Grinding burn classification with surface Barkhausen noise measurements

Abstract

Industrial Barkhausen noise (BN) measurements are commonly utilized for final quality control after machining operations such as grinding to point out grinding burns. Grinding burns might compromise the final use and fatigue lifetime of the ground component. The industrial BN method itself is based on a pre-determined threshold value of the BN root-mean-square value (RMS). Elevated RMS values indicate detrimental changes in the component. Usually, the evaluation of grinding burn severity is not carried out. In this study, real ground cylindrical samples were collected that were rejected based on an industrial quality control with a BN unit. A more detailed BN analysis was carried out for<br/>41 individual grinding burn locations followed by X-ray diffraction based residual stress (RS) surface measurements and residual stress and diffraction peak full-width-at-half-maximum (FWHM) depth profiles. K-means clustering was applied to profiles to label the data points related to grinding burns of different severity. Three classes of grinding burns were identified and verified by micrographs and hardness. A linear discriminant classification model was then identified between the surface BN measurement features and labeled data points. The classification results were reasonable with about 80 %<br/>classification accuracy at worst. They showed that the classes identified can be detected with the surface BN measurements. Thus, the approach presented in this paper shows great potential in the practical use of BN measurement where grinding burns can be detected and evaluated with a surface BN measurement.