Evaluation of Charpy-V transition curve fitting based on distribution-free statistical assessment

Abstract

A non-parametric, distribution-free, statistical assessment of 5 large Charpy transition curve data sets is used for the optimum fitting equations for smaller data sets. The assessment makes use of a combination of rank probability and binomial probability analysis of the data. The original non-parametric assessment method is improved by combining upper and lower bound binomial estimates, thus removing a bias that exists in the original method. The non-parametric assessment is not suitable as a standard method because it requires too many data points to give a reliable result. It is, however, ideal as a research tool to examine transition curve shape and scatter. Based on the assessment several recommendations for transition curve fitting can be made.