Developing the Shock Model and Predicting the Optimal Drop Height for a Free-Fall Shock Machine

• Main Authors: Chia-Hung Wu, 吳家宏
• Other Authors: 陳昭榮
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/gvfcu8

博士 === 國立臺北科技大學 === 電機工程系博士班 === 101 === Shock test is widely adopted to assess the performance of electronic military devices or equipments in a free-fall shock machinec for avoiding the failure in the shock environment. Determining the height of dropping a test item is a very important stage before shock test. However, dropping a test item from an excessive height leads an excessive peak of amplitude and will damage the item, but dropping a test item from a low height may not reach the required peak of amplitude of specification. Therefore, prior to shock tests, an optimal drop height must be established to ensure that the obtained peak of amplitude and duration time meet required test values. Traditional trial-and-error method is typically time-consuming, requiring numerous trials, and cost-ineffective to obtain required peak of amplitude and duration time. To improve the approach, this study builds a shock model of a half sine wave form and using back propagation neural network (BPNN) for estimating the height by more known data sets and comparison with regression method. On the other hand, applying gray predicting method estimates the optimal height which meets the specification by few known data sets. Finally, actual laboratory experiments will be performed for verifying the ability and degree of accuracy. This study has a derived model from shock motion and performs real shock tests to check the performance. It can apply in the real shock tests of lab and the contribution of this study can upgrade more effective and accuracy.