Compositional Optimisation of a Ni-based Superalloy for Additive Repair Applications

Abstract

The use of additive manufacturing (AM) for the fabrication of Ni-based alloys has seen a massive uptake in commercial institutions. Among their many merits, AM techniques offer a unique route for the repair of a wide range of complex components. However, processing of the Ni-based superalloys used by the aerospace industry through AM has encountered numerous issues. Certain complications can be resolved through intense post-processing. Although, such mitigation strategies are not appropriate for repaired material as intense heat-treatments might deteriorate substrate properties. Therefore, new methods are required to control and optimise the deposition of Ni-based superalloys. The commercial superalloy IN718 is currently used for certain AM repair applications. However, due to the limitations on the post-processing that may be tolerated, the complications of an irregular microstructure, an uncontrollable texture and severe elemental segregation remain unresolved for this system. In this thesis, the laser-blown-powder directed-energy-deposition (LBP-DED) of IN718 is characterised and novel methods for compositionally optimising the deposition of this system are investigated. It is shown that a Brass texture ({110} <211>) can be obtained in LBP-DED IN718. This texture is observed to be enhanced through recrystallisation following selected heat treatments. The evolution of elastic properties from the as-deposited state were studied as a function of heat treatment time and duration using Resonant Ultrasound Spectroscopy (RUS). To control the crystallographic texture formed, the addition of niobium carbide (NbC) inoculant particles to the precursor powder was investigated. It was observed that the inoculants enhanced the formation of the Brass texture component, as well as leading to the occurrence of alternating regions possessing mirror symmetry. The addition of the inoculant therefore offers a method of achieving a degree of microstructural and textural control during LBP-DED of IN718 [3]. In the final chapter, a neural network framework is used to design a new Ni-based superalloy that surpasses the performance of IN718 for LBP-DED repair applications. The compositional design space was based on IN718, although, W was additionally included, and elemental limits were modified allowing the alloy to approach the composition of ATI 718Plus®. The newly designed alloy was fabricated, and the properties were experimentally investigated. The testing confirms that this alloy offers advantages for additive repair applications over standard IN718.