Novel approach to predict the crash testing data using multiple regression analysis and principal component analysis

Ravi Lochan Kallur, University of Texas at El Paso

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.^

Subject Area

Engineering, Industrial

Recommended Citation

Kallur, Ravi Lochan, "Novel approach to predict the crash testing data using multiple regression analysis and principal component analysis" (2009). ETD Collection for University of Texas, El Paso. AAI1468298.
http://digitalcommons.utep.edu/dissertations/AAI1468298

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