Application of computational thermodynamic and kinetic models to laser surface alloying

Surface physical and mechanical properties can be tailored to different needs by the laser surface alloying process. These property modifications are made by localized melting of substrate material by laser beams with addition of alloying elements as well as hard particles. Through selection of proper hard particles, modification of underlying microstructure through rapid cooling and controlled alloying addition, one can tailor these surfaces for required properties. However, these processes are developed through extensive experimental trial-anderror methodology. This research shows an alternate design methodology by which these processes can be designed through computational thermodynamic and kinetic models

Thermomechanical testing of nickel base superalloy single crystals

Thermomechanical response of single crystal superalloys during weld heating and cooling will affect cracking behavior in the heat-affected-zone (HAZ) and weld-metal (WM) regions. Thermomechanical response of these superalloys will depend on the phase fractions of ordered am ma-prime precipitates in the am ma matrix. During weld heating, the am ma-prime precipitates will dissolve as they approach the solvus temperature and some liquation may occur in the interdendritic regions. On cooling, the single-phase gamma matrix will decompose to a mixture of am ma and am ma-prime phases. In this work we evaluated the strength, ductility and reduction in area as a function of temperature during on heating and on cooling conditions to evaluate weldability

Thermomechanical behavior of nickel base single crystal superalloy towards understanding of weld hot cracking

Thermomechanical responses of three single crystal superalloys were measured in between 600 and 1200 degrees C using a thermomechanical simulator. On-heating results showed no softening while testing below 1000 degrees C. In contrast, extensive softening was observed while testing above 1000 degrees C. This rapid softening is related to dissolution of gamma\u27 precipitates. During on-cooling tests from 1300 degrees C, strength recovery of these samples occurred only below 900 degrees C. The delay in strength recovery is attributed to the extent of undercooling required for rapid decomposition of the gamma phase into mixture of gamma and gamma\u27 phases

Bayesian neural network analysis of ferrite number in stainless steel welds

Bayesian neural network (BNN) analysis has been used in the present work to develop an accurate model for predicting the ferrite content in stainless steel welds. The analysis reveals the influence of compositional variations on ferrite content for the stainless steel weld metals, and examines the significance of individual elements, in terms of their influence on ferrite content in stainless steel welds, based on the optimised neural network model. This neural network model for ferrite prediction in stainless steel welds has been developed using the database used to generate the WRC-1992 diagram and the first author's laboratory data. The optimised committee model predicts the ferrite number (FN) in stainless steel welds with greater accuracy than the constitution diagrams and the other FN prediction methods. Using this BNN model, the influence of variations of the individual elements on the FN in austenitic stainless steel welds is also determined, and it is found that the change in FN is a non-linear function of the variation in the concentration of the elements. Elements such as Cr, Ni, N, Mo, Si, Ti, and V are found to influence the FN more significantly than the other elements present in stainless steel welds. Manganese is found to have a weaker influence on the FN. A noteworthy observation is that Ti influences the FN more significantly than does Nb, whereas the WRC-1992 diagram considers only the Nb content in calculating the Cr equivalent