Statistical description of fracture toughness revisited: Implications for evaluation of the reference temperature, T0, and characteristic fracture toughness
Claudio Ruggieri , Luís G.T.S. Leite , Daniel C.F. Ferreira
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引用次数: 0
Abstract
The present study focuses on further extensions of the more general three-parameter Weibull distribution to describe the statistical scatter of fracture toughness values and to evaluate the characteristic toughness of structural steels using a statistical description of toughness data in comparison with the minimum of three equivalent (MOTE) method. Fracture toughness tests conducted on several types of structural steels, including an ultra high strength steel and pressure vessel steels, provide the experimental data upon which the Weibull statistical analyses are conducted. These analyses compare descriptions of fracture toughness values based on a standard three-parameter Weibull function with fixed values for parameters and , and a general three-parameter Weibull distribution with unknown parameters in connection with a goodness-of-fit method to assess how well the experimental data fits the assumed distribution. Further, the study also shows that use of a fixed percentile of the distribution describing the toughness data set provides more consistent values of characteristic toughness compared to the MOTE procedure.
期刊介绍:
Mechanics of Materials is a forum for original scientific research on the flow, fracture, and general constitutive behavior of geophysical, geotechnical and technological materials, with balanced coverage of advanced technological and natural materials, with balanced coverage of theoretical, experimental, and field investigations. Of special concern are macroscopic predictions based on microscopic models, identification of microscopic structures from limited overall macroscopic data, experimental and field results that lead to fundamental understanding of the behavior of materials, and coordinated experimental and analytical investigations that culminate in theories with predictive quality.