Estimation of deformation induced martensite in austenitic stainless steels

摘要: Abstract The extent of deformation induced martensite (DIM) is controlled by steel chemistry, strain rate, stress, strain, grain size, stress state, initial texture and temperature deformation. In this research, a neural network model within Bayesian framework has been created using extensive published data correlating the DIM with its influencing parameters in variety austenitic grade stainless steels. method puts error bars on predicted value rate allows significance each individual parameter to be estimated. addition, it possible estimate isolated influence particular variable such as which cannot practice varied independently. This demonstrates ability investigate new phenomena cases where information accessed experimentally. applied confirm that predictions are reasonable context metallurgical principles, present experimental other recent literatures.