February 20, CAMML is awarded the prestigious NSF CAREER Award, entitled "CAREER: Multiscale Reduced Order Modeling and Design to Elucidate the Microstructure-Property-Performance Relationship of Hybrid Composite Materials". This award will allow his team to leverage the multiscale modeling and design expertise at CAMML, and partner with the ME department, SoC, ARCC, WyrkShop, Idaho National Laboratory and industry partners, to conduct a series fully integrated research, ecuation, outreach and workforce development in the next five years. Congratulations!
January 15, new graduate students Md Mahmudul Hasan Mollah (Ph.D.), Md Mahruf Hossain (Ph.D.), and Bashant Bist (M.Sc.) join CAMML! Welcome!
December 30, our paper entitled "Modeling of through-thickness FP and VaSC: impact of sacrificial fiber configurations" is published on Composites Science and Technology! Congratulations!
November 1, Zhuoting delivered a presentation at the IMECE being held in New Orleans, LA. The title of her presentation is: “A Numerical Study on Closed-Loop Control System for Frontal Polymerization-Assisted Layer-by-Layer Additive Manufacturing”. A copy of the abstract is attached below.
Abstract
In the realm of polymer and polymer composite manufacturing, a novel curing strategy known as frontal polymerization (FP) has emerged. FP relies on a self-propagating exothermic reaction front to achieve rapid polymerization of thermoset monomer resins. Its remarkable energy efficiency and ability to swiftly produce fully cured thermosets have led to the development of numerous applications in engineering fields, notably in the field of 3D printing. While existing research showcases successful fabrication of structures via FP-based 3D printing, a prominent challenge lies in the determination of optimal printing process parameters, which presently relies on a trial-and-error approach. This limitation poses a hindrance to the broader application and scale up of FP-based 3D printing for practical use. In our previous work, we developed a multiphysics modeling framework for FP-based 3D printing process within the Multiphysics Object-Oriented Simulation Environment (MOOSE), where the coupled thermos-chemical process was solved along with the element activation which accounts for ink deposition. This model was experimentally validated and adopted investigate the relationship between the printing velocity, printing length and the front behavior, temperature distribution, polymerization process. We confirm the self-regulative response of the front within certain threshold of distance between front and nozzle, and printing velocity. However, the balance between front and nozzle is hard to achieve during constant printing velocity printing process, eventually might resulting in deformation of filament. In this presentation, we demonstrate a closed-loop control simulation system to adapt the printing velocity during the printing process such that the distance between the nozzle and the polymerization front is within a specific tolerance to alleviate the deformation of the uncured ink. The simulation first starts with an initial printing velocity and run for ΔT, where the choice of ΔT could be the sensing interval in an experimental setting. The simulation is then paused, and the results are analyzed to compute the distance between the nozzle and front, which is used to determine the maximumly allowed printing velocity for the next time interval. The simulation is then restarted using the newly determined printing velocity and repeats until the printing process completes. We use this numerical closed-loop control system to study the impact of the variable printing velocity on the printing process and the printed part qualities in a layer-by-layer printing setting. The analysis also provides guidance for developing an experimental closed-loop control set up, where an IR camera is used for monitoring and the printing velocity will be determined and executed on the fly.
July 24, Dr. Zhang delivered a presentation at the USNCCM, being held in Albuquerque, NM. The title of his presentation is : “Adaptive Eigendeformation-Based Reduced-Order Homogenization Model for Composite Materials Under Volumetric and Interfacial Damage;. A copy of the abstract is attached below.
Abstract
Composite materials play a vital role in aerospace engineering, particularly in applications subject to extreme conditions. Computational modeling has emerged as a valuable tool for the predictive evaluation and design of composite structures, addressing the challenges associated with capturing highly nonlinear behaviors at the microstructural scale and efficiently upscaling from material microstructures to structural components. Among the existing modeling techniques, reduced order modeling (ROM) has gained significant attention due to its ability to balance computational cost and efficiency for simulating of complex composite materials. The eigendeformation-based reduced-order homogenization model (EHM) has proven effective in capturing inelastic responses of complicated microstructures by assuming uniform responses over each part in the model. But when involving highly complex geometries and nonlinear behaviors, the computational expense remains substantial in EHM. Efforts have been made to enhance the efficiency of EHM through the implementation of a Uniform Adaptive Reduced Order Modeling (UAROM) approach utilizing the Interface Generalized Finite Element Method (IGFEM). However, a limitation of the existing UAROM is the lack of control over the number of parts and refinement during the simulation process. This limitation restricts the flexibility of the method, hindering its applications in modeling highly complex damage initiation and propagation where adaptability and fine control are essential. To tackle these challenges, we propose a strategy termed the Non-uniform Adaptive Reduced Order Model (NUAROM) for enhanced modeling of composite materials, which draws inspiration from Adaptive Finite Element Methods (AFEMs) that have successfully handled similar problems in the past. In contrast to the uniform approach, the non-uniform method allows for greater flexibility in controlling the model reduction order and fine part refinement during the simulation process. The applicability and accuracy of NUAROM are verified through numerical examples involving simple single inclusion or more complex 10-sphere 3D particulate composites with phase continuum damage and/or cohesive interface damage. It demonstrates that NUAROM possesses flexibility in offering various adaptive refinement strategies through the selection of hyperparameters. The investigation into different partitioning strategies also offers valuable insights into how hyperparameters impact the accuracy of NUAROM. Furthermore, an intriguing discovery indicates that employing fewer adaptive refinements at an opportune moment, even with a smaller number of partitions, can yield superior results compared to a greater number of refinements with a higher partition number. By introducing this flexibility and controllability in NUAROM, NUAROM can improve the accuracy and efficiency of the reduced order modeling technique, facilitating more accurate predictions of composite material behavior.
June 8, Dr. Zhang delivered a presentation at the USNCCM, being held in Atlanta, GA. The title of his presentation is : “A paradigm for fast exploring material response space considering microstructure statistics and application to composite materials;. A copy of the abstract is attached below.
Abstract
It is well recognized that manufacturing process generally introduces variation in the material microstructures, and inherently delivers materials with certain statistical information. In composites, this could be the size distribution of the reinforcement phase (e.g., fiber or particle diameter distribution), nearest neighbor distance distributions, or the phase or interfacial properties (i.e., distribution of the modulus of the fibers, or the cohesive stiffness of the interfaces). There is a strong need to obtain the stress-strain response of a large number of similar microstructures (e.g., different instances of the same statistics), either to probe the spreading of the response of materials from a manufacturing process or providing training data for the development of other data-driven methods. While this could be accomplished straightforwardly by using high-performance computing resources, and the easy-parallel nature of those individual problems to generate and run individual direct numerical simulations, computational cost can still be a limiting factor if the number of direct numerical simulations (DNS) is substantially large. We propose a paradigm for fast evolution of many similar microstructures based on the Eigendeformation-based reduced-order homogenization model (EHM). EHM partitions the microstructure into a few sub-domains (also known as parts) and precomputes coefficient tensors including each part’s localization tensor and the interaction tensors between parts. By assuming a uniform strain response over each part, a reduced-order nonlinear system can be solved for the part-wise responses to replace the full field microscale problem, achieving high computational efficiency for moderately low levels of error. Prior efforts of EHM development include the speed up of the ROM solving stage, as well as the incorporation of different deformation mechanisms and coupled physics and the preprocessing stage is normally conducted for a single microstructure and then used in different simulations, where the non-linear solving process is generally 2-5 orders of magnitude faster than DNS. In the current case, different microstructures are considered, and the pre-processing stage need to be conducted for each microstructure, posing a need to accelerate the pre-processing stage. While the pre-processing stage normally use FE-based methods to solve influence functions (i.e., numerical green’s function) and the computational cost scales linearly with the number of microstructure evaluation, we propose to use physics-informed neural networks are adopted to solve the influence functions. We extend the publicly available PINN and further include material or geometry parameters as input, which once trained, can provide fast evaluation of the coefficient tenors of a new microstructure. The coefficient tensors are compared with those from the FE simulations, and the subsequent nonlinear stress-strain response are compared with both DNS and conventional EHM. The verified model were then to study the response space of a particulate composite considering the material and geometry variation.
Pengfei delivered a presentation at the TMS 2023 Annual Meeting & Exhibition, being held in San Diego, CA. The title of his presentation is: “High-fidelity crystal plasticity finite element modeling of multi-phase medium-Mn TWIP-TRIP steel: considerations in microstructure reconstruction and meshing for capturing the influences of phase constituents;. A copy of the abstract is attached below.
Abstract
A high-fidelity crystal plasticity finite element (CPFE) model is developed to understand the influences of phase constituents on the microstructural response in Medium-Mn steel. An automated workflow si first developed to process a set of serial Electron backscatter diffraction (EBSD) scans and stack them together for exact microstructure reconstruction is developed, followed by a meshing process to establish a high-fidelity finite element (FE) model. This FE model is then used in CPFE simulation, accounting for both twining- and transformation-induced plastic deformation. A series of microstructures are reconstructed from the same set of EBSD data with different resolutions when processing the EBSD data, and element sizes and types. Both the overall stress-strain response, as well as the local response are studied and compared, which identifies the appropriate resolution and element size needed in capturing the response in different constituents. The influence of different constituents are then studied systematically.
Min delivered a presentation at the SciTech 2023, being held in National Harbor, MD. The title of his presentation is: “Adaptive Eigendeformation-Based Reduced-Order Homogenization Model for Composite Materials Under Volumetric and Interfacial Damage;. The corresponding conference publications can be accessed here! Congratulations!
Zhuoting delivered a presentation at the SciTech 2023, being held in National Harbor, MD. The title of her presentation is: “Adaptive Eigendeformation-Based Reduced-Order Homogenization Model for CompositeCoupled Thermo-Chemical Modeling of Frontal Polymerization-Assisted Additive Manufacturing of Thermoset Polymer Components;. The corresponding conference publications can be accessed here! Congratulations!
November 31, our paper entitled "Energy-efficient manufacturing of multifunctional vascularized composites" is published on Journal of Composite Materials! Congratulations!
November 2, Dr. Zhang delivered a talk at the IMECE, being held in Columbus, OH. The title of his presentation is: “Adaptive Eigendeformation-based Reduced-Order Homogenization Model for Composite Materials”. A copy of the abstract is attached below.
Abstract
Computational homogenization provides a straightforward way to concurrently couple microscale simulation to a structural simulation, as in FE2, and is readily able to model a wide range of materials and structures. However, the inherent computational cost associated with computational homogenization prohibits its wide application, especially in the case of nonlinear constitutive responses. This has led to an emerging effort to develop reduced-order models (ROM) for multiscale modeling. The Eigendeformation-based reduced-order homogenization model (EHM) is an attractive method for this purpose, and has seen significant advancement with applications to metals, composites, and other heterogeneous materials [1-2]. EHM operates in a computational homogenization setting, with a focus on model order reduction of the microscale problem and is based upon precomputing elastic microstructure information. EHM partitions the microstructure into a number of sub-domains (also known as parts), and precomputes coefficient tensors including each part’s localization tensor and the interaction tensors between parts. By assuming a uniform strain response over each part, a reduced-order nonlinear system can be solved for the part-wise responses to replace the full field microscale problem, achieving high computational efficiency for moderately low levels of error. While previous studies have shown a hierarchical sequence of ROMs ranging from low fidelity-high efficiency to high fidelity -low efficiency can be achieved by different microstructure partitioning, typically only a single ROM is used in the same simulation. In this research, we present an adaptive EHM where the simulation gradually refines the ROM used to better balance efficiency and accuracy. To achieve this adaptivity, we begin with the finest ROM we are considering and compute the coefficient tensors (i.e., localization and interaction tensors). Coarser partitionings are then constructed by combining two finer parts into a single coarser part. This way, coefficient tensors of the coarse parts can be directly computed from the ones associated with the finest ROM. As the initial response is generally linear, a coarse ROM is sufficient to capture this response. Once inelastic deformation starts and localization starts to accumulate, the simulation adaptively switches to a finer ROM to improve computational accuracy. The data transferring between the coarse and refined ROM, as well as the switching criteria are discussed. The performance and accuracy of the proposed framework is evaluated by comparison with EHM with a fixed ROM partition and the reference direct numerical simulations.
November 3, Dr. Zhang delivered a talk at the IMECE, being held in Columbus, OH. The title of his presentation is: “Load-dependent optimal eigendeformation-based multiscale reduced-order homogenization model for polymer composites under volumetric and interfacial damage”. A copy of the abstract is attached below.
Abstract
Matrix damage and interfacial debonding are commonly seen in polymer matrix composites and have been used in computational modeling of composites failure. The highly nonlinear constitutive behavior, as well as fine mesh typically needed for capturing the microstructure feature, makes is computationally expensive for finite element based direct numerical simulation, and promotes the development of various reduced-order multiscale modeling techniques. The Eigendeformation-based reduced-order homogenization model (EHM) is an attractive method for multiscale modeling of heterogeneous materials, in particular composite materials under volumetric and interfacial damage. EHM operates in a computational homogenization setting, with a focus on model order reduction of the microscale problem and is based upon precomputing elastic microstructure information. EHM partitions the microstructure into a number of sub-domains (also known as parts) and precomputes coefficient tensors including each part’s localization tensor and the interaction tensors between parts. By assuming a uniform strain response over each part, a reduced-order nonlinear system can be derived and solved for the part-wise responses to replace the full field microscale problem, achieving high computational efficiency for moderately low levels of error. The choice of ROM in EHM, which consists of splitting the damageable phase regions and imperfect interfaces into a desired number of parts and selecting their geometries. Increasing the number of parts decreases the model error but also increases the computational cost. It is therefore desirable to achieve the lowest error using the fewest number of parts. In this work, we account for the load the microstructure is expected to experience to achieve better accuracy when the number of parts is fixed. To be more specific, the response of the elastic microstructure subjected to the expected load is used in a k-means clustering process, which groups elements of the microstructure that have similar response under this specific load into the same parts. The same approach is applied to both the volume and interfaces of 2D and 3D composite microstructures. We compared the different partitioning of the microstructure based uniaxial and biaxial loadings, as well as their responses compared to those using the same number of parts, but different partitioning that do not account for the load. Guidance was provided regarding the optimal reduced-order model for the cases when the load is known before hand or not.
May 24, Dr. Zhang delivered an invited talk at the MRS Spring meeting, being held in Honolulu, HI. The title of his presentation is: “Multiphysics Modeling and Experimental Study of a Concurrent Polymerization and Vascularization Process for Manufacturing Polymer and Polymer Composites with embedded Microvascular System”. A copy of the abstract is attached below.
Abstract
Biological materials possess hierarchical vascular networks that mediate heat and mass transport in response to external and internal stimuli, enabling complex living systems to thrive in extreme environments. Inspired by these natural systems, there is an increasing desire to replicate such vascular networks in engineered materials for mass and heat transportation in the field of microfluidics, self-regulating temperature control structure, and microelectronics. Lengthy, multistep fabrication processes involving solvents, external heat, and vacuum hinder large-scale application of vascular networks in structural materials. Recently, a novel synchronized fabrication process for vascularized thermosets and composites have been proposed. In this process, the exothermic frontal polymerization (FP) of a liquid or gelled dicyclopentadiene (DCPD) resin facilitates coordinated depolymerization of an embedded sacrificial polymer (propylenecarbonate) (PPC) template to create hollow structures with high-fidelity interconnected micro channels. The chemical energy released during rapid and self-sustained matrix polymerization eliminates the need for asustained external heat source and greatly reduces external energy and time consumption for processing. Tobetter understand this process, and probe the working window of this technique, we develop a multiphysics framework to study the impact of boundary condition, chemical composition, and fiber volume fraction on the vascularization process. In this multiphysics system, a coupled thermo-chemical equation system is solved within the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE), relying on its adaptive mesh refinement and time stepping. In the first part of the presentation, we focus on the so-called linear vascularization, where the direction of the polymerization front is parallel to the sacrificing template. We systematically studied the working window where thorough curing of the matrix as well as full degradation of the sacrificing fiber can be achieved under open-air as well as glass mold conditions. In the second part, we focus on the through-thickness vascularization, where the direction of the polymerization front is through thickness along the thickness direction, hence perpendicular to the sacrificing filament. In this case, we. focus more on studying the impact of carbon fiber and PPC volume fraction of the host composite laminate and investigate the carbon fiber and PPC volume fraction we can achieve using this method. In both cases, the results are compared with experiments and which show reasonable match. Further insights were gained from the computational modeling by exploring relevant parameters associated with the manufacturing process.
May 24, Dr. Zhang delivered another invited talk at the MRS Spring meeting, being held in Honolulu, HI. The title of his presentation is: “Frontal-Polymerization-Based 3D Printing of Thermoset Polymers and Composites: Experiments and Modeling”. A copy of the abstract is attached below.
Abstract
A recent addition to the family of Direct Ink Writing (DIW) methods for 3D printing, Frontal-Polymerization (FP)-based printing has shown promises in the rapid and accurate creation of complex-shape parts made of thermoset polymers and polymer-matrix composites. FP is a process in which a localized reaction zone, driven by the heat generated through an exothermic reaction, propagates through the monomer or gel by converting it to a polymer. By taking advantage of the self-sustained propagation of the polymerization front, the FP-based printing process combines the printing and curing processes by frontally polymerizing the printed filament gel upon extrusion from the printer head. By concurrently solidifying the filament during extrusion, the FP-based process allows for the creation of 3D freestanding objects of complex shapes without the need for a post-processing step. The process can also be adapted to incorporate nano-fillers to produce multifunctional thermoset composites with targeted thermal, mechanical, or electrical properties. The addition of second-phase materials (carbon nanotubes, carbon black particles, short fibers, …) have an impact on the rheological properties (viscosity, extensibility) of the printed material, and thereby on the manufacturing process. After a presentation of some recent developments associated with the printing of dicyclopentadiene (DCPD)-based polymer and composite parts, we will discuss two challenges associated with the FP-based DIW method. The first one consists in the need to synchronize the printing and curing processes, as the propagation speed of the polymerization front must match that of the printer head. We demonstrate that, by taking advantage of the monotonic dependence of the front speed on the temperature of the gel, the polymerization front automatically adapts to changes in the print speed and in the thermal environment. As imple thermo-chemical model is introduced to capture the key features of this inherent closed-loop control oft he 3D printing process. The second issue to be discussed is linked to the dimensional stability of the manufactured part with emphasis on the deformations of the uncured gel prior to the arrival of the polymerization front. To that effect, we develop a coupled thermo-chemo-structural model that incorporates the mechanically, thermally, and chemically driven sources of deformations and their contributions to the final shape of the printed part. The model, which incorporates the potentially large deformations of the gel, is based on an eigenstrain formulation that ‘freezes ‘the deformations of the gel at the arrival of the polymerization front.
Dr. Zhang delivered a presentation at the virtual SciTech 2022 conference. The title of his presentation is : “Microstructure-Informed Reduced-Order Modeling of Fatigue Initiation in a Titanium Skin Panel Subjected to Thermo-Mechanical Loading;. The corresponding conference publications can be accessed here! Congratulations!
December 30, our paper entitled "Sacrificial Cyclic Poly(phthalaldehyde) Templates for Low-Temperature Vascularization of Polymer Matrices" is published on ACS Applied Polymer Materials! Congradulations!
November 10, we are awarded our first NSF proposal. The proposal title is "An Integrated Multiscale Reduced-Order Modeling and Experimental Framework for Lithium-ion Batteries under Mechanical Abuse Conditions". This project is in collaboration with Dr. Chen's group from University of Lousville! Congradulations!
September 11, our paper entitled "Large-deformation reduced order homogenization of polycrystalline materials" is published on Computer Methods in Applied Mechanics and Engineering! Congradulations!
August 30, our paper entitled "Multiscale reduced-order modeling of a titanium skin panel subjected to thermo-mechanical loading" is published on AIAA Journal! Congradulations!
August 16, our paper entitled "Nonlinear guided wave tomography for detection and evaluation of early-life material degradation in plates" is published on Sensors! Congradulations！
July 27, Xiang Zhang presented at the virtually in the 16th USNCCM conference. The title of his presentation is: Nonlinear Microstructure Material Design with Reduced-Order Modeling". A copy of the abstract is attached below.
Abstract
Recent progress in multiscale modeling and sensitivity analysis, together with advancement in additive manufacturing, allow us to develop an integrated workflow to design and manufacture the microstructure geometrical features and constituent properties to deliver a desired stress-strain response. During this process, the prohibitive computational cost associated with multiple optimization iteration and costly evaluation of a single microstructure problem still limits the application of this workflow, especially for the cases that involve complex microstructure and different deformation modes. Here we present a multiscale reduced-order optimization method for efficient nonlinear microstructure material design. This method builds on the recent development on Interface-enriched Generalized Finite Element Method (IGFEM) based reduced-order model, to formulate a reduced order representation of the microstructure problem. Model order reduction is achieved by partitioning the microstructure volume and interface into a number of subdomains called parts, where a series of influence function problems based on the elastic properties of the microstrucrue are solved a priori to obtain the interaction coefficients between different parts and between each part ant the microstructure. Based on these interaction coefficients, and the assumption that the response in each part is uniform, a system of linear algebra equations is derived to replace the microstructure problem with part-wise response as unknows. In addition, the material sensitivities are further derived withing the reduced order system of equation. The reduced-order microstructure problem evaluation, and reduce-order sensitivity analysis allow us to very efficiently optimize the microstructure material properties with multiple initial states, from which we choose the best optimization results and further conduct a full IGFEM-based optimization to obtain the final optimization result. This two-step optimization process is demonstrated to deliver satisfactory results on 3D particulate composites with the presence of both volumetric and interfacial damage compare with pure IGFEM-based optimization.
May 26, Xiang Zhang presented at the virtually in the EMI 2021/PMC 2021 conference. The title of his presentation is: Multiscale Reduced-Order Modeling of a Titanium Skin Panel Subjected to Thermo-Mechanical Loading". A copy of the abstract is attached below.
Abstract
We propose a reduced order multiscale computational approach to predict the response of a polycrystalline structure subjected to thermo-mechanical loading, in which the material microstructure (i.e., at the scale of the representative volume) and all relevant microstructural response mechanisms are directly embedded and fully coupled with a structural analysis. The proposed approach is based on the eigenstrain-based reduced order model previously developed the authors. EHM operates in a computational homogenization settings, which takes the concept of transformation field theory that pre-computes certain microscale information (e.g. localization tensors, concentration tensors) by evaluating linear elastic microscale problems and considers piece-wise constant inelastic response within partitions (e.g., grains) of the microstructure. By this approach, a significant reduction in computational cost is achieved, compared with classical computational homogenization approaches that employ crystal plasticity finite element (CPFE) simulation to describe the microscale response. While previous development considers only mechanical loading, the proposed approach further accounts for the thermal strain at the microscale, as well as temperature dependent material properties and evolution laws. To account for the thermal effects, a set of temperature influence functions, similar to the elastic and inelastic function problems are formulated, and the part-wise thermal strain is accounted for in the reduced order system. The proposed approach was calibrated and validated by a series of uniaxial tensile tests of Ti-6242S at a wide range of temperatures and strain rates. The validated model is then adopted to study the response of a generic aircraft skin panel subjected to thermo-mechanical loading associated with supersonic flight, which demonstrates the capability of the developed model for structural scale simulation that involves thermo-mechanical loading, and provides insights on understanding the plastic deformation as well as fatigue initiation of the panel structure.
May 14, our paper entitled "Rapid synchronized fabrication of vascularized thermosets and composites" is published on Nature Communications! Congradulations！
Feburary 4, our paper entitled "A GFEM-based reduced-order homogenization model for heterogeneous materials under volumetric and interfacial damage" is published on Computer Methods in Applied Mechanics and Engineering! Congradulations！
November 16, Xiang Zhang presented at ASME IMECE 2020 virtually. The title of Xiang’s presentation was: “Integrating GFEM and Eigendeforamtion-based Reduced-Order Homogenization Model for Simulating Heterogeneous Materials Under Volumetric and Interfacial Damage”.
June 11, our paper entitled "Frontal vs. bulk polymerization of fiber-reinforced polymer-matrix composites" is accepted by Composites Science and Technology! Congradulations！
March 11, our book chapter entitled "Transverse Failure of Unidirectional Composites: Sensitivity to Interfacial Properties" is published in Integrated Computational Materials Engineering (ICME)! Congradulations！
January 27, Pengfei Shen joins CAMML as a Ph.D. student. He will be studying fatigue in additivelly manufactured Titanium alloys. Welcome Penfei！
December 11, our paper entitled "Dislocation density informed eigenstrain based reduced order homogenization modeling: verification and application on a titanium alloy structure subjected to cyclic loading" is accepted by Modelling and Simulation in Materials Science and Engineering! Congradulations！
Abstract
September 19, our paper entitled "Manufacturing of unidirectional glass-fiber-reinforced composites via frontal polymerization: A numerical study" is accepted by Composite Science and Technology! Congradulations！
August 27, Xiang Zhang officially started as a an assistant professor in the Mechanical Engineering Department at the University of Wyoming! Welcome!
Abstract
A rapid and energy-efficient manufacturing process for polymer and polymer composites called frontal polymerization (FP) was recently developed. In FP-based manufacturing, only an initial local heat stimulus is required to activate the polymerization, upon which the heat from the exothermic polymerization of the monomer creates a self-propagating polymerization front that transforms the monomers into fully cured polymers. A 3D printing technique that uses FP to simultaneously cure the printed material as it is deposited has also been recently introduced for free-standing polymer components. During this printing process, the polymerizing front follows the printing nozzle and rapidly transforms the viscoelastic filament into a stiff thermoset, thereby eliminating the need for support structures and post curing process and providing high printing accuracy compared to traditional direct ink writing. In this presentation, we start by introducing a coupled thermo-chemo-mechanical model specially developed to model the evolution of temperature, degree of cure, and strain fields during the FP process. The model is first validated against experimental measurements and then used to probe the front characteristics under different experimental settings. We then focus on the development of a design diagram for FP-based 3D printing to maximize the printing efficiency (i.e., maximum printing velocity) while maintaining the desired printing accuracy (i.e., limited deformation of the printed filament). The design space contains parameters that characterize the settings of the printer (e.g., ink temperature, extruding pressure, length and diameter of the nozzle), the nature of the ink (e.g., initial degree of cure and cure kinetics associated with the chosen ink composition), and the printing environment (e.g., ambient temperature and air flow rate).The constraints are associated with equilibrated printing for which the front velocity equals the printing speed, the printing accuracy achieved by limiting the deflection of the deposited filament, the capability of the printer, and non-blocking of the nozzle by a threshold depositing temperature.
July 24, our paper entitled "Experimental and numerical study of mechanical properties of multi-phase medium-Mn TWIP-TRIP steel: Influences of strain rate and phase constituents" is accepted by Acta Materialia! Congradulations!
May 15, our paper entitled "IGFEM-based shape sensitivity analysis of the transverse failure of a composite laminate" is accepted by Computationsl Mechanics. Congradulations!
April 29, Xiang Zhang accepted the offer as an assistant professor in the Mechanical Engineering Department at the University of Wyoming! Congradulations!
Xiang Zhang, Ph.D.,
Assistant Professor of Mechanical Engineering Department
Room 335B, EERB
1000 E. University
Dept. 3295
Laramie, WY 82071
EmaiL: xiang.zhang@uwyo.edu
Phone: 307-766-42381000
Fax: 307.766.2695