Matching Items (102)
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- All Subjects: Mechanical Engineering
- All Subjects: Fatigue
- Creators: Liu, Yongming
- Creators: Chattopadhyay, Aditi
- Creators: Solanki, Kiran
Description
The increasing demand for structural materials with superior mechanical properties has provided a strong impetus to the discovery of novel materials, and innovations in processing techniques to improve the properties of existing materials. Methods like severe plastic deformation (SPD) and surface mechanical attrition treatment (SMAT) have led to significant enhancement in the strength of traditional structural materials like Al and Fe based alloys via microstructural refinement. However, the nanocrystalline materials produced using these techniques exhibit poor ductility due to the lack of effective strain hardening mechanisms, and as a result the well-known strength-ductility trade-off persists. To overcome this trade-off, researchers have proposed the concept of heterostructured materials, which are composed of domains ranging in size from a few nanometers to several micrometers. Over the last two decades, there has been intense research on the development of new methods to synthesize heterostructured materials. However, none of these methods is capable of providing precise control over key microstructural parameters such as average grain size, grain morphology, and volume fraction and connectivity of coarse and fine grains. Due to the lack of microstructural control, the relationship between these parameters and the deformation behavior of heterostructured materials cannot be investigated systematically, and hence designing heterostructured materials with optimized properties is currently infeasible. This work aims to address this scientific and technological challenge and is composed of two distinct but interrelated parts. The first part concerns the development of a broadly applicable synthesis method to produce heterostructured metallic films with precisely defined architectures. This method exploits two forms of film growth (epitaxial and Volmer-Weber) to generate heterostructured metallic films. The second part investigates the effect of different microstructural parameters on the deformation behavior of heterostructured metallic films with the aim of elucidating their structure-property relationships. Towards this end, freestanding heterostructured Fe films with different architectures were fabricated and uniaxially deformed using MEMS stages. The results from these experiments are presented and their implications for the mechanical properties of heterostructured materials is discussed.
ContributorsBerlia, Rohit (Author) / Rajagopalan, Jagannathan (Thesis advisor) / Sieradzki, Karl (Committee member) / Peralta, Pedro (Committee member) / Crozier, Peter (Committee member) / Solanki, Kiran (Committee member) / Arizona State University (Publisher)
Created2021
Description
Stiffness and flexibility are essential in many fields, including robotics, aerospace, bioengineering, etc. In recent years, origami-based mechanical metamaterials were designed for better mechanical properties including tunable stiffness and tunable collapsibility. However, in existing studies, the tunable stiffness is only with limited range and limited controllability. To overcome these challenges, two objectives were proposed and achieved in this dissertation: first, to design mechanical metamaterials with metamaterials with selective stiffness and collapsibility; second, to design mechanical metamaterials with in-situ tunable stiffness among positive, zero, and negative.In the first part, triangulated cylinder origami was employed to build deployable mechanical metamaterials through folding and unfolding along the crease lines. These deployable structures are flexible in the deploy direction so that it can be easily collapsed along the same way as it was deployed. An origami-inspired mechanical metamaterial was designed for on-demand deployability and selective collapsibility: autonomous deployability from the collapsed state and selective collapsibility along two different paths, with low stiffness for one path and substantially high stiffness for another path. The created mechanical metamaterial yields unprecedented load bearing capability in the deploy direction while possessing great deployability and collapsibility. The principle in this prospectus can be utilized to design and create versatile origami-inspired mechanical metamaterials that can find many applications.
In the second part, curved origami patterns were designed to accomplish in situ stiffness manipulation covering positive, zero, and negative stiffness by activating predefined creases on one curved origami pattern. This elegant design enables in situ stiffness switching in lightweight and space-saving applications, as demonstrated through three robotic-related components. Under a uniform load, the curved origami can provide universal gripping, controlled force transmissibility, and multistage stiffness response. This work illustrates an unexplored and unprecedented capability of curved origami, which opens new applications in robotics for this particular family of origami patterns.
ContributorsZhai, Zirui (Author) / Nian, Qiong (Thesis advisor) / Zhuang, Houlong (Committee member) / Huang, Huei-Ping (Committee member) / Zhang, Wenlong (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2021
Description
Carbon nanotubes (CNTs) have emerged as compelling materials for enhancing both electrical and mechanical properties of aerospace structures. Buckypaper (BP), a porous
membrane consisting of a highly cross-linked network of CNTs, can be effectively
integrated with carbon fiber reinforced polymer (CFRP) composites to simultaneously
enhance their electromagnetic interference (EMI) shielding effectiveness (SE) and
mechanical properties. In existing literature, CNT based nanocomposites are shown to
improve the flexural strength and stiffness of CFRP laminates. However, a limited amount
of research has been reported in predicting the EMI SE of hybrid BP embedded CFRP
composites.
To characterize the EMI shielding response of hybrid BP/CFRP laminates, a novel
modeling approach based on equivalent electrical circuits is employed to estimate the
electrical conductivity of unidirectional CFRP plies. This approach uses Monte Carlo
simulations and accounts for the effects of quantum tunneling at the fiber-fiber contact
region. This study specifically examines a signal frequency range of 50 MHz to 12 GHz,
corresponding to the very high to X band spectrum. The results indicate that at a frequency
of 12 GHz, the longitudinal conductivity decreases to around ~3,300 S/m from an initial
DC value of 40,000 S/m, while the transverse conductivity concurrently increases from
negligible to approximately ~12.67 S/m. These results are then integrated into Ansys High
Frequency Structure Simulator (HFSS) to predict EMI SE by simulating the propagation
of electromagnetic waves through a semi-infinite composite shield representative volume
element. The numerical simulations illustrate that incorporating BP allows for significant
ii
improvements in SE of hybrid BP/CFRP composites. At 12 GHz signal frequency, for
example, the incorporation of a single BP interleave enhances the SE of a [90,0] laminate
by up to ~64%, while the incorporation of two BP interleaves in a [90,0,+45,-45,0,90]s
balanced symmetric laminate enhances its SE by ~20% . This enhancement is due to the
high conductivity of BP at high frequencies.
Additionally, to evaluate the flexural property enhancements due to BP, experimental
three-point bend tests were conducted on different configurations of hybrid BP/CFRP
laminates, and their strength and stiffness were compared with the non-BP samples.
Micrographs of failed samples are acquired using an optical microscope, which provides
insights into their underlying damage mechanisms. Fractography analysis confirms the role
of BP in preventing through-thickness crack propagation, attributed to the excellent crack
retardation properties of CNTs.
ContributorsTripathi, Kartik (Author) / Chattopadhyay, Aditi (Thesis advisor) / Henry, Todd C. (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2024
Description
This dissertation contributes to uncertainty-aware neural networks using multi-modality data, with a focus on industrial and aviation applications. Drawing from seminal works in recent years that have significantly advanced the field, this dissertation develops techniques for incorporating uncertainty estimation and leveraging multi-modality information into neural networks for tasks such as fault detection and environmental perception. The escalating complexity of data in engineering contexts demands models that predict accurately and quantify uncertainty in these predictions. The methods proposed in this document utilize various techniques, including Bayesian Deep Learning, multi-task regularization and feature fusion, and efficient use of unlabeled data. Popular methods of uncertainty quantification are analyzed empirically to derive important insights on their use in real world engineering problems. The primary objective is to develop and refine Bayesian neural network models for enhanced predictive accuracy and decision support in engineering. This involves exploring novel architectures, regularization methods, and data fusion techniques. Significant attention is given to data handling challenges in deep learning, particularly in the context of quality inspection systems. The research integrates deep learning with vision systems for engineering risk assessment and decision support tasks, and introduces two novel benchmark datasets designed for semantic segmentation and classification tasks. Additionally, the dissertation delves into RGB-Depth data fusion for pipeline defect detection and the use of semi-supervised learning algorithms for manufacturing inspection tasks with imaging data. The dissertation contributes to bridging the gap between advanced statistical methods and practical engineering applications.
ContributorsRathnakumar, Rahul (Author) / Liu, Yongming (Thesis advisor) / Yan, Hao (Committee member) / Jayasuriya, Suren (Committee member) / Zhuang, Houlong (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2024
Description
The relationships between the properties of materials and their microstructures have been a central topic in materials science. The microstructure-property mapping and numerical failure prediction are critical for integrated computational material engineering (ICME). However, the bottleneck of ICME is the lack of a clear understanding of the failure mechanism as well as an efficient computational framework. To resolve these issues, research is performed on developing novel physics-based and data-driven numerical methods to reveal the failure mechanism of materials in microstructure-sensitive applications.
First, to explore the damage mechanism of microstructure-sensitive materials in general loading cases, a nonlocal lattice particle model (LPM) is adopted because of its intrinsic ability to handle the discontinuity. However, the original form of LPM is unsuitable for simulating nonlinear behavior involving tensor calculation. Therefore, a damage-augmented LPM (DLPM) is proposed by introducing the concept of interchangeability and bond/particle-based damage criteria. The proposed DLPM successfully handles the damage accumulation behavior in general material systems under static and fatigue loading cases.
Then, the study is focused on developing an efficient physics-based data-driven computational framework. A data-driven model is proposed to improve the efficiency of a finite element analysis neural network (FEA-Net). The proposed model, i.e., MFEA-Net, aims to learn a more powerful smoother in a multigrid context. The learned smoothers have good generalization properties, and the resulted MFEA-Net has linear computational complexity. The framework has been applied to efficiently predict the thermal and elastic behavior of composites with various microstructural fields. Finally, the fatigue behavior of additively manufactured (AM) Ti64 alloy is analyzed both experimentally and numerically. The fatigue experiments show the fatigue life is related with the contour process parameters, which can result in different pore defects, surface roughness, and grain structures. The fractography and grain structures are closely observed using scanning electron microscope. The sample geometry and defect/crack morphology are characterized through micro computed tomography (CT). After processing the pixel-level CT data, the fatigue crack initiation and growth behavior are numerically simulated using MFEA-Net and DLPM. The experiments and simulation results provided valuable insights in fatigue mechanism of AM Ti64 alloy.
ContributorsMeng, Changyu (Author) / Liu, Yongming (Thesis advisor) / Hoover, Christian (Committee member) / Li, Lin (Committee member) / Peralta, Pedro (Committee member) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2023
Description
This thesis presents a study of the microstructure and mechanical properties of Yttrium-Zinc (YZn) thin films. Rare-earth intermetallic compounds have gained significant attention in recent years due to their unique structural and mechanical properties, making them suitable for various applications. However, studies on the Y-Zn system are scarce and there are very few published reports on YZn thin films. The main objective of this study is to investigate the microstructure and mechanical properties of YZn thin films using various experimental techniques.In this study, YZn films of various thicknesses were synthesized via magnetron co-sputtering: 200 nm, 500 nm, 1 µm, 2 µm and 11.5 µm. Then these samples were annealed at 250°C, 300°C, 350°C and 400°C to investigate their microstructural evolution and mechanical properties. X-ray diffraction (XRD) and scanning electron microscopy (SEM) based techniques have been used to analyze the microstructure and chemical composition of these compounds. The mechanical properties such as hardness and elastic modulus have been measured using nanoindentation.
The results show that the microstructure of YZn thin films is dependent on the annealing conditions. The microstructure of samples deposited at room temperature and those annealed at 250°C and 300°C were found to be amorphous except for the 200 nm YZn film. Annealing at higher temperatures leads to crystallization of the films. Moreover, the results demonstrate that YZn intermetallic thin films have high hardness, which varies with the film thickness and annealing treatment.
This work represents an initial effort to understand the microstructural evolution and mechanical properties of YZn thin films as a function of film thickness and annealing temperatures. The results of this study can be used to guide the design and development of YZn thin films with tailored microstructures and mechanical properties for various applications.
ContributorsAkkarakaduppil, Riju Philip James (Author) / Rajagopalan, Jagannathan (Thesis advisor, Committee member) / Peralta, Pedro (Committee member) / Solanki, Kiran (Committee member) / Arizona State University (Publisher)
Created2023
Description
The design of energy absorbing structures is driven by application specific requirements like the amount of energy to be absorbed, maximum transmitted stress that is permissible, stroke length, and available enclosing space. Cellular structures like foams are commonly leveraged in nature for energy absorption and have also found use in engineering applications. With the possibility of manufacturing complex cellular shapes using additive manufacturing technologies, there is an opportunity to explore new topologies that improve energy absorption performance. This thesis aims to systematically understand the relationships between four key elements: (i) unit cell topology, (ii) material composition, (iii) relative density, and (iv) fields; and energy absorption behavior, and then leverage this understanding to develop, implement and validate a methodology to design the ideal cellular structure energy absorber. After a review of the literature in the domain of additively manufactured cellular materials for energy absorption, results from quasi-static compression of six cellular structures (hexagonal honeycomb, auxetic and Voronoi lattice, and diamond, Gyroid, and Schwarz-P) manufactured out of AlSi10Mg and Nylon-12. These cellular structures were compared to each other in the context of four design-relevant metrics to understand the influence of cell design on the deformation and failure behavior. Three new and revised metrics for energy absorption were proposed to enable more meaningful comparisons and subsequent design selection. Triply Periodic Minimal Surface (TPMS) structures were found to have the most promising overall performance and formed the basis for the numerical investigation of the effect of fields on the energy absorption performance of TPMS structures. A continuum shell-based methodology was developed to analyze the large deformation behavior of field-driven variable thickness TPMS structures and validated against experimental data. A range of analytical and stochastic fields were then evaluated that modified the TPMS structure, some of which were found to be effective in enhancing energy absorption behavior in the structures while retaining the same relative density. Combining findings from studies on the role of cell geometry, composition, relative density, and fields, this thesis concludes with the development of a design framework that can enable the formulation of cellular material energy absorbers with idealized behavior.
ContributorsShinde, Mandar (Author) / Bhate, Dhruv (Thesis advisor) / Peralta, Pedro (Committee member) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Uncertainty quantification is critical for engineering design and analysis. Determining appropriate ways of dealing with uncertainties has been a constant challenge in engineering. Statistical methods provide a powerful aid to describe and understand uncertainties. This work focuses on applying Bayesian methods and machine learning in uncertainty quantification and prognostics among all the statistical methods. This study focuses on the mechanical properties of materials, both static and fatigue, the main engineering field on which this study focuses. This work can be summarized in the following items: First, maintaining the safety of vintage pipelines requires accurately estimating the strength. The objective is to predict the reliability-based strength using nondestructive multimodality surface information. Bayesian model averaging (BMA) is implemented for fusing multimodality non-destructive testing results for gas pipeline strength estimation. Several incremental improvements are proposed in the algorithm implementation. Second, the objective is to develop a statistical uncertainty quantification method for fatigue stress-life (S-N) curves with sparse data.Hierarchical Bayesian data augmentation (HBDA) is proposed to integrate hierarchical Bayesian modeling (HBM) and Bayesian data augmentation (BDA) to deal with sparse data problems for fatigue S-N curves. The third objective is to develop a physics-guided machine learning model to overcome limitations in parametric regression models and classical machine learning models for fatigue data analysis. A Probabilistic Physics-guided Neural Network (PPgNN) is proposed for probabilistic fatigue S-N curve estimation. This model is further developed for missing data and arbitrary output distribution problems. Fourth, multi-fidelity modeling combines the advantages of low- and high-fidelity models to achieve a required accuracy at a reasonable computation cost. The fourth objective is to develop a neural network approach for multi-fidelity modeling by learning the correlation between low- and high-fidelity models. Finally, conclusions are drawn, and future work is outlined based on the current study.
ContributorsChen, Jie (Author) / Liu, Yongming (Thesis advisor) / Chattopadhyay, Aditi (Committee member) / Mignolet, Marc (Committee member) / Ren, Yi (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2022
Description
A finite element model that replicates the experimental procedure to test and certify soft body armor has been developed. The model consists of four components: bullet, clay, straps, and shoot pack with different material models that closely capture the behavior of each component when subjected to ballistic impact loading. To test the fidelity of the model, three metrics are used - back face signature (BFS), the number of penetrated shoot pack layers, and the number of damaged shoot pack layers on the clay side of the shoot pack assembly. In addition, the shape and size of the bullet, and the shape and size of the hole in the shoot pack are also considered as qualitative measures to assess the developed model. The focus of this research work is to improve the shoot pack material model, while the constitutive model for the components is taken from earlier work done at ASU. Results show considerable improvement in the model in terms of capturing the number of penetrated layers, the size and shape of the holes in the shoot pack layer, and the predicted BFS. The developed finite element models can be used to predict the behavior of soft body armor for different initial conditions, shoot pack materials, and arrangement of the layers.
ContributorsPechetti, Sateesh (Author) / Rajan, Subramaniam (Thesis advisor) / Mignolet, Marc (Committee member) / Solanki, Kiran (Committee member) / Arizona State University (Publisher)
Created2024
Description
Aging-related damage and failure in structures, such as fatigue cracking, corrosion, and delamination, are critical for structural integrity. Most engineering structures have embedded defects such as voids, cracks, inclusions from manufacturing. The properties and locations of embedded defects are generally unknown and hard to detect in complex engineering structures. Therefore, early detection of damage is beneficial for prognosis and risk management of aging infrastructure system.
Non-destructive testing (NDT) and structural health monitoring (SHM) are widely used for this purpose. Different types of NDT techniques have been proposed for the damage detection, such as optical image, ultrasound wave, thermography, eddy current, and microwave. The focus in this study is on the wave-based detection method, which is grouped into two major categories: feature-based damage detection and model-assisted damage detection. Both damage detection approaches have their own pros and cons. Feature-based damage detection is usually very fast and doesn’t involve in the solution of the physical model. The key idea is the dimension reduction of signals to achieve efficient damage detection. The disadvantage is that the loss of information due to the feature extraction can induce significant uncertainties and reduces the resolution. The resolution of the feature-based approach highly depends on the sensing path density. Model-assisted damage detection is on the opposite side. Model-assisted damage detection has the ability for high resolution imaging with limited number of sensing paths since the entire signal histories are used for damage identification. Model-based methods are time-consuming due to the requirement for the inverse wave propagation solution, which is especially true for the large 3D structures.
The motivation of the proposed method is to develop efficient and accurate model-based damage imaging technique with limited data. The special focus is on the efficiency of the damage imaging algorithm as it is the major bottleneck of the model-assisted approach. The computational efficiency is achieved by two complimentary components. First, a fast forward wave propagation solver is developed, which is verified with the classical Finite Element(FEM) solution and the speed is 10-20 times faster. Next, efficient inverse wave propagation algorithms is proposed. Classical gradient-based optimization algorithms usually require finite difference method for gradient calculation, which is prohibitively expensive for large degree of freedoms. An adjoint method-based optimization algorithms is proposed, which avoids the repetitive finite difference calculations for every imaging variables. Thus, superior computational efficiency can be achieved by combining these two methods together for the damage imaging. A coupled Piezoelectric (PZT) damage imaging model is proposed to include the interaction between PZT and host structure. Following the formulation of the framework, experimental validation is performed on isotropic and anisotropic material with defects such as cracks, delamination, and voids. The results show that the proposed method can detect and reconstruct multiple damage simultaneously and efficiently, which is promising to be applied to complex large-scale engineering structures.
Non-destructive testing (NDT) and structural health monitoring (SHM) are widely used for this purpose. Different types of NDT techniques have been proposed for the damage detection, such as optical image, ultrasound wave, thermography, eddy current, and microwave. The focus in this study is on the wave-based detection method, which is grouped into two major categories: feature-based damage detection and model-assisted damage detection. Both damage detection approaches have their own pros and cons. Feature-based damage detection is usually very fast and doesn’t involve in the solution of the physical model. The key idea is the dimension reduction of signals to achieve efficient damage detection. The disadvantage is that the loss of information due to the feature extraction can induce significant uncertainties and reduces the resolution. The resolution of the feature-based approach highly depends on the sensing path density. Model-assisted damage detection is on the opposite side. Model-assisted damage detection has the ability for high resolution imaging with limited number of sensing paths since the entire signal histories are used for damage identification. Model-based methods are time-consuming due to the requirement for the inverse wave propagation solution, which is especially true for the large 3D structures.
The motivation of the proposed method is to develop efficient and accurate model-based damage imaging technique with limited data. The special focus is on the efficiency of the damage imaging algorithm as it is the major bottleneck of the model-assisted approach. The computational efficiency is achieved by two complimentary components. First, a fast forward wave propagation solver is developed, which is verified with the classical Finite Element(FEM) solution and the speed is 10-20 times faster. Next, efficient inverse wave propagation algorithms is proposed. Classical gradient-based optimization algorithms usually require finite difference method for gradient calculation, which is prohibitively expensive for large degree of freedoms. An adjoint method-based optimization algorithms is proposed, which avoids the repetitive finite difference calculations for every imaging variables. Thus, superior computational efficiency can be achieved by combining these two methods together for the damage imaging. A coupled Piezoelectric (PZT) damage imaging model is proposed to include the interaction between PZT and host structure. Following the formulation of the framework, experimental validation is performed on isotropic and anisotropic material with defects such as cracks, delamination, and voids. The results show that the proposed method can detect and reconstruct multiple damage simultaneously and efficiently, which is promising to be applied to complex large-scale engineering structures.
ContributorsChang, Qinan (Author) / Liu, Yongming (Thesis advisor) / Mignolet, Marc (Committee member) / Chattopadhyay, Aditi (Committee member) / Yan, Hao (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2019