Structural Materials Commons^™

Neural Operator And Physics-Informed Deep Learning Approaches For Inverse Design Of Composites And Manufacturing Processes, Minglei Lu

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In this dissertation, artificial intelligence (AI) models are designed and used to accelerate inverse design of composites and manufacturing processes. The critical bottlenecks in machine learning (ML) including data availability, data quality, model generalization and adaptation, interpretability, physical consistency, and the ’black box’ nature of models for the inverse design are addressed. And the proposed AI models are tested under different engineering scenarios. Firstly, a fast deep neural operator (DNO) structure was developed to significantly reduce training time. This model was tested in the context of additive manufacturing, a transformative industrial technology that allows for the creation of materials with …

On The Structure, Energy, And Segregation Behavior Of Grain Boundaries In Metallic Systems, Yasir Mahmood

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Nearly all structural metallic systems are multi-component polycrystalline aggregates; their microstructures are composed of crystalline grains that are internally joined at grain boundaries (GBs). This thesis focuses on GB structure, energy, and chemistry, as these greatly influence many material properties and processes, including boundary dynamics during processing treatments or under operating conditions.

Using atomistic simulations, we examine the impact of metastable GB structures on solute segregation. A wide range of GB geometries and metastable structures are used in our study. The Al-Mg alloy is used because it is of interest for light weighting. The atomistic simulation results are used to …

Physics-Informed Machine Learning Methods For Inverse Design Of Multi-Phase Materials With Targeted Mechanical Properties, Yunpeng Wu

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Advances in machine learning algorithms and applications have significantly enhanced engineering inverse design capabilities. This work focuses on the machine learning-based inverse design of material microstructures with targeted linear and nonlinear mechanical properties. It involves developing and applying predictive and generative physics-informed neural networks for both 2D and 3D multiphase materials.

The first investigation aims to develop a machine learning method for the inverse design of 2D multiphase materials, particularly porous materials. We first develop machine learning methods to understand the implicit relationship between a material's microstructure and its mechanical behavior. Specifically, we use ResNet-based models to predict the elastic …

Numerical Simulation Of Laser Induced Elastic Waves In Response To Short And Ultrashort Laser Pulses., Alireza Zarei

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In an era of intensified market competition, the demand for cost-effective, high-quality, high-performance, and reliable products continues to rise. Meeting this demand necessitates the mass production of premium products through the integration of cutting-edge technologies and advanced materials while ensuring their integrity and safety. In this context, Nondestructive Testing (NDT) techniques emerge as indispensable tools for guaranteeing the integrity, reliability, and safety of products across diverse industries.

Various NDT techniques, including ultrasonic testing, computed tomography, thermography, and acoustic emissions, have long served as cornerstones for inspecting materials and structures. Among these, ultrasonic testing stands out as the most prevalent method, …

A Manufacturing-To-Response Pathway For Manufacturing Optimization Of Carbon Fiber Reinforced Polymer Composite Structures, Madhura Limaye

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Over the past decade, there has been an increased adoption of thermoplastic and thermoset based continuous carbon fiber reinforced polymer (CFRP) composites for structural applications in several industries. Among the different manufacturing methods, thermoforming process for thermoplastic based continuous CFRP’s offer a major advantage in reducing cycle times for large scale productions. Similarly, out-of-autoclave curing process for thermoset based continuous CFRP’s using heated tooling enables production of large composite structures. However, these manufacturing processes can have a significant impact on the structural performance of parts by inducing undesirable effects. These effects include inhomogeneous fiber orientations, thickness variations, and residual stresses …

Cfrp Delamination Density Propagation Analysis By Magnetostriction Theory, Brandon Eugene Williams

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While Carbon Fiber Reinforced Polymers (CFRPs) have exceptional mechanical properties concerning their overall weight, their failure profile in demanding high-stress environments raises reliability concerns in structural applications. Two crucial limiting factors in CFRP reliability are low-strain material degradation and low fracture toughness. Due to CFRP’s low strain degradation characteristics, a wide variety of interlaminar damage can be sustained without any appreciable change to the physical structure itself. This damage suffered by the energy transfer from high- stress levels appears in the form of microporosity, crazes, microcracks, and delamination in the matrix material before any severe laminate damage is observed. This …

Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin

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Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …

Carbon Fibers From Bio-Based Precursors Derived From Renewable Sources, Sagar Kanhere

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Carbon fibers have the highest strength and modulus among all known fibers and are used as reinforcements in high-performance composites [1]. Carbon fibers also have a very low density relative to metals. Therefore, carbon fibers possess ultrahigh specific strength and modulus, which make them desirable for high-performance light-weight composites. A vast majority of commercial carbon fibers are produced from PAN precursors that are expensive, which limits the use of PAN-derived carbon fibers to aerospace applications (e.g., airplanes). However, for costsensitive applications, there is a need for low-cost, moderate performance carbon fibers. Lignin is a low-cost by-product of pulping and biorefining …

Crack Control And Bond Performance Of Alternative Coated Reinforcements In Concrete, Sachin Sreedhara

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Concrete cracking in structures is a ubiquitous problem which can lead to the deterioration of the structure. Other than affecting the strength aspect of a structure, cracking impacts the serviceability criteria as well. Although cracking phenomenon in any structure is highly inevitable, it has to be minimized in order to maintain a structure’s life effectively. Cracking in reinforced concrete structures is related to the bond strength developed between the bar and the concrete. It also depends on an ability of the bar to resist the stresses due to shrinkage to minimize the crack. Another important aspect is the resistance offered …

Sustainable Composites From Agricultural And Petroleum Waste, Menisha S. Karunarathna

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Green gas emission has been a pervasive and persistent subject of debate for a prolonged period. The soaring number of industries and vehicle fuel emissions presage a concomitant rise in global CO₂ emissions. Global cement production is responsible for 8% of the total CO₂ release, yet, the production continues due to the surging demand. Hence, there is an ongoing quest to find alternatives for cement and building materials produced with zero to lower CO₂ emissions. The work presented in this dissertation focuses on finding recyclable, zero CO₂ gas-producing high sulfur biocomposites materials, which can compete with …

Mechanical Interfacial Locking And Multiscale Modeling Of Reinforced Thermoplastic Composites For Structural Applications, Anmol Kothari

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The ever-growing pressure of reducing the adverse impact of transportation systems on environment has pushed industries towards fuel-efficient and sustainable solutions. While several approaches have been used to improve fuel efficiency, the light-weighting of structural components has proven broadly effective. In this regard, reinforced thermoplastic composites (RTPC), owing to their high recyclability, higher impact strength and fast cycle times, have become competitive candidates at an industrial scale. However, to implement RTPC toward large scale structural applications several challenges pertaining to material design and manufacturing effects need to be addressed. To this end, a computational study is carried out to address …