Critical analysis of the prediction of stress relaxation from forward creep of Type 316H austenitic stainless steel. (5th April 2016)

• Record Type: Journal Article
• Authors: Wang, Y.Q.; Spindler, M.W.; Truman, C.E.; Smith, D.J.
• Is Part Of: Materials & design. Volume 95(2016)
• Page Start: 656
• Page End: 668
• Publication Date: 2016-04-05
• Subjects: Stress relaxation -- Forward creep -- Constitutive equations -- 316H austenitic stainless steel; Materials -- Periodicals; Engineering design -- Periodicals; Matériaux -- Périodiques; Conception technique -- Périodiques; Electronic journals; 620.11
• DOI: 10.1016/j.matdes.2016.01.118
• Languages: English
• ISSNs: 0264-1275

Abstract

Abstract: This work evaluates the effectiveness of using forward creep deformation laws to predict the observed relaxation behaviour of 316H austenitic stainless steel. A summary of empirical creep methods, forward and stress relaxation experimental data were given. A primary and secondary creep strain rate (RCC-MR) model and an average creep strain rate model are combined with derived material constants to predict a group of stress relaxation tests at temperatures from 475 °C to 600 °C. The RCC-MR primary creep model predicted the stress relaxation behaviour soundly and the average creep strain rate model underestimated all of the stress relaxation data. The difference between stress relaxations predicted using strain hardening and time hardening versions of the RCC-MR equations are small. Factors affecting the predictions; including limitations in data range, data scatter, selection of a forward creep model, application of a forward model to stress relaxation and difference between forward creep and stress relaxation and are discussed. Graphical abstract: Highlights: A critical analysis of how to predict long term stress relaxation based on forward creep data and models is presented. The success of using forward creep to predict stress relaxation demonstrates the stress relaxation can be treated as creep at reducing stress. Limitations in data range, data scatter and selection of a forward creep model can affect the prediction results.