thermal conductivity.

Simulation of Liquid Crystal Polymer Directionality During Cast Film Extrusion

Liquid crystal polymers (LCP’s) comprise a class of melt-processable materials that derive specialized mechanical, chemical, and electrical properties from long-range molecular ordering. This unique microstructure gives rise to anisotropic bulk behavior that can be problematic for industrial applications, and thus the ability to model the orientation state in the polymer is necessary for the design of isotropic material manufacturing processes. Previous efforts to model LCP directionality have been primarily restricted to structured grids and simple geometries that demonstrate the underlying theory, but fall short of simulating realistic manufacturing geometries. In this investigation, a practical methodology is proposed to simulate the director field in full-scale melt-processing set-ups, specifically cast film extrusion, to predict the bulk material orientation state. The hybrid approach utilizes separate simulations for the polymer flow with commercial computational fluid dynamics (CFD) software, and the material directionality through a user-defined post-processing script. Wide-angle x-ray scattering (WAXS) is used to experimentally validate the simulated directionality during extrusion processing. It is shown that the model is capable of predicting both the direction and degree of orientation in the polymer resulting from processing, and the model produces strong agreement with experimental measurement of the polymer orientation state.