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Sample functions, i.e., stochastic process realizations, are used to define cumulative damage phenomena which end into an observable terminal state or failure. The complexity inherent to such phenomena justifies the use of phenomenological models associated with the evolution of a physical magnitude feasible to be monitored during the test. Sample functions representing the damage evolution may be identified, once normalized to the interval [0,1], with cumulative distribution functions (cdfs), generally, of the generalized extreme value (GEV) family. Though usually only a fraction of the whole damage evolution, according to the specific problem handled, is available from the test record, the phenomenological models proposed allow the whole damage process to be recovered. In this way, down- and upwards extrapolations of the whole damage process beyond the scope of the experimental program are provided as a fundamental tool for failure prediction in the practical design. The proposed methodology is detailed and its utility and generality confirmed by its successive application to representative well-known problems in fatigue and fracture characterization. The excellent fittings, the physical interpretation of the model parameters and the good expectations to achieve a complete probabilistic analysis of these phenomena justify the interest of the proposed phenomenological approach with possible applications to other cumulative damage processes.
Hydraulic pressure testing of composite gas cylinders damages the cylinders, but provides very limited information. For this reason, the Federal Institute of Materials Research and Testing (BAM) demands the assessment of the safety of design types for retest periods of more than three years by employing destructive testing of small samples.
This gives insight in properties not quantified by current standards.
Depending on the cycle fatigue behaviour of the individual design type either cycle tests or slow burst tests are employed. In this paper methods are introduced for a statistical assessment of sample results of both test methods. This includes gathering the average and load cycle burst strength, and the scatter of a sample as well as analysing and plotting them as a pair of values. Each pair of values representing a sample can be assessed approximately regarding its survival rate in service, if the introduced performance charts are employed.