IU Indianapolis ScholarWorks :: Browsing by Author "Jones, Alan"

Browsing by Author "Jones, Alan"

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Effectiveness of Current-generation Virtual Reality-based Laboratories
(ASEE, 2018-06) Jones, Alan; Golub, Michael; Mechanical and Energy Engineering, School of Engineering and Technology
Typical curricula in engineering and science disciplines in both secondary and post-secondary education include extensive laboratory experiences. Current generation virtual reality (VR) hardware such as the HTC Vive and the Oculus Rift allow for unprecedented immersive capability such that virtual reality based laboratories may be able to overcome the deficiencies of current virtual labs and tele-operated equipment and provide students with a functional equivalent to the situated learning that occurs in traditional hands-on laboratories. Financial considerations and the interest to deliver more distance-education based courses may encourage more programs to utilize VR laboratory experiences versus traditional laboratories. This paper reports on the perceived effectiveness of using a VR laboratory developed with current generation (HTC Vive) VR equipment in place of a traditional laboratory.
Enhancing Mechanical Engineering Education Through a Virtual Instructor in an Ai-Driven Virtual Reality Fatigue Test Lab
(2023-08) Yahyaeian, Amir Abbas; Jones, Alan; Zhang, Jing; Du, Xiaoping
This thesis demonstrates the combination of virtual reality (VR) and artificial intelligence (AI) specifically exploring the practical application of Natural Language Processing (NLP) and GPT-based models in educational VR laboratories. The objective is to design a comprehensive learning environment where users can independently engage in laboratory experiments, deriving similar educational outcomes as they would from a traditional, physical laboratory setup, particularly within the realms of Science, Technology, Engineering, and Mathematics (STEM) disciplines. Using machine learning techniques and authentic virtual reality simulating educational experiments, we propose an advanced learning platform—Virtual Reality Instructional Laboratory Environment (VRILE). A key feature of the VRILE is an AI-powered instructor capable of not only guiding the learners through the tasks but also responding intelligently to their actions in real-time. The AI constituent of the VRILE uses the GPT-2 model for text generation in the field of Natural Language Processing (NLP). To ensure the generated instructions were contextually relevant and meaningful to lab participants, the model was trained on a dataset derived from an augmentation over user interactions within the VR environment. By pushing the boundaries of how AI can be utilized in educational VR environments, this research paves the way for broader adoption across other domains of engineering education. Furthermore, it provides a solid foundation for future research in this interdisciplinary field. It marks a significant stride in the integration of technology and education, encouraging more ventures into this promising frontier.
Evaluation of Canvas-Based Online Homework for Engineering: American Society for Engineering Education
(2017) Jones, Alan; Mechanical Engineering, School of Engineering and Technology
Factors influencing cartilage wear in an accelerated in vitro test: collagen fiber orientation, anatomic location, cartilage composition, and photo-chemical crosslinking
(2018) Hossain, M. Jayed; Wagner, Diane; Jones, Alan; Holguin, Nilsson
Articular cartilage (AC) is a strong but flexible connective tissue that covers and protects the end of the long bones. Although cartilage has excellent friction and wear properties that allow smooth joint function during daily activities, these properties are not fully understood. Many material properties of cartilage are anisotropic and vary with anatomic location and the composition of the tissue, but whether this is also true for cartilage friction and wear has not been previously determined. Furthermore, cartilage disease and injury are major health concerns that affect millions of people, but there are few available treatments to prevent the progression of cartilage degeneration. Collagen crosslinking may be a potential treatment to reduce cartilage wear and slow or prevent the progression of cartilage disease. The objectives of this thesis were to investigate the relationships between the friction/wear characteristics of cartilage and the orientation of the preferred fiber direction, the anatomic location of the tissue, the composition of the tissue, and exogenous photochemical crosslinking. In the superficial zone, AC has preferential fiber direction which leads to anisotropic material behavior. Therefore, we hypothesized that AC will show anisotropic behavior between longitudinal and transverse direction in an accelerated, in vitro wear test on bovine cartilage in terms of friction and wear. This hypothesis was proven by the quantification of glycosaminoglycans released from the tissue during the wear test, which showed that more glycosaminoglycans were released when the wear direction was transverse to the direction of the fibers. However, the hydroxyproline released from the tissue during the wear test was not significantly different between the two directions, nor was the coefficient of friction. The material properties of AC can also vary with anatomic location, perhaps due to differences in how the tissue is loaded in vivo. We hypothesized that cartilage from a higher load bearing site will give better wear resistance than cartilage from lower load bearing regions. However, no differences in friction or wear were observed between the different anatomic locations on the bovine femoral condyles. The concentration of collagen, glycosaminoglycans, cells and water in the tissue was also quantified, but no significant differences in tissue composition were found among the locations that were tested. Although wear did not vary with anatomic location, variation in the wear measurements were relatively high. One potential source of variation is the composition of the cartilage. To determine whether cartilage composition influences friction and wear, a correlation analysis was conducted. An accelerated, in vitro wear test was conducted on cartilage from bovine femoral condyles, and the tissue adjacent to the wear test specimens was analyzed for collagen, glycosaminoglycan, cell, and water content. Because wear occurs on the cartilage surface, the superficial zone of the cartilage might play an important role in wear test. Therefore, composition of the adjacent cartilage was determined in both the superficial zone and the full thickness of the tissue. A significant negative correlation was found between wear and collagen content in the full thickness of the tissue, and between the initial coefficient of friction and the collagen content in the superficial zone. This correlation suggests that variation in the collagen content in the full thickness of the cartilage partially explains differences in amount of wear between specimens. The wear resistance of cartilage can be improved with exogenous crosslinking agents, but the use of photochemical crosslinking to improve wear resistance is not well understood. Two photochemical crosslinking protocols were analyzed to improve the wear resistance of the cartilage by using chloro-aluminum phthalocyanine tetrasulfonic acid (CASPc) and 670nm laser light. The cartilage treated with the two crosslinking protocols had lower wear than the non-treated group without changing the friction properties of the cartilage.
Modeling of Steel Laser Cutting Process Using Finite Element, Machine Learning, and Kinetic Monte Carlo Methods
(2022-05) Stangeland, Dillon; Zhang, Jing; Jones, Alan; Daehyun Koo, Dan
Laser cutting is a manufacturing technology that uses a focused laser beam to melt, burn and vaporize materials, resulting in a high-quality cut edge. Although previous efforts are primarily based on a trial-and-error approach, there is insufficient understanding of the laser cutting process, thus hindering further development of the technology. Therefore, the motivation of this thesis is to address this research need by developing a series of models to understand the thermal and microstructure evolution in the process. The goal of the thesis is to design a tool for optimizing the steel laser cutting process through a modeling approach. The goal will be achieved through three interrelated objectives: (1) understand the thermal field in the laser cutting process of ASTM A36 steel using the finite element (FE) method coupled with the user-defined Moving Heat Source package; (2) apply machine learning method to predict heat-affected zone (HAZ) and kerf, the key features in the laser cutting process; and (3) employ kinetic Monte Carlo (kMC) simulation to simulate the resultant microstructures in the laser cutting process. Specifically, in the finite element model, a laser beam was applied to the model with the parameters of the laser’s power, cut speed, and focal diameter being tested. After receiving results generated by the finite element model, they were then used by two machine learning algorithms to predict the HAZ distance and kerf width that is produced due to the laser cutting process. The two machine learning algorithms tested were a neural network and a support vector machine. Finally, the thermal field was imported into the kMC model as the boundary conditions to predict grain evolution’s in the metals. The results of the research showed that by increasing the focal diameter of a laser, the kerf width can be decreased and the HAZ distance experienced a large decrease. Additionally, a pulse-like pattern was observed in the kerf width through modeling and can be minimized into more of a uniform cut through the increase of the focal diameter. By increasing the power of a laser, the HAZ distance, kerf width, and region of the material above its original temperature increase. Additionally, through the increase of the cut speed, the HAZ distance, kerf width, kerf pulse-like pattern, and region of the material above its original temperature decrease. Through the incorporation of machine learning algorithms, it was found that they can be used to effectively predict the HAZ distance to a certain degree. The Neural Network and Support Vector Machine models both show that the experimental HAZ distance data lines up with the results derived from ANSYS. The Gaussian Process Regression HAZ model shows that the algorithm is not powerful enough to create an accurate prediction. Additionally, all of the kerf width models show that the experimental data is being overfit by the ANSYS results. As such, the kerf width results from ANSYS need additional validation to prove their accuracy. Using the kMC model to examine the microstructure change due to the laser cutting process, three observations were made. First, the largest grain growth occurs at the edge of the laser where the material was not hot enough to be cut. Then, grain growth decays as the distance from the edge increases. Finally, at the edge of the HAZ boundary, grain growth does not occur.
Molecular dynamics modeling of mechanical and tribological properties of additively manufactured AlCoCrFe high entropy alloy coating on aluminum substrate
(Elsevier, 2021-04-15) Yang, Xuehui; Zhang, Jian; Sagar, Sugrim; Dube, Tejesh; Kim, Bong-Gu; Jung, Yeon-Gil; Koo, Dan Daehyun; Jones, Alan; Zhang, Jing; Mechanical and Energy Engineering, School of Engineering and Technology
In this work, an improved molecular dynamics (MD) model is developed to simulate the nanoindentation and tribological tests of additively manufactured high entropy alloys (HEA) AlCoCrFe coated on an aluminum substrate. The model shows that in the interface region between the HEA coating and Al substrate, as the laser heating temperature increases during the HEA coating additive manufacturing process, more Al in the substrate is melted to react with other elements in the coating layer, which is qualitatively in agreement with experiment in literature. Using the simulated nanoindentation tests, the calculated Young's modulus of pure Al and Al with HEA coating is 79.93 GPa and 119.30 GPa, respectively. In both our simulations and the experimental results in the literature, the hardness of Al with the HEA coating layer is about 10 times higher than the Al hardness, indicating that HEA can significantly improve the hardness of the metallic substrate. Using the simulated tribological scratch tests, the computed wear tracks are qualitatively in agreement with experimental images in literature. Both our model and experiment show that the Al with HEA coating has a much smaller wear track than that of Al, due to less plastic deformation, confirmed by a dislocation analysis. The computed average coefficient of friction of Al is 0.62 and Al with HEA coating is 0.14. This work demonstrates that the HEA coating significantly improves the mechanical and tribology properties, which are in excellent agreement with the experiments reported in the literature.
Molecular Dynamics Simulations of the Mechanical Deformation Behavior of Face-Centered Cubic Metallic Nanowires
(2010-05-05T14:41:16Z) Heidenreich, Joseph David; Wang, Guofeng; Chen, Jie; Jones, Alan
Nanoscale materials have become an active area of research due to the enhanced mechanical properties of the nanomaterials in comparison to their respective bulk materials. The effect that the size and shape of a nanomaterial has on its mechanical properties is important to understand if these materials are to be used in engineering applications. This thesis presents the results of molecular dynamics (MD) simulations on copper, gold, nickel, palladium, platinum, and silver nanowires of three cross-sectional shapes and four diameters. The cross-sectional shapes investigated were square, circular, and octagonal while the diameters varied from one to eight nanometers. Due to a high surface area to volume ratio, nanowires do not have the same atomic spacing as bulk materials. To account for this difference, prior to tensile loading, a minimization procedure was applied to find the equilibrium strain for each structure size and shape. Through visualization of the atomic energy before and after minimization, it was found that there are more than two energetically distinct areas within the nanowires. In addition, a correlation between the anisotropy of a material and its equilibrium strain was found. The wires were then subjected to a uniaxial tensile load in the [100] direction at a strain rate of 108 s-1 with a simulation temperature of 300 K. The embedded-atom method (EAM) was employed using the Foiles potential to simulate the stretching of the wires. The wires were stretched to failure, and the corresponding stress-strain curves were produced. From these curves, mechanical properties including the elastic modulus, yield stress and strain, and ultimate strain were calculated. In addition to the MD approach, an energy method was applied to calculate the elastic modulus of each nanowire through exponential fitting of an energy function. Both methods used to calculate Young’s modulus qualitatively gave similar results indicating that as diameter decreases, Young’s modulus decreases. The MD simulations were also visualized to investigate the deformation and yield behavior of each nanowire. Through the visualization, most nanowires were found to yield and fail through partial dislocation nucleation and propagation leading to {111} slip. However, the 5 nm diameter octagonal platinum nanowire was found to yield through reconstruction of the {011} surfaces into the more energetically favorable {021} surfaces.
Self-Healing Corrosion Resistant Coatings
(Office of the Vice Chancellor for Research, 2014-04-11) Jones, Alan; Ye, Lujie
According to the U.S. Federal Highway Administration, the annual cost of corrosion in the United States is estimated at $276 billion. The most common way to protect materials from corrosion is with coatings, including organic (paint), ceramic and metallic coatings. During use, micro-cracks form in coatings resulting in exposure to the environment, which can lead to catastrophic failure of critical components. Our group is developing low-cost self-healing technology to significantly extend the service life of coatings and the components they protect. Potential healing agents were evaluated and an air-drying triglyceride (linseed oil) was identified as the candidate healing agent. Self-healing coatings are fabricated using urea-formaldehyde encapsulated linseed oil and are evaluated for mechanical performance, corrosion resistance, and self-healing performance. Research into optimization and long term durability and performance of low-cost self-healing coating materials is ongoing.
Surrogate-based global optimization of composite material parts under dynamic loading
(2017-08) Valladares Guerra, Homero Santiago; Tovar, Andres; Jones, Alan; Anwar, Sohel
The design optimization of laminated composite structures is of relevance in automobile, naval, aerospace, construction and energy industry. While several optimization methods have been applied in the design of laminated composites, the majority of those methods are only applicable to linear or simplified nonlinear models that are unable to capture multi-body contact. Furthermore, approaches that consider composite failure still remain scarce. This work presents an optimization approach based on design and analysis of computer experiments (DACE) in which smart sampling and continuous metamodel enhancement drive the design process towards a global optimum. Kriging metamodel is used in the optimization algorithm. This metamodel enables the definition of an expected improvement function that is maximized at each iteration in order to locate new designs to update the metamodel and find optimal designs. This work uses explicit finite element analysis to study the crash behavior of composite parts that is available in the commercial code LS-DYNA. The optimization algorithm is implemented in MATLAB. Single and multi-objective optimization problems are solved in this work. The design variables considered in the optimization include the orientation of the plies as well as the size of zones that control the collapse of the composite parts. For the ease of manufacturing, the fiber orientation is defined as a discrete variable. Objective functions such as penetration, maximum displacement and maximum acceleration are defined in the optimization problems. Constraints are included in the optimization problem to guarantee the feasibility of the solutions provided by the optimization algorithm. The results of this study show that despite the brittle behavior of composite parts, they can be optimized to resist and absorb impact. In the case of single objective problems, the algorithm is able to find the global solution. When working with multi-objective problems, an enhanced Pareto is provided by the algorithm.
Water surface reinforcement effect in 3D printed polymer derived SiOC ceramics
(Springer, 2021-01) Yang, Xuehui; Zhang, Jian; Park, Hye-Yeong; Jung, Yeon-Gil; Jones, Alan; Zhang, Jing; Mechanical and Energy Engineering, School of Engineering and Technology
In this work, PDC SiOC porous structures were fabricated using the stereolithography (SLA) 3D printing method with preceramic resin methylsiloxane. The printed structures were then sintered at 1000 °C for 1 h. Water treatment can help prevent the shrinkage and remove the unreacted resin. From the XRD and FT-IR results, the sintered ceramic is amorphous SiOC. The microstructures show that the printed structures have high geometric accuracy. The water reinforcement process produces smoother surfaces on the printed structures.

Browsing by Author "Jones, Alan"

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