Quantitative mapping of service process-microstructural degradation-property deterioration for a Ni-based superalloy based on chord length distribution imaging process. (May 2021) - Fan, Y.S.; Yang, X.G.; Shi, D.Q.; Tan, L.; Huang, W.Q.
Abstract: Directionally solidified (DS) and single crystal (SC) Ni-based superalloys inevitably underwent microstructural degradation induced by the harsh operating environment. For the safety service and economic overhaul, constructing a quantitative mapping chain from service process to microstructural degradation and to property deterioration is critically essential. The present work started with stress-free and stress-assisted pre-service treatments of a DS Ni-based superalloy to obtain microstructures with different degraded states. An imaging process based on two-phase rotary chord length distributions was established to extract the high dimensional statistical information for identifying the morphology and size features of microstructures. To reduce the dimension of the statistical information and quantitatively characterize the microstructural states in fewer parameters, principal component analysis was employed to capture the core microstructural indicators, which was utilized to establish the response surface between the deterioration of fatigue resistance and the microstructural degradation. Finally, a multi-output support vector regression (SVR) model was constructed to map between service process and microstructural degradation. The results showed acceptable accuracy to estimate the microstructural degradation of pre-serviced alloys. Meanwhile, the framework provides a technical chain for the waste determination and microstructural degradation estimation of the hot section components made by DS and SC Ni-based superalloys. Graphical abstract: Unlabelled Image Highlights: A two phase chord length distribution approach to extract the morphology and size information from microstructural SEM images for Ni-based superalloys was established and the image-related robustness of the algorithm was provided. A multi-output support vector regression model was developed to build the quantitative map between service and aging process and microstructural degradation states. A quantitative map of service process-microstructural degradation-property deterioration was constructed based on the TP-CLD and SVR approach.