Nickel-based superalloys such as Inconel 718 (IN718) and AD730 are critical materials in high temperature applications due to their exceptional mechanical properties and corrosion resistance. Optimising their thermo-mechanical processing is essential for enhancing performance and extending service life in demanding environments. This study aims to develop comprehensive predictive models to improve understanding and control over the thermo-mechanical processing of these alloys. This research first examined the effects of quenching configuration and media on residual
stress generation during water quenching of scaled IN718 discs. A novel methodology was created to determine heat transfer coefficients (HTCs), which were integrated into a finite element (FE) model to predict residual stresses. Experimental validation confirmed the model’s accuracy and its effectiveness in predicting and mitigating residual stresses. To facilitate precise mechanical characterisation at elevated temperatures, a new sample
geometry was designed for miniaturised tensile testing using an electro-thermal mechanical testing system. This optimised geometry provides more reliable and accurate measurement of
high-temperature mechanical properties compared to existing designs. Using this optimised geometry, stress relaxation tests were conducted on IN718 and AD730, leading to the formulation of Zener-Wert-Avrami and hyperbolic constitutive stress relaxation models. The hyperbolic model proved particularly effective in predicting stress relaxation behaviour during ageing, enhancing the understanding of material responses under service conditions. The study culminated in the creation of a multi-process FE model to simulate the thermomechanical processing of AD730 during forging and ageing. This model integrated HTC values, stress relaxation data, and advanced constitutive material models. The model successfully predicted the hot deformation behaviour of AD730, with experimental validation confirming accuracy and underscoring potential as a powerful tool for optimising processing parameters
and improving component performance. Overall, this study provides significant advancements in predictive modelling of nickel-based
superalloys, offering insights and methodologies for improving manufacturing processes and material performance in high-temperature applications.
| Date of Award | 19 Sept 2024 |
|---|
| Original language | English |
|---|
| Awarding Institution | - University Of Strathclyde
|
|---|
| Supervisor | Salaheddin Rahimi (Supervisor) & Ioannis Violatos (Supervisor) |
|---|