title: Material grain size characterization method based on energy attenuation coefficient spectrum and support vector regression.
abstract: A grain size characterization method based on energy attenuation coefficient spectrum and support vector regression (SVR) is proposed. First, the spectra of the first and second back-wall echoes are cut into several frequency bands to calculate the energy attenuation coefficient spectrum. Second, the frequency band that is sensitive to grain size variation is determined. Finally, a statistical model between the energy attenuation coefficient in the sensitive frequency band and average grain size is established through SVR. Experimental verification is conducted on austenitic stainless steel. The average relative error of the predicted grain size is 5.65%, which is better than that of conventional methods.
authors.affiliation: Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China. Electronic address: limin@ustb.edu.cn.
authors.forename: Min
authors.initials: M
authors.lastname: Li
authors.affiliation: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
authors.forename: Tong
authors.initials: T
authors.lastname: Zhou
authors.affiliation: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
authors.forename: Yanan
authors.initials: Y
authors.lastname: Song
keywords: Austenitic stainless steel; Energy attenuation coefficient spectrum; Grain size characterization; Support vector regression; Ultrasonic test