Abstract

Interpretation of field data from shock tests and subsequent assessment of product safety margins via laboratory testing are based on the shock response spectra (SRS). The SRS capture how a single degree of freedom (SDOF) structure responds to the shock at differing frequencies and, therefore, no longer contain the duration or other temporal parameters pertaining to the shock. A single duration can often be included in the technical specification or in the recreation of acceleration vs. time history from the specified SRS; however, there is little basis for that beyond technical judgment. The loss of such temporal information can result in the recreated SRS being the same while its effect on a system or component can be different. This paper attempts to quantify this deficiency as well as propose a simple method of capturing damping from shock waves that can allow the original waveform to be more accurately reconstructed from the SRS. In this study the decay rate associated with various frequencies that comprise the overall shock was varied. This variation in the decay rate leads to a variation in the acceleration vs. time history, which can be correlated to a “Damage Index” that captures the fatigue damage imparted to the object under shock. Several waveforms that have the same SRS but varying rates of decay for either high- or low-frequency components of the shock were investigated. The resulting variation in stress cycles and Damage Index is discussed in the context of the lognormal distribution of fatigue failure data. It is proposed that, along with the SRS, the decay rate is also captured to minimize the discrepancy between field data and representative laboratory tests.

Highlights

  • Mechanical vibration and shock have a detrimental effect on components or systems. erefore, shock tests are conducted to ensure items can survive the repeated shocks that they are expected to endure during their lifetime. e level of shock to be applied in the shock tests is prescribed in terms of the shock response spectra (SRS). e SRS levels used for testing are typically prescribed in the terms of P99 or P95 levels

  • An issue that arises with this approach is that SRS are not a unique representation of shock; various shock events can have the same SRS. e different shocks corresponding to the same SRS will inflict a different amount of damage to the item of interest

  • Statistical data are available in the literature on how the Damage Index at failure (# of stress cycles leading to failure) varies among tests. is information can be used to assess how a variation in the number and magnitude of stress cycles relates to the probability of failure. is, in turn, allows the variation in stress cycles from the 4 trials to be related to the consequent variation in the probability of failure. e various sources of data and the analytical methods are presented

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Summary

Introduction

Mechanical vibration and shock have a detrimental effect on components or systems. erefore, shock tests are conducted to ensure items can survive the repeated shocks that they are expected to endure during their lifetime. e level of shock to be applied in the shock tests is prescribed in terms of the shock response spectra (SRS). e SRS levels used for testing are typically prescribed in the terms of P99 or P95 levels (for example, P99 implies that the shock applied in the test will exceed 95% of the shock the item may see in its lifetime). E different shocks (decaying acceleration vs time) corresponding to the same SRS will inflict a different amount of damage to the item of interest. E consequence of the shock can be measured in terms of a “Damage Index”. A “Damage Index” is quantified in terms of the magnitude and number of stress cycles as per equation (1). Statistical data are available in the literature on how the Damage Index at failure (# of stress cycles leading to failure) varies among tests. Is information can be used to assess how a variation in the number and magnitude of stress cycles relates to the probability of failure. Is, in turn, allows the variation in stress cycles from the 4 trials to be related to the consequent variation in the probability of failure. Statistical data are available in the literature on how the Damage Index at failure (# of stress cycles leading to failure) varies among tests. is information can be used to assess how a variation in the number and magnitude of stress cycles relates to the probability of failure. is, in turn, allows the variation in stress cycles from the 4 trials to be related to the consequent variation in the probability of failure. e various sources of data and the analytical methods are presented

Implications of Fatigue Test Data
Damage Corresponding to Different Decay Rates for the Same SRS
Consequence of Damage Variations in Test Reliability Levels
Capturing Rate of Decay of Shock
Conclusions
Findings
Disclosure
Full Text
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