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.

The reflection of the laser energy by the powder makes the laser energy no longer exhibit a Gaussian distribution. The purpose of this paper is to investigate the effect of the heat source model on the behavior of the molten pool in laser cladding. First, the Gaussian heat source is modified according to the law of laser energy reflection by powder, and a laser heat source model considering powder reflection is established. Then, the AM900 superalloy laser cladding model was established by using the fluid dynamics method with the modified Gaussian heat source and ordinary Gaussian heat source, respectively. The influence of the heat source model on the behavior of the laser cladding pool was studied. The thermal cycle curve was collected and the cladding layer morphology was obtained in the actual cladding process to verify the model. It is found that the impact of the droplet has the greatest impact on the flow pattern of the molten pool. The modified Gaussian heat source can reduce the peak temperature in the center of the molten pool (about 130 K), which can reflect the actual laser beam during cladding. The flow velocity of the molten metal simulated with the modified Gaussian heat source is slower than that of the ordinary Gaussian heat source, resulting in smaller melt pool width and penetration depth (reduced to 0.27 and 0.12, respectively). The improved heat source model is more in line with the actual cladding process.

Corner cracks are one of the common forms of flaws in the reactor pressure vessels (RPVs) of nuclear power plants (NPPs). Accurate evaluation of the driving force of such corner cracks is essential and critical for the efficient design and integrity assessment of the RPVs. In this study, the combined effect of the depth, length and angle of a given corner crack under the service loading was analyzed using elastic–plastic finite element analysis (EPFEA). Based on the consideration of the distribution profiles of stress intensity factor (SIF) and J integral as the crack growth along corner crack front, the crack growth stability with various crack shapes and angles was analyzed. Finite element analysis (FEA) results showed that the crack growth driving force is affected by the shape and angle of the initial crack. It is also found that the sliding mode (mode II) crack or tearing mode (mode III) crack should be taken into account in the structural integrity analysis of RPVs.

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.

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.