Error predicting for material Brinell hardness measurement of  poor information based on grey bootstrap method

Error predicting for material Brinell hardness measurement of poor information is a common problem in the field of hardness measurement. Only small sample measurement data obtained for Brinell hardness measurements are destructive. Different from statistical methods, a novel poor information Brinell hardness measurement error prediction method was presented, which was based on grey system theory and bootstrap theory. After calibrated all measurement error sources, all measurement error transfer coefficients should be calculated and the calibration data of error sources should be sampled in terms of bootstrap theory. The predictions of calibration data of all error sources were gained by a grey Bootsrap fusion model. The error prediction values were obtained for material Brinell hardness measurement of poor information in terms of error combination principle. In an example of a general Brinell hardness measurement, the predicting Brinell hardness measurement errors acquired by this novel proposed method and the actual measurement errors were shown to be in a good agreement with each other, and the validity of the proposed method was also represented.