Determination of optimal drop height in free-fall shock test using regression analysis and back-propagation neural network

Abstract

The primary purpose of this study is to provide methods that can be used to determine the most suitable drop height for shock testing military equipment, in an efficient and cost ineffective manner. Shock testing is widely employed to assess the performance of electronic systems, including military devices and civilian systems. Determining the height of the drop for a test item is an important step prior to performing the test. Dropping a test item from excessive height leads high G-peak values to damage the test equipment. On the other hand, dropping an item from a low height may not generate the required G-peak value and duration. Therefore, prior to performing shock tests, an optimal drop height must be established to ensure that the resulting G-peak value and duration time match the required test values. The traditional trial-and-error methods are time-consuming and cost-ineffective, often requiring many attempts. To improve the conventional approaches, this study proposes the application of regression analysis and back-propagation neural network for determining the most suitable drop height for free-fall shock tests. A new method is suggested for determining the drop test height. The results of the model are verified, using the results of a series of experiments that are conducted to verify the accuracy of the suggested approaches. The results of the study indicate that both approaches are equally effective in providing an effective guideline for performing drop tests from heights that would result in the peak Gs and duration needed for testing electronic devices.