Date of Completion
4-23-2019
Embargo Period
4-23-2019
Advisors
Jeongho Kim, Michael Accorsi, Wei Zhang
Field of Study
Civil Engineering
Degree
Master of Science
Open Access
Open Access
Abstract
Finite element analysis (FEA) has become an increasingly popular tool used by researchers and professionals for comparing experimental data to theoretically expected values. Its use can prove to be an invaluable resource when analyzing or designing a system by giving the user the ability to understand the process being modeled at a much deeper level. However, finite element analysis it is still prone to generating inaccurate results if not used properly. When simulating certain physical processes, such as those found during manufacturing, the obtained results are sometimes prone to error when compared with their experimentally obtained counterparts.
The present work concentrates on exploring the various methods available for improving the accuracy of FEA for manufacturing processes. Specifically, the methods of improvement are explored as applied to laser-induced bending for a thin metal sheet. A series of sequentially-coupled thermomechanical analyses have been created for the laser bending process and the results have been improved upon through multiple analysis configurations. Through this approach, numerous finite element models have been created in order to study the effects of incorporating different element technologies available within current FEA commercial software. An improved model has been created and is discussed in detail for its better performance. Additionally, many model configurations for the same laser forming analysis are presented that do not show superior performance. They are included to investigate why certain modeling configurations do not yield accurate results.
Recommended Citation
Johanson, Joseph, "Improving the Accuracy of a Finite Element Based Manufacturing Simulation: A Case Study of Laser-Induced Bending in a Thin Metal Sheet" (2019). Master's Theses. 1328.
https://digitalcommons.lib.uconn.edu/gs_theses/1328
Major Advisor
Jeongho Kim