An improved inverse power law model for accelerated fatigue life prediction of 6061-T6 and AZ31B-F. (August 2022)

• Record Type: Journal Article
• Authors: Ge, Changyuan; Xu, Zizhen; Du, Kaifan; Zhou, Caihua; Tan, Chenggong; Liu, Wenhu; Xu, Shengli; Wang, Bo
• Is Part Of: Engineering failure analysis. Volume 138(2022)
• Publication Date: 2022-08
• Subjects: Accelerated fatigue -- Two-stage S-N curve -- Improved inverse power law model -- Segmented fitting strategy; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2022.106381
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: An improved inverse power law model (IIPL) is proposed to improve the accuracy of fatigue life prediction. For single-stage S-N curves, the life prediction formula of IIPL is the same as the inverse power law model (IPL), and the prediction error does not exceed 12%. For two-stage S-N curves, the prediction errors of IIPL are several orders of magnitude smaller than that of IPL. The fatigue life of materials can be directly predicted by IIPL without fatigue experiments. Abstract: Accelerated vibration fatigue, using the inverse power law model (IPL), is effective to evaluate the durability of materials. However, for 6061-T6 and AZ31B-F with two-stage S-N curves, the prediction error exceeds orders of magnitude. Therefore, an improved inverse power law model (IIPL) is proposed. The results obtained from the vibration fatigue experiments show that the fatigue life prediction accuracy of IIPL is more acceptable than IPL. For two-stage S-N curves, the error of IIPL is between −61.8% – −0.8%, better than that of IPL (8.68 × 10 1 % – 2.42 × 10 5 %). Even without accelerated fatigue tests, the error by IIPL is also reasonable (-69.1% – 23.2%).