This study aimed to establish whether useful fatigue design stress-life curves could be estimated for additively manufactured metals through statistical and machine learning analysis of a large quantity of experimental fatigue data. The study focused on additively manufactured aluminium, steel and titanium. Three manufacturing parameters were considered, namely the manufacturing direction, heat-treatment and surface operations, with the results presented for 0.1 and −1 loading ratios. By gathering experimental data for all parameters, the negative inverse slopes were found to be concentrated between 3 and 6, and the mean endurance limit as a ratio to ultimate tensile strength was 0.18 and 0.21 for 0.1 and −1 loading ratios, respectively, without any statistical analysis. Surface operations were observed to have a significant effect on the fatigue strength of additively manufactured aluminium, steel and titanium regardless of other manufacturing parameters. Multiple linear regression analysis and several machine learning methods (Decision Tree, Support Vector Machines, K-Nearest Neighbour, Multi-Layer Perceptron, Partial Least Squares and Gaussian Process Regression) were used to develop predictive models. The results of these analyses highlight that the conventional approach applied to fatigue of traditional metals does not suffice for additively manufactured metals. While artificial intelligence presents a promising solution, our investigation indicates it is necessary to account for parameters in addition to those considered here such as manufacturing processes, material properties, material microstructure and defects to make reliable fatigue property estimates for additively manufactured metals using machine learning.

Reducing and preventing leakage is a priority for water distribution network managers in many countries, including the UK. Understanding the mechanisms that cause leaks to form, and developing the ability to model these processes, will enable proactive replacement of water pipes before they start to leak. Smaller diameter Grey Cast Iron (GCI) water pipes are understood to experience biaxial, repeating loads, so fatigue cracking may be a cause of leakage for these pipes. To investigate this fatigue cracking mechanism a small-scale biaxial fatigue experiment is under development at The University of Sheffield. A large number of small diameter, un-corroded GCI pipes are needed to serve as specimens for this experiment. Therefore, in this work off-the-shelf BS416-2 DN 50 mm soil pipes are explored as an alternative to using exhumed pipes, which are often corroded and ≥76.2 mm diameter. The graphite microstructure and tensile stress-strain behaviour of a BS416-2 pipe were characterised and compared with literature data for exhumed spun-cast GCI water pipes, and a good agreement was found. This work concluded that BS 416-2 soil pipes can be used to represent spun-cast GCI water pipes in small-scale destructive experiments.

Controlled laboratory-based physical evidence is presented, showing how the type of fatigue loading impacts the remaining life and the failure mode of corroded GCI water pipes. Leak-before-burst behavior is shown for pipes experiencing internal water pressure fatigue loading but not for four-point bending fatigue. Sharp pits are shown to reduce fatigue strength by up to 5.4 times, with the degree of reduction dependent on alignment. Condition assessments of corroded GCI water pipes must consider both the three-dimensional shape of the corrosion pitting and the loading experienced by the pipe to give a true assessment of the damage caused by a corrosion pit.

This research explored the hypothesis that different corrosion pit and multiaxial stress alignments cause a significant change in the fatigue strength and leakage behaviour of grey cast iron pipes. Destructive laboratory experiments were used to characterise the fatigue strength and failure mechanisms, including leak-to-burst behaviour. The experiments subjected the pipes to cyclic internal pressure and/or bending loads to cause stresses acting in different directions for a range of artificial pit shapes, including long, narrow pits for the first time. These unique experiments revealed that small cracks in cast iron pipes can close and stop leaking even under high positive water pressures, which has significant implications for leak modelling and detection. Small corrosion pits with stress alignments that caused a high stress concentration were shown to extend the leak-to-burst interval by about two orders of magnitude due to stress concentration effects of the pit. Such pits will progress to catastrophic burst behaviour more slowly than from small initial cracks under uniform wall loss or lower stress concentration pits. From a leakage onset perspective, the fatigue strength reduction caused by long, narrow corrosion pits was found to change by about 60 % depending on the applied stress direction, showing the importance of considering pit-stress alignment in damage assessment and modelling. This research has found that corrosion pit shape, and the relative alignment of the pipe wall stresses, can substantially affect the ability of cast iron pipes to withstand cracking, as well as affecting the stability of leaking cracks once formed. These findings can be utilised to improve cast iron pipe condition assessment and leak detection techniques.