Plasma FIB milling for the determination of structures in situ

This thesis investigates wear mechanisms using nanoindentation and nanoscratch techniques. Nanoindentation measures hardness and elastic modulus at the nanoscale, while nanoscratch testing simulates sliding contact and material removal. Atomic Force Microscopy (AFM) is used to study how pile-up formation changes with normal load and how wear tracks develop with repeated scratches, enhancing the understanding of surface structure and abrasion processes. The research focuses on the early stages of wear in nickel-based alloys, which are used in high-temperature turbines and corrosive environments due to their strength and corrosion resistance. Nanoscratch testing and AFM, which mimic single-asperity contact, explore the factors influencing the scratch formation and the relationship between material properties and scratch resistance. Indentation and scratch hardness tests were performed on three nickel-based alloys (Ni201, Ni600, and Ni718) with different strengths and compositions. The results show a correlation between hardness profiles and macroscopic yield strength, with scratch hardness exceeding indentation hardness. The orientation of the Berkovich tip affects scratch strength and appearance: tips facing the surface produce higher hardness measurements with wavy grooves, while edge positioning results in straight scratches. A novel aspect of this study is the AFM analysis of nanoscratch shape changes under increased normal forces, providing insights into material deformation. Multi-pass nanoscratch tests on a single Ni600 grain showed stabilisation in penetration depth with repeated passes. Focused Ion Beam (FIB) and High-Resolution Electron Backscatter Diffraction (HR-EBSD) analyses identified stress and rotation distributions in the plastic zone, supporting theories of stabilisation. To connect microscale data with macroscale properties, a machine learning model was developed to predict Johnson-Cook constants based on nanoindentation results. These parameters were used in Abaqus finite element simulations of nanoscratch experiments. Sensitivity analyses examined the impact of factors such as yield stress on indentation outcomes. Simulations matched experimental data in initial passes but deviated in later passes, prompting further investigation. This research enhances the understanding of nanoscale wear in nickel-based alloys by linking nanoscratch behaviour with macroscale properties through machine learning. Key contributions include explaining penetration depth variations in multi-pass nanoscratch tests and providing detailed AFM characterisation of nanoscratch morphology. The predictive model offers the potential for optimising wear-resistant nickel-based alloys for industrial applications