Browsing by Author "Taetragool, Unchalisa"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- Numerical techniques to find optimal input parameters for achieving mean particles’ temperature and axial velocity in atmospheric plasma spray processBatra, Romesh C.; Taetragool, Unchalisa (Nature Research, 2020)We numerically find values of four process input parameters, namely, the argon flow rate, the hydrogen flow rate, the powder feed rate, and the current, that yield the desired mean particles’ temperature and the mean particle velocity (collectively called mean particles’ characteristics, or MPCs) in an atmospheric plasma spray process just before the particles arrive at the substrate to be coated. Previous studies have shown that the coating quality depends upon the MPCs. The process is simulated by using the software, LAVA-P-3D, that provides MPCs close to their experimental values. Thus, numerical rather than physical experiments are conducted. We first use the design of experiments to characterize the sensitivity of the MPCs to process parameters. We then identify relationships between the significant input parameters and the MPCs by using two methods, namely, the least squares regression and the response surface methodology (RSM). Finally, we employ an optimization algorithm in conjunction with the weighted sum method to find optimum values of the process input variables to achieve desired values of the MPCs. The effects of weights assigned to the objective functions for the temperature and the velocity, and the difference in using the regression and the RSM model have been studied. It is found that these values of the process parameters provide MPCs within 5% of their desired values. This methodology is applicable to other coating processes and fabrication technologies such as hot forging, machining and casting.
- Optimal Parameters for Doubly Curved Sandwich Shells, Composite Laminates, and Atmospheric Plasma Spray ProcessTaetragool, Unchalisa (Virginia Tech, 2018-01-31)Optimization is a decision making process to solve problems in a number of fields including engineering mechanics. Bio-inspired optimization algorithms, including genetic algorithm (GA), have been studied for many years. There is a large literature on applying the GA to mechanics problems. However, disadvantages of the GA include the high computational cost and the inability to get the global optimal solution that can be found by using a honeybee-inspired optimization algorithm, called the New Nest-Site Selection (NeSS). We use the NeSS to find optimal parameters for three mechanics problems by following the three processes: screening, identifying relationships, and optimization. The screening process identifies significant parameters from a set of input parameters of interest. Then, relationships between the significant input parameters and responses are established. Finally, the optimization process searches for an optimal solution to achieve objectives of a problem. For the first two problems, we use the NeSS algorithm in conjunction with a third order shear and normal deformable plate theory (TSNDT), the finite element method (FEM), a one-step stress recovery scheme (SRS) and the Tsai-Wu failure criterion to find the stacking sequence of composite laminates and the topology and materials for doubly curved sandwich shells to maximize the first failure load. It is followed by the progressive failure analysis to determine the ultimate failure load. For the sandwich shell, we use the maximum transverse shear stress criterion for delineating failure of the core, and also study simultaneously maximizing the first failure load and minimizing the mass subject to certain constraints. For composite laminates, it is found that the first failure load for an optimally designed stacking sequence exceeds that for the typical [0°/90°]₅ laminate by about 36%. Moreover, the design for the optimal first failure load need not have the maximum ultimate load. For clamped laminates and sandwich shells, the ultimate load is about 50% higher than the first failure load. However, for simply supported edges the ultimate load is generally only about 10% higher than the first failure load. For the atmospheric spray process, we employ the NeSS algorithm to find optimal values of four process input parameters, namely the argon flow rate, the hydrogen flow rate, the powder feed rate and the current, that result in the desired mean particle temperature and the mean particle velocity when they reach the substrate. These optimal values give the desired mean particle temperature and the mean particle velocity within 5% of their target values.