Document Type

Thesis

Date of Award

5-31-1985

Degree Name

Master of Science in Mechanical Engineering - (M.S.)

Department

Mechanical Engineering

First Advisor

Richard C. Progelhof

Second Advisor

Joseph Ellis Kopf

Third Advisor

E. S. Geskin

Abstract

The ASTM D3029 drop weight test on plasic parts or sheeting is simulated in this thesis. The simulation is accomplished by using the Bruceton staircase method or the Up-and-Down method and the normal distribution theory. The "Probit" method is also analyzed.

The simulation programs are done in FORTRAN computer language. First, the random number generator is used to find the probability value between 0 and 1.0. Then, the cumulative probability distribution formula is used to find the absolute standard normal value. This absolute standard normal value is converted in to X normal value using given input values of mean and standard deviation. This procedure is repeated for each test specimen. Then, the Bruceton staircase method is applied to find the mean failure weight or mean failure height of the particular plastic sample depending on the method used. For the "Probit" method a set of test specimens are simulated and compared to the test value. These simulations are done for single normal and multinormal distributions. In the case of multinormal distribution, two single normal distribution values of the same type of material and dimensions are used, but different input values of mean and standard deviations are taken to see its effect on the mean failure energy.

The results for ASTM D3029 test methods were found to be in very good agreement with the pretested initial values as expected for single normal distribution. For the "Probit" method, the uncertainty at the lower failure levels requires more test specimens in a set than is necessary at the higher failure levels.

For multinormal distributions, both methods are extremely cumbersome and the Bruceton staircase technique can produce misleading results. However, the "Probit" approach although using an excessive number of test specimens does indicate more complex phenomena occuring in the test samples.

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