Hongyi Xu

• PhD
• Professor (Assistant) at University of Connecticut

Conventional predictive modeling of parametric relationships in manufacturing processes is limited by the subjectivity of human expertise and intuition on the one hand and by the cost and time of experimental data generation on the other hand. This work addresses this issue by establishing a new Large Language Model (LLM) framework. The novelty lie...

Engineered architectured Materials, such as metamaterials with periodic patterns, achieve superior properties compared with their stochastic counterparts, such as the random microstructures found in natural materials. The primary research question focuses on the feasibility of learning advantageous microstructural features from stochastic microstru...

Mechanical metamaterials are artificial structures that possess exceptional mechanical properties that are not naturally occurring. The complex geometrical and topological features of these metamaterials pose significant challenges to both structure design and manufacturing, despite the recent rapid development of additive manufacturing (AM) techni...

This paper presents a deep generative model-based design methodology for tailoring the structural stochasticity of microstructures. Although numerous methods have been established for designing deterministic (periodic) or stochastic microstructures, a systematic design approach that allows the unified treatment of both deterministic and stochastic...

Electrochemical and mechanical properties of lithium‐ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure mo...

In-field Additive Manufacturing (AM) is exposed to irregular variations in process conditions (externalities) that affect defect dynamics. These externalities are invariable in conventional in-factory AM. Stoppage-free and real-time mitigation of part defects induced by these externality variations is necessary for timely delivery of quality parts...

Additive manufacturing enables the fabrication of complex designs while minimizing waste, but faces challenges related to defects and process anomalies. This study presents a novel multimodal Retrieval-Augmented Generation-based framework that automates anomaly detection across various Additive Manufacturing processes leveraging retrieved informati...

Mechanical metamaterials represent an innovative class of artificial structures, distinguished by their extraordinary mechanical characteristics, which are beyond the scope of traditional natural materials. The use of deep generative models has become increasingly popular in the design of metamaterial units. The effectiveness of using deep generati...

Applying external pressure to a pouch cell results in improved performance, implicating systems-level design of batteries. Here, different formats and amounts of external pressure to Li-LixNi0.8Mn0.1Co0.1O2 (Li-NMC811) pouch cells were studied under lean electrolyte conditions. Due to the more uniform lithium plating/stripping, a constant gap fixtu...

Cell-level battery models, most of which rely on the successful porous electrode theories, effectively estimate cell performance. However, pinpointing the contributions of individual components of an electrode remains challenging. In...

In this paper, we propose and compare two novel deep generative model-based approaches for the design representation, reconstruction, and generation of porous metamaterials characterized by complex and fully connected solid and pore networks. A highly diverse porous metamaterial database is curated, with each sample represented by solid and pore ph...

Porous metamaterial units filled with fluid have been used in engineering systems due to their ability to achieve desired properties (e.g., effective thermal conductvity). Designing 3D porous metamaterial units while ensuring complete connectivity of both solid and pore phases presents a significant challenge. In this study, we propose a generative...

Increasing the thickness of the electrodes is considered the primary strategy to elevate battery energy density. However, as the thickness increases, rate performance, cycling performance, and mechanical stability are affected due to the sluggish ion transfer kinetics and compromised structural integrity. Inspired by the natural hierarchical porous...

Designing 3D porous metamaterial units while ensuring complete connectivity of both solid and pore phases presents a significant challenge. This complete connectivity is crucial for manufacturability and structure-fluid interaction applications (e.g., fluid-filled lattices). In this study, we propose a generative graph neural network-based framewor...

In this paper, we propose and compare two novel deep generative model-based approaches for the design representation, reconstruction, and generation of porous metamaterials characterized by complex and fully connected solid and pore networks. A highly diverse porous metamaterial database is curated, with each sample represented by solid and pore ph...

Mechanical metamaterials represent an innovative class of artificial structures, distinguished by their extraordinary mechanical characteristics, which are beyond the scope of traditional natural materials. The use of deep generative models has become increasingly popular in the design of metamaterial units. The effectiveness of using deep generati...

The design of floating offshore wind turbines requires a rigorous method for handling the complex multi-systems and multi-physics interactions involved. The traditional method optimizes subsystems of different disciplines sequentially and independently and the control is always implemented at the last step. This neglects the coupling between sub-sy...

Bridging the gaps among various categories of stochastic microstructures remains a challenge in the design representation of microstructural materials. Each microstructure category requires certain unique mathematical and statistical methods to define the design space (design representation). The design representation methods are usually incompatib...

Bridging the gaps among various categories of stochastic microstructures remains a challenge in the design representation of microstructural materials. Each microstructure category requires certain unique mathematical and statistical methods to define the design space (design representation). The design representation methods are usually incompatib...

It has been widely recognized that structural materials are better designed simultaneously with the target component so that their properties can be tailored to the service loading conditions to meet component performance. This concurrent approach helps avoid over-strengthening the materials and components through unnecessary reinforcements, leadin...

Starting in 2020, ten faculty members of the University of Connecticut’s (UConn) Master of Engineering program in Advanced Systems Engineering applied four existing competency frameworks to define the unique aspects of their professional training program using a competency-based education approach. The four frameworks include the 21st Century Cyber...

Quantification and propagation of aleatoric uncertainties distributed in complex topological structures remain a challenge. Existing uncertainty quantification and propagation approaches can only handle parametric uncertainties or high dimensional random quantities distributed in a simply connected spatial domain. There lacks a systematic method th...

Phononic metamaterials have the capability to manipulate the propagation of mechanical waves. The traditional finite element (FE) analysis-based methods for predicting phonon dispersion curves are computationally expensive for structure optimization that may require thousands of design evaluations, especially when applied to high-resolution metamat...

The utilization of silicon anodes in all‐solid‐state lithium batteries provides good prospects for facilitating high energy density. However, the compatibility of sulfide solid‐state electrolytes (SEs) with Si and carbon is often questioned due to potential decomposition. Herein, operando X‐ray absorption near‐edge structure (XANES) spectroscopy, e...

The utilization of silicon (Si) anodes in all-solid-state lithium batteries (ASLBs) provides the potential for high energy density. However, the compatibility of sulfide solid-state electrolytes (SEs) with Si and carbon is often questioned due to potential decomposition. To investigate this, operando X-ray absorption near-edge structure (XANES) spe...

Phononic metamaterials are widely used to attenuate wave propagation. However, designing the structure of phononic metamaterial remains a challenge. In this work, we proposed a transfer learning-based design framework to accelerate the design of phononic metamaterials with wide bandgaps. First, we establish a transfer learning model with convolutio...

Tread compounding has always been faced with the simultaneous optimization of multiple performance properties, most of which have tradeoffs between the properties. The search for overcoming these conflicting tradeoffs have led many companies in the tire industry to discover and develop material physics-based platforms. This report describes some of...

Phononic bandgap metamaterials, which consist of periodic cellular structures, are capable of absorbing energy within a certain frequency range. Designing metamaterials that trap waves across a wide wave frequency range is still a challenging task. In this paper, we present a deep feature learning-based design framework for both unsupervised genera...

Li–S Batteries In article number 2103048, Bin Li and co‐workers report the results of an operando pressure study on a practical Li–S pouch cell, which reveals the role of pressure and the failure mechanisms due to poor wetting. Li metal anode degradation can also be diagnosed in real time. These results shed light on the rational design of high‐ene...

Stimulation is an integral part of any human machine interface such as wearable devices. Recently, an approach based on vibro-tactile communication through metasurfaces has been introduced, where a grid of geometric patterns can induce targeted sensations on the human skin, amplifying both input force and displacement, upon contact. The previous wo...

For lithium–sulfur battery commercialization, research at a pouch cell level is essential, as some problems that can be ignored or deemed minimal at a smaller level can have a greater effect on the performance of the larger pouch cell. Herein, the failure mechanisms of Li–S pouch cells are deeply investigated via in operando pressure analysis. It i...

Phononic bandgap metamaterials, which consist of periodic cellular structures, are capable of absorbing energy within a certain frequency range. Designing metamaterials that trap waves across a wide wave frequency range is still a challenging task. In this study, we proposed a deep feature learning-based framework to design cellular metamaterial st...

Machine learning classification techniques have been used widely to recognize the feasible design domain and discover hidden patterns in engineering design. An accurate classification model needs a large dataset; however, generating a large dataset is costly for complex simulation-based problems. After training by a small dataset, surrogate models...

Understanding the relationships between microstructural characteristics and multiphysics properties is key to designing battery electrode materials for desired properties. Significant efforts have been made to achieve a quantitative understanding of the relationship between mass transport properties and Li-ion microstructure characteristics [1-3] i...

div class="section abstract"> Rapid development of Laser Powder Bed Fusion (L-PBF) technology enables almost unconstrained design freedom for metallic parts and components in automotive industry. However, the mechanical properties of L-PBF alloys, AlSi10Mg for example, have shown significant differences when compared with their counterparts via con...

To relieve the computational cost of design evaluations using expensive finite element (FE) simulations, surrogate models have been widely applied in computer-aided engineering design. Machine learning algorithms (MLAs) have been implemented as surrogate models due to their capability of learning the complex interrelations between the design variab...

The traditional structural optimal design methods aiming to generate a global optimum may fall into the unfeasible domain due to the presence of uncertainty. This issue can be addressed by generating a group of satisfactory design or sub-design regions rather than a single optimal one. A data mining method has been recently developed based on the d...

The complex topological characteristics of network-like structural systems, such as lattice structures, cellular metamaterials, and mass transport networks, pose a great challenge for uncertainty quantification (UQ). Existing UQ approaches are only applicable to parametric uncertainties or high dimensional random quantities distributed in a simply...

A new approach to generate high-fidelity 3D microstructure reconstructions by leveraging resolution and sample volume characteristics from 2D and 3D microscopy methods is presented. This approach is employed to model the microstructure of a highly orthotropic polypropylene separator used in lithium-ion batteries, which have challenging multi-scale...

Modern tire compound design is confronted with the simultaneous optimization of multiple performance properties, most of which have tradeoffs between the properties. In order to uncover new design principles to overcome these historical tradeoffs, multiscale compound experiment, physics, and simulation are being developed and integrated into next-g...

Machine learning for classification has been used widely in engineering design, for example, feasible domain recognition and hidden pattern discovery. Training an accurate machine learning model requires a large dataset; however, high computational or experimental costs are major issues in obtaining a large dataset for real-world problems. One poss...

Chopped carbon fiber sheet molding compound has a great potential in lightweight automotive, marine, and aerospace applications. One of the most challenging tasks is to predict the failure strength of the material due to its anisotropy and heterogeneity, as well as complex stress states in real-world working conditions. In this paper, a novel const...

Description The 24 peer-reviewed papers in this book were presented at the Fourth ASTM International Symposium on the Structural Integrity of Additive Manufactured Materials and Parts held in 2019. Held in coordination with partners from government, industry, and academia, this event provided a forum for the exchange of ideas regarding the structur...

Network-like engineering systems, such as transport networks and lattice metamaterials, are featured by high dimensional, complex topological characteristics, which pose a great challenge for uncertainty quantification (UQ). Existing UQ approaches are only applicable to parametric uncertainties, or high dimensional random quantities distributed in...

Metal parts manufactured via Powder Bed Fusion (PBF) process show great potential in industrial applications. Hierarchical, heterogeneous microstructure characteristics of the PBF-built alloys pose a significant challenge to the prediction of structural performances. To enable computational engineering of this type of materials, multiscale microstr...

To relieve the computational cost of design evaluations using expensive finite element simulations, surrogate models have been widely applied in computer-aided engineering design. Machine learning algorithms (MLAs) have been implemented as surrogate models due to their capability of learning the complex interrelations between the design variables a...

Metamaterials are engineered structural materials with special mechanical properties (e.g., negative Poisson’s ratio) that are not found in nature materials. The properties of the metamaterials can be tailored by designing the cellular structure at the mesoscale. Additively manufactured metamaterial structures provide new opportunities for the deve...

Dry-processed polyolefin film is currently the most widely-applied type of separators for commercial lithium-ion batteries. Its porous structure could undergo large deformation during normal charging-discharging cycles, affecting the mass transfer process through the electrolyte, and during mechanical abuse loadings, leading to the direct contact b...

Gaussian random field has been widely applied to quantify high dimensional uncertainties in the spatial or temporal domain. A common practice in Gaussian random field modeling is to use the exponential function to represent the covariance matrix. However, the exponential function-based covariance formulation does not allow negative values, thus it...

Computational analyses of emerging materials such as fibrous composites rely on multiscale simulations. To account for the inherent uncertainty in these materials, such simulations must be integrated with statistical uncertainty quantification (UQ) and propagation (UP) methods. However, limited advancement has been made in this regard due to the si...

This paper presents a 3D microstructure model to predict mechanical behaviors of the anisotropic battery separator. A statistical characterization and stochastic reconstruction method is established to enable 3D microstructure modeling based on 2D microscopic images. Statistics of the key microstructure characteristics, such as porosity and the sha...

This paper presents a computational framework to predict the tensile failure of chopped carbon fiber SMC composites based on the Representative Volume Element (RVE) model. In the framework, a conforming mesh-based reconstruction algorithm is developed to generate mesostructure RVE models of SMC based on the statistical characteristics. Moreover, co...

Variance and sensitivity analysis are challenging tasks when the evaluation of system performances incurs a high-computational cost. To resolve this issue, this paper investigates several multifidelity statistical estimators for the responses of complex systems, especially the mesostructure-structure system manufactured by additive manufacturing. F...

Design optimization of composite structures is more challenging than design optimization of metal structures due to the larger dimensionality of the design space. In addition to the geometric variables (e.g. thickness of each component), the composite layup (the fiber orientation of each layer) also needs to be considered as design variables in opt...

Topology optimization is being widely employed in designing metamaterial unit cells which can be utilized as core materials for structural components. In this paper, we carried out multi-material topology optimization using orthotropic metamaterials. After topology optimization, the microstructures of the metamaterials are mapped to the resulted ir...

Woven fiber composites have been increasingly employed as light-weight materials in aerospace, construction, and transportation industries due to their superior properties. These materials possess a hierarchical structure that necessitates the use of multiscale simulations in their modeling. To account for the inherent uncertainty in materials, suc...

Chopped carbon fiber sheet molding compound (SMC) material is a promising material for mass-production lightweight vehicle components. However, the experimental characterization of SMC material property is a challenging task and needs to be further investigated. There now exist two ASTM standards (ASTM D7078/D7078M and ASTM D5379/D5379M) for charac...

Predicting the mechanical behavior of the chopped carbon fiber Sheet Molding Compound (SMC) due to spatial variations in local material properties is critical for the structural performance analysis but is computationally challenging. Such spatial variations are induced by the material flow in the compression molding process. In this work, a new mu...

The purpose of multi-layer composite structure optimization is to find the optimal composite layout, such that superior structure performances and lightweight can be achieved. However, the existing optimization methods have a low efficiency when applied to the multi-component, multi-layer composite structure. Such low efficiency is caused by the hi...

Process integration and optimization is the key enabler of the Integrated Computational Materials Engineering (ICME) of carbon fiber composites. In this work, automated workflows are developed for two types of composites: Sheet Molding Compounds (SMC) short fiber composites, and multi-layer unidirectional (UD) composites. For SMC, the proposed work...

To advance vehicle lightweighting, chopped carbon fiber sheet molding compound (SMC) is identified as a promising material to replace metals. However, there are no effective tools and methods to predict the mechanical property of the chopped carbon fiber SMC due to the high complexity in microstructure features and the anisotropic properties. In th...

Compression molded SMC composed of chopped carbon fiber and resin polymer which balances the mechanical performance and manufacturing cost presents a promising solution for vehicle lightweight strategy. However, the performance of the SMC molded parts highly depends on the compression molding process and local microstructure, which greatly increase...

Evolutionary multi-objective optimization has established itself a core field of research and application, with a proliferation of algorithms derived. During the multi-objective optimization processes, the discovered ideal solutions should be diversely distributed at the Pareto front. In order to measure and compare the performances of different mu...

To provide a seamless integration of manufacturing processing simulation and fiber microstructure modeling, two new stochastic 3D microstructure reconstruction methods are proposed for two types of random fiber composites: random short fiber composites, and Sheet Molding Compounds (SMC) chopped fiber composites. A Random Sequential Adsorption (RSA)...

To establish metamodels for the multi-material structure design problems, the material selection of each component is considered as a categorical design variable. One challenging task is to establish an accurate mixed-variable metamodel. It is critical to reduce the prediction error of the mixed-variable metamodel in order to achieve a feasible des...

Model bias can be normally modeled as a regression model to predict potential model errors in the design space with sufficient training data sets. Typically, only continuous design variables are considered since the regression model is mainly designed for response approximation in a continuous space. In reality, many engineering problems have discr...

Developing process-structure relationships that predict the impact of the filler-matrix interfacial thermodynamics is crucial to nanocomposite design. This work focuses on developing quantitative relationships between the filler-matrix interfacial energy, the processing conditions, and the nanoparticle dispersion in polymer nanocomposites. We use a...

In structural design optimization, it is challenging to determine the optimal dimensions and material for each component simultaneously. Material selection of each part is always formulated as a categorical design variable in structural optimization problems. However, it is difficult to solve such mixed-variable problems using the metamodelbased st...

Data-driven random process models have become increasingly important for uncertainty quantification (UQ) in science and engineering applications, due to their merit of capturing both the marginal distributions and the correlations of high-dimensional responses. However, the choice of a random process model is neither unique nor straightforward. To...

Multiobjective, multidisciplinary design optimization (MDO) of complex system is challenging due to the long computational time needed for evaluating new designs’ performances. Heuristic optimization algorithms are widely employed to overcome the local optimums, but the inherent randomness of such algorithms leads to another disadvantage: the need...

In designing microstructural materials systems, one of the key research questions is how to represent the microstructural design space quantitatively using a descriptor set that is sufficient yet small enough to be tractable. Existing approaches describe complex microstructures either using a small set of descriptors that lack sufficient level of d...

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs’ performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical...

In designing microstructural materials systems, one of the key research questions is how to represent the microstructural design space quantitatively using a descriptor set that is sufficient yet small enough to be tractable. Existing approaches describe complex microstructures either using a small set of descriptors that lack sufficient level of d...

3D reconstructions of heterogeneous microstructures are important for assessing material properties using advanced simulation techniques such as finite element analysis (FEA). Nevertheless, for many materials systems like polymer nanocomposites, only 2D microstructural images are available even with the state-of-the-art imaging techniques. This pap...

In designing a microstructural materials system, there are several key questions associated with design representation, design evaluation, and design synthesis: how to quantitatively represent the design space of a heterogeneous microstructure system using a small set of design variables, how to efficiently reconstruct statistically equivalent micr...

In designing a microstructural materials system, there are several key questions associated with design representation, design evaluation, and design synthesis: how to quantitatively represent the design space of a heterogeneous microstructure system using a small set of design variables, how to efficiently reconstruct statistically equivalent micr...

Accelerated insertion of nanocomposites into advanced applications is predicated on the ability to perform a priori property predictions on the resulting materials. In this paper, a paradigm for the virtual design of spherical nanoparticle‐filled polymers is demonstrated. A key component of this “Materials Genomics” approach is the development and...

Design of high performance materials system requires highly efficient methods for assessing microstructure-property relations of heterogeneous materials. Toward this end, a domain decomposition, affordable analysis, and subsequent stochastic reassembly approach is proposed in this paper. The approach hierarchically decomposes the statistically repr...

In designing a microstructural materials system, there are several key questions associated with design representation, design evaluation, and design synthesis: how to quantitatively represent the design space of a heterogeneous microstructure system using a small set of design variables, how to efficiently reconstruct statistically equivalent micr...

Recognizing that modern materials contain multiple phases with inherent random microstructure and in situ constituent material properties that are oft uncharacterizable with exactness, this research uses benchmark computational studies to unveil scenarios where uncertainties significantly affect macroscopic behavior. The benchmark studies, which se...

Efficient and accurate analysis of materials behavior across multiple scales is critically important in designing complex materials systems with exceptional performance. For heterogeneous materials, apparent properties are typically computed by averaging stress-strain behavior in a statistically representative cell. To be statistically representati...

Heavy-duty roller bearing is a kind of machine element widely used in large equipment, and its internal high stress has great influence on its service life. A finite element model for dynamic contact analysis of the roller bearing is established and then two kinds of plastic material models, namely, the bilinear isotropic models and the plastic kin...

Microoscillation is a typical case of transient motion, which occurs in many machine elements, including rolling or sliding element bearings, cams, and gears. Wear is easy to occur on the surface of such elements, particularly at the end point of the stroke, where the surfaces are momentarily static. In the present work, an experimental investigati...