The purpose of this study was to use a systematic approach to model the welding parameters for tungsten inert gas (TIG) welding of two thin pieces of titanium sheet metal. The experiment was carried out using a mechanized welding equipment to consistently perform each trial. The Latin Hyper Cube sampling method for data generation was applied to four input variables and the Kriging interpolation technique was used to develop surrogate models that map the four input variables to the three output parameters of interest. A total of fifty data points were generated. To utilize the minimal amount of weld samples, Kriging models were created using five sets of data at a time and the surrogate model was tested against an actual data output. Once the models have been generated and verified, an attempt is made to optimize the output variables individually and as a multi-objective problem using genetic algorithm (GA) optimization.
|Commitee:||Chen, Hsin-Piao, Yavari, Parviz|
|School:||California State University, Long Beach|
|Department:||Mechanical and Aerospace Engineering|
|School Location:||United States -- California|
|Source:||MAI 53/01M(E), Masters Abstracts International|
|Keywords:||Genetic algorithm optimization, Tungsten inert gas, Welding|
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