Date of Award
2009-01-01
Degree Name
Master of Science
Department
Manufacturing Engineering
Advisor(s)
Tzu-Liang (Bill) Tseng
Abstract
In the design process of vehicles, crash tests are very critical to determine the safety measures. Every motor manufacturing company has to maintain certain standards for their autos. This will lead to the design optimization of safety measures utilizing the data available from crash tests. The engineers perform various experiments to generate data from crash testing of vehicles in their manufacturing facility. With the help of simulators; they create a virtual environment to perform design changes. Hence the data obtained from the crash tests is vital in design optimization of safety systems. The present study deals with the technologies involved in analyzing data obtained from these experiments to ensure the prediction of data from crash tests being accurate. Current approach compares Multiple Regression Analysis and Principal Component Analysis for the prediction of data. The present work successfully derived methods for predicting data mere accurately to help the engineers reducing their efforts in conducting real time crash tests.
Language
en
Provenance
Received from ProQuest
Copyright Date
2009
File Size
70 pages
File Format
application/pdf
Rights Holder
Ravi Lochan Kallur
Recommended Citation
Kallur, Ravi Lochan, "Novel Approach To Predict The Crash Testing Data Using Multiple Regression Analysis And Principal Component Analysis" (2009). Open Access Theses & Dissertations. 292.
https://scholarworks.utep.edu/open_etd/292