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ContributorsZhang, Yifan (Performer) / Shen, Yian (Performer) / Chen, Wei-Jhen (Performer) / ASU Library. Music Library (Publisher)
Created2023-04-14
ContributorsCastillo, Alicia (Performer) / ASU Library. Music Library (Publisher)
Created2023-04-15
ContributorsRamirez, Jessica (Performer) / Kuehn, Jonathan (Performer) / Ardelt, Lauren (Performer) / ASU Library. Music Library (Publisher)
Created2023-04-17
ContributorsPark, Chulyoung (Performer) / Lim, Soyoung (Performer) / An, Zhihuan (Performer) / ASU Library. Music Library (Publisher)
Created2023-04-16
Description
Physical human-robot interaction (pHRI) has immense potential in fields like industry, military, rehabilitation, and robotic-surgery. However, as the field continues to grow in prominence, there are technical challenges that must be addressed, including safety/stability, adaptability, efficiency, user experience, and versatility. Enhancing pHRI is paramount to overcome these challenges and benefit numerous areas. This dissertation consists of different studies that focus on improving physical human-robot interaction through the development and implementation of various control methods. The first study investigates the lower bounds of robotic damping that humans can stably interact with in different arm postures. The results indicate that the human arm is less capable of adjusting to the unstable environments when it is close to the body and laterally displaced for the anterior-posterior (AP) and the medial-lateral (ML) directions, respectively. The second study proposes a multi-degree-of-freedom variable damping controller that balances stability and agility and reduces user effort in pHRI. The controller effectively reduces user effort while increasing agility without compromising stability. The third study presents a variable stiffness control method to provide intuitive and smooth force guidance during pHRI. This controller significantly reduces robotic force guidance and user effort while maintaining speed and accuracy of movement. Based on the findings from these studies, a biomechanics-based user-adaptive variable impedance control is proposed, which can be applied in a diverse set of applications to enhance the overall performance of coupled human-robot systems. This controller accounts for impedance properties of the human limbs and adaptively changes robotic damping, stiffness, and equilibrium trajectory based on online estimation of user's intent of motion and intent of movement direction while minimizing energy of the coupled human-robot system. Bayesian optimization was used to evaluate an unknown objective function and optimize noisy performance. The presented adaptive control strategy could reduce energy expenditure and achieve performance improvement in several metrics of stability, agility, user effort, smoothness, and user preference. All studies were validated and tested through several human experiments. Overall, the dissertation contributes to the field of pHRI by providing insights into the dynamics of human-robot interactions and proposing novel control strategies to enhance their performance.
ContributorsZahedi, Fatemeh (Author) / Lee, Hyunglae Prof. (Thesis advisor) / Berman, Spring Prof. (Committee member) / Marvi, Hamid Prof. (Committee member) / Yong, Sze Zheng Prof. (Committee member) / Zhang, Yu Prof. (Committee member) / Arizona State University (Publisher)
Created2023
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
The study aims to develop and evaluate failure prediction models that accurately predict crack initiation sites, fatigue life in additively manufactured Ti-6Al-4V, and burst pressure in relevant applications.The first part proposes a classification model to identify crack initiation sites in AM-built Ti-6Al-4V alloy. The model utilizes surface and pore-related parameters and achieves high accuracy (0.97) and robustness (F1 score of 0.98). Leveraging CT images for characterization and data extraction from the CT-images built STL files, the model effectively detects crack initiation sites while minimizing false positives and negatives. Data augmentation techniques, including SMOTE+Tomek Links, are employed to address imbalanced data distributions and improve model performance.
This study proposes the Probabilistic Physics-guided Neural Network 2.0 (PPgNN) for probabilistic fatigue life estimation. The presented approach overcomes the limitations of classical regression machine models commonly used to analyze fatigue data. One key advantage of the proposed method is incorporating known physics constraints, resulting in accurate and physically consistent predictions.
The efficacy of the model is demonstrated by training the model with multiple fatigue S-N curve data sets from open literature with relevant morphological data and tested using the data extracted from CT-built STL files. The results illustrate that PPgNN 2.0 is a flexible and robust model for predicting fatigue life and quantifying uncertainties by estimating the mean and standard deviation of the fatigue life. The loss function that trains the proposed model can capture the underlying distribution and reduce the prediction error.
A comparison study between the performance of neural network models highlights the benefits of physics-guided learning for fatigue data analysis. The proposed model demonstrates satisfactory learning capacity and generalization, providing accurate fatigue life predictions to unseen examples.
An elastic-plastic Finite Element Model (FEM) is developed in the second part to assess pipeline integrity, focusing on burst pressure estimation in high-pressure gas pipelines with interactive corrosion defects. The FEM accurately predicts burst pressure and evaluates the remaining useful life by considering the interaction between corrosion defects and neighboring pits. The FEM outperforms the well-known ASME-B31G method in handling interactive corrosion threats.
ContributorsBalamurugan, Rakesh (Author) / Liu, Yongming (Thesis advisor) / Zhuang, Houlong (Committee member) / Bhate, Dhruv (Committee member) / Arizona State University (Publisher)
Created2023
ContributorsSouza, Gibran (Performer) / Crissman, Jonathan (Performer) / Parabola Guitar Duo (Performer) / ASU Library. Music Library (Publisher)
Created2017-04-15
Description
Computing the fluid phase interfaces in multiphase flow is a challenging area of research in fluids. The Volume of Fluid andLevel Set methods are a few algorithms that have been developed for reconstructing the multiphase fluid flow interfaces.
The thesis work focuses on exploring the ability of neural networks to reconstruct the multiphase fluid flow interfaces using
a data-driven approach.
The neural network model has liquid volume fraction stencils as an input, and it predicts the radius of the circle as an
output of the network which represents a phase interface separating two immiscible fluids inside a fluid domain. The liquid
volume fraction stencils are generated for randomly varying circle radii within a 1x1 domain using an open-source VOFI
library. These datasets are used to train the neural network. Once the model is trained, the predicted circular phase
interface from the neural network output is used to generate back the predicted liquid volume fraction stencils.
Error norms values are calculated to assess the error in the neural network model’s predicted liquid volume fraction
stencils with the actual liquid volume fraction stencils from the VOFI library. The neural network parameters are optimized
by testing them for different hyper-parameters to reduce the error norms. So as to minimize the difference between the
predicted and the actual liquid volume fraction stencils and errors in reconstructing the fluid phase interface geometry.
ContributorsPawar, Pranav Rajesh (Author) / Herrmann, Marcus (Thesis advisor) / Zhuang, Houlong (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2023