In this study, the effect of heat treatment parameters on the optimized performance of Ni-rich nickel–titanium wires (NiTi/Nitinol) were investigated that were intended for application as actuators across various industries. In this instance, the maximum recovery strain and actuation angle achievable by a nitinol wire were employed as indicators of optimal performance. Nitinol wires were heat treated at different temperatures, 400–500 °C, and times, 30–120 min, to study the effects of these heat treatment parameters on the actuation performance and properties of the nitinol wires. Assessment covered changes in density, hardness, phase transition temperatures, microstructure, and alloy composition resulting from these heat treatments. DSC analysis revealed a decrease in the austenite transformation temperature, which transitioned from 42.8 °C to 24.39 °C with an increase in heat treatment temperature from 400 °C to 500 °C and was attributed to the formation of Ni₄Ti₃ precipitates. Increasing the heat treatment time led to an increase in the austenite transformation temperature. A negative correlation between the hardness of the heat-treated samples and the heat treatment temperature was found. This trend can be attributed to the formation and growth of Ni₄Ti₃ precipitates, which in turn affect the matrix properties. A novel approach involving image analysis was utilized as a simple yet robust analysis method for measurement of recovery strain for the wires as they underwent actuation. It was found that increasing heat treatment temperature from 400 °C to 500 °C above 30 min raised recovery strain from 0.001 to 0.01, thereby maximizing the shape memory effect.

Thermal cycling is known to adversely affect the performance and lifespan of thermoelectric generators (TEGs) yet has received limited attention to date. The current study experimentally investigates the effect of thermal cycling on the performance of twelve nominally identical TEG modules. Six samples were subjected to the same thermal cycle profile with an average heating time of 154 s to examine the variation in their outputs. The maximum cycling temperature was varied between 170 °C and 190 °C for a further six samples to investigate the effect of maximum set-point temperature on performance. Degradation in performance was exhibited by all modules, with maximum power outputs between 28% and 57% of pre-cycling values and a decrease in the dimensionless figure of merit $$ZT$$ZT of 21% to 56% upon cessation of cycling. Sudden ‘breakdowns’ or significant reductions in output power were observed for all TEGs, accompanied by increased electrical resistance, which is indicative of internal damage to the modules arising from the formation of micro-cracks at the interface between the semiconductor thermocouples and electrically conductive material. The rate of degradation post-‘breakdown’ appeared to be influenced by the maximum set-point temperature, with more rapid decreases observed for increasing temperatures.

This research examines the use of an unsupervised and semi-supervised machine learning approach to facilitate the extraction and analysis of process monitoring data during the printing of Ti-6Al-4 V alloy parts in real time. The detection of processing anomalies is achieved by analysing in situ photodiode sensor data gathered from a combination of laser power output data, along with photodiode data obtained from the laser-powder bed fusion (L-PBF) laser melt pool emissions. The machine learning techniques evaluated are the unsupervised Search and TRace AnomalY (STRAY) algorithm, along with the semi-supervised 1-dimensional autoencoder (1-DAE) technique. The latter approach is considered semi-supervised, as it requires an unlabelled training dataset for the model to learn inherent latent features of the data. To this end, the techniques were assessed on their ability to detect defects intentionally induced during the printing of lattice structures with a range of unit cell shapes and sizes. Based on the analysis of the print datasets, it was demonstrated that the 1-DAE achieved a maximum predictive accuracy (F1 score) of 0.94 and a minimum score of 0.80. Comparatively, the STRAY algorithm had a minimum F1 score of 0.09 and a maximum score of 0.92 when applied to the same datasets. While 1-DAE requires an initial training dataset before it can be applied, its implementation time post-training is only 1.92 ms. This speed along with its reliability makes it a potential candidate for the real-time detection of L-PBF processing anomalies.

The evolution of laser powder bed fusion (L-PBF) has provided increased design flexibility in the fabrication of a range of parts, including aerospace components and medical devices. The presence of overhangs in metal alloy print structures, however, can give rise to enhanced levels of print defects such as porosity. This is associated with overheating of the alloy meltpool in the region around the overhang structures. This study demonstrated the effectiveness of in-situ meltpool process monitoring as a route to assist laser parameter optimisation for printed Ti-6Al-4V alloy parts. Informed by the observations from the process monitoring of the laser meltpool, the laser parameters are controlled to prevent overheating in the area around the overhang, yielding a more homogeneous printed part's microstructure. In addition to microstructure optimisation, the enhanced control of the L-PBF process yielded an 88% reduction in overhang roughness (Ra) and a 75% reduction in porosity.

Geometrically complex features, such as overhang structures, can be a challenge in laser powder bed fusion (L-PBF), as they can be associated with print defects, such as porosity. In this study, Ti-6Al-4V alloy overhang structures were fabricated using an L-PBF system. Differences were observed in the microstructure in the region around the overhang structures, compared with that observed for the bulk alloy. These included larger grain sizes and a less homogenous microstructure in the print layers closest to overhang structures. It is hypothesized that these microstructural changes are associated with the excess heat generated in the overhang region due to the decreased thermal conductivity of the powder immediately below the print layers, compared with solid alloy. Also observed was an increased level of porosity (up to 0.08%) in the overhang print alloy, compared with the corresponding <0.02% in the alloy bulk. During printing, in-situ process monitoring of the meltpool emissions was obtained in the near-infrared range and correlated with the properties of the printed parts. This in-process data assisted in selecting optimal laser processing conditions in the first fifteen layers above the overhang to prevent meltpool overheating. By systematically controlling the laser energy during the printing of L-PBF overhang structures, the level of porosity was reduced to match that of the bulk alloy. There was also an associated reduction in the roughness of the overhang itself, with its Ra decreasing from 62.4 ± 7.3 µm to 7.5 ± 1.9 µm.