Abstract

Friction stir welding is an advanced welding process that is being investigated for use in many different industries. One area that has been investigated for its application is in healing critical nuclear reactor components that are developing cracks. However, friction stir welding is a complicated process and it is difficult to predict what the final properties of a set of welding parameters will be. This thesis sets forth a method using finite element analysis and a random forest model to accurately predict hardness in the welding nugget after processing. The finite element analysis code used and ALE formulation that enabled an Eulerian approach to modeling. Hardness is used as the property to estimate because of its relationship to tensile strength and grain size. The input parameters to the random forest model are temperature, cooling rate, strain rate, and RPM. Two welding parameter sets were used to train the model. The method was found to have a high level of accuracy as measured by R^2, but had greater difficulty in predicting the parameter set with higher RPM.

Degree

MS

College and Department

Ira A. Fulton College of Engineering and Technology; Mechanical Engineering

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2019-08-01

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd10931

Keywords

friction stir welding, finite element analysis, grain size, hardness, machine learning, random forest

Language

english

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