Modelling and Validation of Gamma Prime (g') Evolution In An Advanced Polycrystalline Nickel-Base Superalloy

The modelling software, PrecipiCalc has been used to predict the evolving g' distributions in the nickel-base super-alloy RR1000, during heat treatments. Microstructural observations by SEM and TEM show good correlation to the modelling results. However, observations are limited to the start and end conditions of a thermal exposure. The use of in situ techniques utilising SANS (small-angle neutron diffraction) and high resolution powder diffraction show great promise for providing temporally resolved data for the validation of microstructural models, particularly in complex materials with multimodal precipitation distributions. Experiments have been carried out using these methods to monitor the coarsening behaviour of g' when exposed to an aging temperature of 760°C. From a starting bimodal g' distribution, achieved by solution heat treating the material at 1180°C and subsequently oil quenching the samples, the secondary and tertiary g' precipitates could be observed within the scattering vector, Q, range of the SANS instrument. The SANS experiment enabled the mean particle radius, volume fraction and thereby PSDs to be calculated throughout the aging experiment. In a second experiment, powder diffraction data was gathered, in situ, allowing precipitate size measurements to be obtained by peak broadening analysis. Misfit as function of aging time was also derived. The PSDs measured by both techniques complemented each other well, showing satisfactory agreement throughout. In addition to this crucial validatory data, the scattering profiles from SANS and the misfit evolution from X-ray diffraction indicated strong changes in composition of the tertiary g', providing insights to aid the future modelling of more complex heat treatments. The results obtained from both experiments were shown to be in satisfactory agreement with the PSDs predicted using PrecipiCalc, demonstrating the capability for the experimental validation of such models.