Modeling Heat Treatment for Characterizing Distortion, Residual Stress, Loading Response and Fracture in a Vacuum Carburized and Gas Quenched Steel Coupon

Monday, June 16, 2014: 8:00 AM
Sun 5 (Gaylord Palms Resort )
Mr. Andrew Freborg , DANTE Solutions, Inc., Cleveland, OH
Dr. Zhichao Li , DANTE Solutions, Inc., Cleveland, OH
Dr. B. Lynn ferguson , DANTE Solutions, Inc., Cleveland, OH
Understanding residual stresses in steel parts during processing and subsequent loading applications is a critical engineering requirement. Carburization introduces additional complexity in terms of characterizing local hardening and a varied residual stress gradient from case through the part core. Aerospace transmission components are typically manufactured from high strength, case carburized alloy steels such as AMS 6308 (Pyrowear®53). The combination of carburization and quench hardening of these steels produces residual compressive surface stresses and high surface hardness, for the specific purpose of enhancing fatigue resistance and surface durability. Using an internal state variable (ISV) material model, the DANTE heat treatment simulation software code was successfully applied to characterize the heat treatment response for this steel. The model provided critical engineering data for understanding microstructural, residual stress and distortion response. This paper describes the use of heat treatment simulation to engineer residual stress and distortion response in a complex shaped AMS 6308 alloy steel coupon, for subsequent cyclic load testing and evaluation of stress relaxation response. The use of process-descriptive boundary conditions is presented in the context of vacuum carburizing and gas quenching. Model predicted residual stress and distortion response for a tapered, notched coupon are validated against x-ray diffraction and dimensional physical testing. Preliminary results of stress relaxation tests are presented, and physical evaluation of failure mode in the coupon is examined in the context of the process and loading model predictions.