Redefinition of failure envelope of composite materials considering uncertainty of test data

Abstract

After conducting three World-Wide Failure Exercises (WWFEs), it has become apparent that existing strength theories or criteria have certain deficiencies, and no single theory can accurately match all experimental results. As a result, there is a growing focus on the uncertainties present in the macrocosm test data of composite materials. The paper introduces the concept of uncertainty into the collection of test data for the classical Tsai-Wu failure criterion and Hashin criterion. The method proposed involves fitting the coefficients of the failure criteria by treating experimental data as random variables, a technique that diverges significantly from the conventional method of using composite allowable values to define these coefficients. It also uses the probability of success as a constraint and defines the fitted error as an optimization objective. Monte Carlo sampling is employed to perform a reliability analysis, and six sigma is used for a reliability-based optimization of the failure criteria. The Multi-Island Genetic Algorithm is utilized to search for the optimal value. The approach presented in this study provides the ability to predict future failures in composite materials by considering the uncertainty of the test data. By incorporating uncertainties into the analysis and design process, a more accurate and reliable assessment of the performance of composite materials can be achieved. Moreover, there seems to be a significant difference in the fitting error between the mean data and the normal distribution data. Additionally, when Hashin criterion is modified, the test data matches the failure envelope more closely and the degree of fit between the experimental data and the failure envelope is higher.