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ItemAccurate location of tumor in head and neck cancer radiotherapy treatment with respect to machine isocentre(2017-05) Tangirala, Deepak Kumar; Razban, Ali; Chen, Jie; Tovar, AndresRadiation Therapy has been one of the most common techniques to treat various types of cancers, in particular is Head and Neck Cancer (HNC) which accounts for three percent of all cancers in the United States. During the treatment procedure, the patient is immobilized using immobilization devices such as the full head face mask, bite blocks, stereotactic frame, etc. to get accurate location of tumor. The disadvantage of these devices is that they are very uncomfortable to the patient especially people suffering from Post-Traumatic Stress Disorder (PTSD) and claustrophobia who cannot wear any confined masked system such as the full head mask or bite block during the treatment procedure. To mitigate this problem, there has been a lot of research in modifying such immobilizing devices without neglecting the accurate location of tumor. To this end, the research presented in this thesis focuses on developing a mask less system with accurately locating the position of tumor using the technique of coordinate transformation at the same time fulfilling the three important characteristics: • Comfort • Accuracy • Low price Such a system is comfortable to the patient because no confining mask system is used and we choose minimal contact points on the patient for fixing the patient. Traditionally, such type of cancer treatment is carried out in two stages: Diagnosis stage, which identifies the location of the tumor and the external markers and the Treatment stage where the tumor is treated with immobilization device being common in both the stages. In the new system, the immobilization devices vary at the two stages. The head position is monitored by using pressure sensor assembly where spring and pressure sensor setup detects the amount and direction of head deviation. We also prepare a customized 3D printed nose bridge part for extra referencing in the treatment room. Also, it is important that we use material for our immobilization devices which does not contain any metal and MRI compatible. Once the patient lies down on the treatment couch and is immobilized using the immobilization devices, then tumor location is calculated using the theory of coordinate transformation and transformation matrix in the Diagnosis and Treatment Stage. To validate the system, simulation of immobilization devices used in the new design was carried out using ANSYS Workbench 15.0 and LS-Dyna software’s Explicit Dynamics method. The simulation for the head-fixing device showed a deflection of ±0.1974 mm with respect to machine isocenter with a load of 60 N, which is lower than the customer requirement of ±3 mm with respect to machine isocenter of head deviation. The material used for the external markers for patient positioning was selected to be polyetheretherketone (PEEK) which is a radiolucent and widely used MRI compatible material. The system also takes into consideration the effect of weight loss, which is one of the drawbacks of the current systems. Although still in the development stage, this mask less system holds to be the next new variety of immobilization devices that are comfortable to the patient and less expensive to be implemented in future cancer treatment practices. ItemAI Based Modelling and Optimization of Turning Process(2012-08) Kulkarni, Ruturaj Jayant; El-Mounayri, Hazim; Anwar, Sohel; Wasfy, TamerIn this thesis, Artificial Neural Network (ANN) technique is used to model and simulate the Turning Process. Significant machining parameters (i.e. spindle speed, feed rate, and, depths of cut) and process parameters (surface roughness and cutting forces) are considered. It is shown that Multi-Layer Back Propagation Neural Network is capable to perform this particular task. Design of Experiments approach is used for efficient selection of values of parameters used during experiments to reduce cost and time for experiments. The Particle Swarm Optimization methodology is used for constrained optimization of machining parameters to minimize surface roughness as well as cutting forces. ANN and Particle Swarm Optimization, two computational intelligence techniques when combined together, provide efficient computational strategy for finding optimum solutions. The proposed method is capable of handling multiple parameter optimization problems for processes that have non-linear relationship between input and output parameters e.g. milling, drilling etc. In addition, this methodology provides reliable, fast and efficient tool that can provide suitable solution to many problems faced by manufacturing industry today. ItemAnalysis of Process Induced Shape Deformations and Residual Stresses in Composite Parts during Cure(2019-05) Patil, Ameya S.; Dalir, Hamid; El-Mounayri, Hazim; Zhang, JingProcess induced dimensional changes in composite parts has been the topic of interest for many researchers. The residual stresses that are induced in composite laminates during curing process while the laminate is in contact with the process tool often lead to dimensional variations such as spring-in of angles and warpage of flat panels. The traditional trial-and-error approach can work for simple geometries, but composite parts with complex shapes require more sophisticated models. When composite laminates are subjected to thermal stresses, such as the heating and cooling processes during curing, they can become distorted as the in-plane and the throughthickness coeffcients of thermal expansion are di erent, as well as chemical shrinkage of the resin, usually cause spring-in. Deformed components can cause problems during assembly, which significantly increases production costs and affects performance. This thesis focuses on predicting these shape deformations using software simulation of composite manufacturing and curing. Various factors such as resin shrinkage, degrees of cure, difference between through thickness coeffcient of thermal expansion of the composite laminate are taken into the consideration. A cure kinetic model is presented which illustrates the matrix behavior during cure. The results obtained using the software then were compared with the experimental values of spring-in from the available literature. The accuracy of ACCS package was validated in this study. Analyzing the effects of various parameters of it was estimated that 3D part simulation is an effective and cost and time saving method to predict nal shape of the composite part. ItemAnalyzing Compressed Air Demand Trends to Develop a Method to Calculate Leaks in a Compressed Air Line Using Time Series Pressure Measurements(2022-05) Daniel, Ebin John; Razban, Ali; Goodman, David; Chen, JieCompressed air is a powerful source of stored energy and is used in a variety of applications varying from painting to pressing, making it a versatile tool for manufacturers. Due to the high cost and energy consumption associated with producing compressed air and it’s use within industrial manufacturing, it is often referred to as a fourth utility behind electricity, natural gas, and water. This is the reason why air compressors and associated equipment are often the focus for improvements in the eyes of manufacturing plant managers. As compressed air can be used in multiple ways, the methods used to extract and transfer the energy from this source vary as well. Compressed air can flow through different types of piping, such as aluminum, Polyvinyl Chloride (PVC), rubber, etc. with varying hydraulic diameters, and through different fittings such as 90-degree elbows, T-junctions, valves, etc. which can cause one of the major concerns related to managing the energy consumption of an air compressor, and that is the waste of air through leaks. Air leaks make up a considerable portion of the energy that is wasted in a compressed air system, as they cause a multitude of problems that the compressor will have to make up for to maintain the steady operation of the pneumatic devices on the manufacturing floor that rely on compressed air for their application. When air leaks are formed within the compressed air piping network, they act as continuous consumers and cause not only the siphoning off of said compressed air, put also reduce the pressure that is needed within the pipes. The air compressors will have to work harder to compensate for the losses in the pressure and the amount of air itself, causing an overconsumption of energy and power. Overworking the air compressor also causes the internal equipment to be stretched beyond its capabilities, especially if they are already running at full loads, reducing their total lifespans considerably. In addition, if there are multiple leaks close to the pneumatic devices on the manufacturing floor, the immediate loss in pressure and air can cause the devices to operate inefficiently and thus cause a reduction in production. This will all cumulatively impact the manufacturer considerably when it comes to energy consumption and profits. There are multiple methods of air leak detection and accounting that currently exist so as to understand their impact on the compressed air systems. The methods are usually conducted when the air compressors are running but during the time when there is no, or minimal, active consumption of the air by the pneumatic devices on the manufacturing floor. This time period is usually called non-production hours and generally occur during breaks or between employee shift changes. This time is specifically chosen so that the only air consumption within the piping is that of the leaks and thus, the majority of the energy and power consumed during this time is noted to be used to feed the air leaks. The collected data is then used to extrapolate and calculate the energy and power consumed by these leaks for the rest of the year. There are, however, a few problems that arise when using such a method to understand the effects of the leaks in the system throughout the year. One of the issues is that it is assumed that the air and pressure lost through the found leaks are constant even during the production hours i.e. the hours that there is active air consumption by the pneumatic devices on the floor, which may not be the case due to the increased air flow rates and varying pressure within the line which can cause an increase in the amount of air lost through the same orifices that was initially detected. Another challenge that arises with using only the data collected during a single non-production time period is that there may be additional air leaks that may be created later on, and the energy and power lost due to the newer air leaks would remain unaccounted for. As the initial estimates will not include the additional losses, the effects of the air leaks may be underestimated by the plant managers. To combat said issues, a continuous method of air leak analyses will be required so as to monitor the air compressors’ efficiency in relation to the air leaks in real time. By studying a model that includes both the production, and non-production hours when accounting for the leaks, it was observed that there was a 50.33% increase in the energy losses, and a 82.90% increase in the demand losses that were estimated when the effects of the air leaks were observed continuously and in real time. A real time monitoring system can provide an in-depth understanding of the compressed air system and its efficiency. Managing leaks within a compressed air system can be challenging especially when the amount of energy wasted through these leaks are unaccounted for. The main goal of this research was to find a nonintrusive way to calculate the amount of air as well as energy lost due to these leaks using time series pressure measurements. Previous studies have shown a strong relationship between the pressure difference, and the use of air within pneumatic lines, this correlation along with other factors has been exploited in this research to find a novel and viable method of leak accounting to develop a Continuous Air Leak Monitoring (CALM) system. ItemApplication of an innovative MBSE (SysML-1D) co-simulation in healthcare(2018-05) Kalvit, Kalpak; El-Mounayri, Hazim ItemApplying Machine Learning to Optimize Sintered Powder Microstructures from Phase Field Modeling(2020-12) Batabyal, Arunabha; Zhang, Jing; Yang, Shengfeng; Du, XiaopingSintering is a primary particulate manufacturing technology to provide densification and strength for ceramics and many metals. A persistent problem in this manufacturing technology has been to maintain the quality of the manufactured parts. This can be attributed to the various sources of uncertainty present during the manufacturing process. In this work, a two-particle phase-field model has been analyzed which simulates microstructure evolution during the solid-state sintering process. The sources of uncertainty have been considered as the two input parameters surface diffusivity and inter-particle distance. The response quantity of interest (QOI) has been selected as the size of the neck region that develops between the two particles. Two different cases with equal and unequal sized particles were studied. It was observed that the neck size increased with increasing surface diffusivity and decreased with increasing inter-particle distance irrespective of particle size. Sensitivity analysis found that the inter-particle distance has more influence on variation in neck size than that of surface diffusivity. The machine-learning algorithm Gaussian Process Regression was used to create the surrogate model of the QOI. Bayesian Optimization method was used to find optimal values of the input parameters. For equal-sized particles, optimization using Probability of Improvement provided optimal values of surface diffusivity and inter-particle distance as 23.8268 and 40.0001, respectively. The Expected Improvement as an acquisition function gave optimal values 23.9874 and 40.7428, respectively. For unequal sized particles, optimal design values from Probability of Improvement were 23.9700 and 33.3005 for surface diffusivity and inter-particle distance, respectively, while those from Expected Improvement were 23.9893 and 33.9627. The optimization results from the two different acquisition functions seemed to be in good agreement with each other. The results also validated the fact that surface diffusivity should be higher and inter-particle distance should be lower for achieving larger neck size and better mechanical properties of the material. ItemAtomistic and finite element modeling of zirconia for thermal barrier coating applications(2014) Zhang, Yi; Zhang, Jing; El-Mounayri, Hazim; Tovar, Andrés; Anwar, SohelZirconia (ZrO2) is an important ceramic material with a broad range of applications. Due to its high melting temperature, low thermal conductivity, and high-temperature stability, zirconia based ceramics have been widely used for thermal barrier coatings (TBCs). When TBC is exposed to thermal cycling during real applications, the TBC may fail due to several mechanisms: (1) phase transformation into yttrium-rich and yttrium-depleted regions, When the yttrium-rich region produces pure zirconia domains that transform between monoclinic and tetragonal phases upon thermal cycling; and (2) cracking of the coating due to stress induced by erosion. The mechanism of erosion involves gross plastic damage within the TBC, often leading to ceramic loss and/or cracks down to the bond coat. The damage mechanisms are related to service parameters, including TBC material properties, temperature, velocity, particle size, and impact angle. The goal of this thesis is to understand the structural and mechanical properties of the thermal barrier coating material, thus increasing the service lifetime of gas turbine engines. To this end, it is critical to study the fundamental properties and potential failure mechanisms of zirconia. This thesis is focused on investigating the structural and mechanical properties of zirconia. There are mainly two parts studied in this paper, (1) ab initio calculations of thermodynamic properties of both monoclinic and tetragonal phase zirconia, and monoclinic-to-tetragonal phase transformation, and (2) image-based finite element simulation of the indentation process of yttria-stabilized zirconia. In the first part of this study, the structural properties, including lattice parameter, band structure, density of state, as well as elastic constants for both monoclinic and tetragonal zirconia have been computed. The pressure-dependent phase transition between tetragonal (t-ZrO2) and cubic zirconia (c-ZrO2) has been calculated using the density function theory (DFT) method. Phase transformation is defined by the band structure and tetragonal distortion changes. The results predict a transition from a monoclinic structure to a fluorite-type cubic structure at the pressure of 37 GPa. Thermodynamic property calculations of monoclinic zirconia (m-ZrO2) were also carried out. Temperature-dependent heat capacity, entropy, free energy, Debye temperature of monoclinic zirconia, from 0 to 1000 K, were computed, and they compared well with those reported in the literature. Moreover, the atomistic simulations correctly predicted the phase transitions of m-ZrO2 under compressive pressures ranging from 0 to 70 GPa. The phase transition pressures of monoclinic to orthorhombic I (3 GPa), orthorhombic I to orthorhombic II (8 GPa), orthorhombic II to tetragonal (37 GPa), and stable tetragonal phases (37-60 GPa) are in excellent agreement with experimental data. In the second part of this study, the mechanical response of yttria-stabilized zirconia under Rockwell superficial indentation was studied. The microstructure image based finite element method was used to validate the model using a composite cermet material. Then, the finite element model of Rockwell indentation of yttria-stabilized zirconia was developed, and the result was compared with experimental hardness data. ItemAtomistic Study of the Effect of Magnesium Dopants on Nancrystalline Aluminium(2019-08) Kazemi, Amirreza; Yang, Shengfeng; Zhang, Jing; Zhu, LikunAtomistic simulations are used in this project to study the deformation mechanism of polycrystalline and bicrystal of pure Al and Al-Mg alloys. Voronoi Tessellation was used to create three-dimensional polycrystalline models. Monte Carlo and Molecular Dynamics simulations were used to achieve both mechanical and chemical equilibrium in all models. The first part of the results showed improved strength, which is included the yield strength and ultimate strength in the applied tensile loading through the addition of 5 at% Mg to nanocrystalline aluminum. By viewing atomic structures, it clearly shows the multiple strengthening mechanisms related to doping in Al-Mg alloys. The strength mechanism of dopants exhibits as dopant pinning grain boundary (GB) migration at the early deformation stage. At the late stage where it is close to the failure of nanocrystalline materials, Mg dopants can stop the initiation of intergranular cracks and also do not let propagation of existing cracks along the GBs. Therefore, the flow stress will improve in Al-Mg alloy compared to pure Al. In the second part of our results, in different bicrystal Al model, ∑ 3 model has higher strength than other models. This result indicates that GB structure can affect the strength of the material. When the Mg dopants were added to the Al material, the strength of ∑5 bicrystal models was improved in the applied shear loading. However, it did not happen for ∑ 3 model, which shows Mg dopants cannot affect the behavior of this GB significantly. Analysis of GB movements shows that Mg dopants stopped GBs from moving in the ∑ 5 models. However, in the ∑ 3 GB, displacement of grain boundary planes was not affected by Mg dopants. Therefore, the strength and flow stress are improved by Mg dopants in ∑ 5 Al GBs, not in the ∑ 3 GBs. ItemAu nanoparticle assembly on cnts using flash induced solid-state dewetting(2015-04-28) Kulkarni, Ameya; Ryu, Jong Eun; Agarwal, Mangilal; Xie, Jian; Cheng, RuihuaCarbon Nanotubes (CNTs) are used extensively in various applications where substrate are required to be possessing higher surface area, porosity and electrical and thermal conductivity. Such properties can be enhanced to target a particular gas and biochemical for efficient detection when CNT matrix is functionalized with Nanoparticles (NPs). Conventional functionalization involves harsh oxidation repeated washing, filtration and sonication, which induce defects. The defects lead to hindered mobility of carriers, unwanted doping and also fragmentation of the CNTs in some cases. In this document we demonstrate functionalization of CNT with Au nanoparticles on a macro scale under dry and ambient condition using Xenon ash induced solid-state dewetting. A sputtered thin film was transformed into nanoparticles which were confirmed to be in a state of thermodynamic equilibrium. We worked on 3 nm, 6 nm, 9 nm, 15 nm, 30 nm initial thickness of thin films. Xenon ash parameters of energy, number of pulse, duration of pulse, duration of gap between consecutive pulses were optimized to achieve complete dewetting of Au thin films. 3 nm deposition was in the form of irregular nano-islands which were transformed into stable nanoparticles with a single shot of 10 J/cm2 of 2 ms duration. 6 nm and 9 nm deposition was in form of continues film which was also dewetted into stable nanoparticles with a single pulse but with an increased energy density of 20 J/cm2 and 35 J/cm2 respectively. In case of 15 nm and 30 nm deposition the thin film couldn't be dewetted with a maximum energy density of 50 J/cm2, it was observed that 3 and 4 pulses of 2 ms pulse duration and 2 ms gap duration with an energy density of 50 J/cm2 were required to completely dewet the thicker films. However irregularity was induced in the sizes of the NPs due to Ostwald ripening phenomenon which causes smaller particle within a critical difiusion length to combine and form a larger particle during or after dewetting process. For comparison, the Au thin films were also dewetted by a conventional process involving annealing of samples until the thin film was fully transformed into NPs and the size of NPs seized to grow. Scanning electron microscope (SEM) was used to characterize the samples. Thermodynamic stability of the particles was confirmed with statistical analyses of size distribution after every additional pulse. ItemAn Automated Grid-Based Robotic Alignment System for Pick and Place Applications(2013-12) Bearden, Lukas R.; Razban, Ali; Wasfy, Tamer; Li, Lingxi; Anwar, SohelThis thesis proposes an automated grid-based alignment system utilizing lasers and an array of light-detecting photodiodes. The intent is to create an inexpensive and scalable alignment system for pick-and-place robotic systems. The system utilizes the transformation matrix, geometry, and trigonometry to determine the movements to align the robot with a grid-based array of photodiodes. The alignment system consists of a sending unit utilizing lasers, a receiving module consisting of photodiodes, a data acquisition unit, a computer-based control system, and the robot being aligned. The control system computes the robot movements needed to position the lasers based on the laser positions detected by the photodiodes. A transformation matrix converts movements from the coordinate system of the grid formed by the photodiodes to the coordinate system of the robot. The photodiode grid can detect a single laser spot and move it to any part of the grid, or it can detect up to four laser spots and use their relative positions to determine rotational misalignment of the robot. Testing the alignment consists of detecting the position of a single laser at individual points in a distinct pattern on the grid array of photodiodes, and running the entire alignment process multiple times starting with different misalignment cases. The first test provides a measure of the position detection accuracy of the system, while the second test demonstrates the alignment accuracy and repeatability of the system. The system detects the position of a single laser or multiple lasers by using a method similar to a center-of-gravity calculation. The intensity of each photodiode is multiplied by the X-position of that photodiode. The summed result from each photodiode intensity and position product is divided by the summed value of all of the photodiode intensities to get the X-position of the laser. The same thing is done with the Y-values to get the Y-position of the laser. Results show that with this method the system can read a single laser position value with a resolution of 0.1mm, and with a maximum X-error of 2.9mm and Y-error of 2.0mm. It takes approximately 1.5 seconds to process the reading. The alignment procedure calculates the initial misalignment between the robot and the grid of photodiodes by moving the robot to two distinct points along the robot’s X-axis so that only one laser is over the grid. Using these two detected points, a movement trajectory is generated to move that laser to the X = 0, Y = 0 position on the grid. In the process, this moves the other three lasers over the grid, allowing the system to detect the positions of four lasers and uses the positions to determine the rotational and translational offset needed to align the lasers to the grid of photodiodes. This step is run in a feedback loop to update the adjustment until it is within a permissible error value. The desired result for the complete alignment is a robot manipulator positioning within ±0.5mm along the X and Y-axes. The system shows a maximum error of 0.2mm in the X-direction and 0.5mm in the Y-direction with a run-time of approximately 4 to 5 minutes per alignment. If the permissible error value of the final alignment is tripled the alignment time goes down to 1 to 1.5 minutes and the maximum error goes up to 1.4mm in both the X and Y-directions. The run time of the alignment decreases because the system runs fewer alignment iterations.