Evaluating the condition of a Hadfield steel crossing nose using existing inspection methods is subject to accessibility and geographical constraints. Thus, the use of conditional monitoring techniques to complement the existing inspection methods has become increasingly necessary. This paper focuses on the study of acoustic emission (AE) behaviour and its correlation with fatigue crack growth in Hadfield steel during bending fatigue tests. The probability density function for acoustic emission parameters was analysed based on the power law distribution. The results show that a sharp increase in the moving average and cumulative sum of the AE parameter can give early warning against the final failure of Hadfield steel. Two parts (Part 1 and Part 2) can be identified using the change in the slope of duration rate (dD/dN) vs. ΔK plot during the stable fatigue crack growth (FCG) process where Paris’s law is valid. The fitted power law exponent of AE parameters is smaller in Part 2 than in Part 1. The novelty of this research lies in the use of the fitted power law distribution of AE parameters for monitoring fatigue damage evolution in Hadfield steel, unlike existing AE fatigue monitoring methodology, which relies solely on the analysis of AE parameter trends.

Wire and arc additive manufacturing (WAAM) offers promise in creating large complex structures due to its flexibility and high material deposition rates. The nickel-based alloy IN718 is favoured for WAAM due to its weldability and compatibility. However, WAAM can introduce issues like anisotropic grain structure, porosity, and residual stresses which can lead to directional variations in tensile, fatigue, and fracture behaviour. This paper studied the WAAM process of IN718, utilising cold metal transfer (CMT). The optimised CMT-WAAM parameters for IN718 were identified to as a wire feed speed of 8–10 m/min and a torch travel speed of 0.5–0.7 m/min, resulting in stable deposition and minimal defects. Nevertheless, columnar grain structures were observed in the build direction (BD), with coarse grains in the wall-length direction (WD). This anisotropic microstructure coupled with stress concentrators, contributes to the directional dependence observed in tensile properties, fatigue endurance, and crack growth. The investigation revealed superior ductility in the BD compared to the WD. Interestingly, the fatigue endurance testing showed a longer life in the WD compared with the BD, attributed to stronger stress concentrators in the BD specimens. However, when examining a cracked specimen, the fatigue crack propagated faster in the WD rather than the BD.

In this study, a mock-up of a nuclear safe-end dissimilar metal weld (DMW) joint (SA508-3/316L) was manufactured. The manufacturing process involved cladding and buttering of the ferritic steel tube (SA508-3). It was then subjected to a stress relief heat treatment before being girth welded together with the stainless steel tube (316L). The finished mock-up was subsequently machined to its final dimension. The weld residual stresses were thoroughly characterised using neutron diffraction and the contour method. A detailed finite element (FE) modelling exercise was also carried out for the prediction of the weld residual stresses resulting from the manufacturing processes of the DMW joint. Both the experimental and numerical results showed high levels of tensile residual stresses predominantly in the hoop direction of the weld joint in its final machined condition, tending towards the OD surface. The maximum hoop residual stress determined by the contour method was 500 MPa, which compared very well with the FE prediction of 467.7 Mpa. Along the neutron scan line at the OD subsurface across the weld joint, both the contour method and the FE modelling gave maximum hoop residual stress near the weld fusion line on the 316L side at 388.2 and 453.2 Mpa respectively, whereas the neutron diffraction measured a similar value of 480.6 Mpa in the buttering zone near the SA508-3 side. The results of this research thus demonstrated the reasonable consistency of the three techniques employed in revealing the level and distribution of the residual stresses in the DMW joint for nuclear applications.

Structural health monitoring (SHM) of fatigue cracks is essential for ensuring the safe operation of engineering equipment. The acoustic emission (AE) technique is one of the SHM techniques that is capable of monitoring fatigue-crack growth (FCG) in real time. In this study, fatigue-damage evolution of Hadfield steel was characterized using acoustic emission (AE) and machine learning-based methods. The AE signals generated from the entire fatigue-load process were acquired and correlated with fatigue-damage evolution. The AE-source mechanisms were discussed based on waveform characteristics and bispectrum analysis. Moreover, multiple machine learning algorithms were used to classify fatigue sub-stages, and the results show the effectiveness of classification of fatigue sub-stages using machine learning algorithms. The novelty of this research lies in the use of machine learning algorithms for the classification of fatigue sub-stages, unlike the existing methodology, which requires prior knowledge of AE-loading history and calculation of ∆K.

Microstructural evolution of a nickel-base single-crystal superalloy during homogenisation at ultra-high temperature has been investigated using scanning electron microscope and electron probe microanalyzer. The results show that incipient melting occurs and all γ/γ′ eutectics are re-melted into liquid after 1 h homogenisation at 1350°C. The volume fraction of IM decreases gradually with increasing homogenisation time. The IM disappears totally after 14 h homogenisation. Moreover, the formation of the TCP phase is suppressed during long-term thermal exposure at 1100°C for the reduction of element segregation, especially the segregation of refractory element Re was reduced during ultra-high-temperature homogenisation. The microstructure evolution of IM was discussed based on the element diffusion during homogenisation.