The main role of Rare Earth (RE) elements in the steelmaking industry is to affect the nature of inclusions (composition, geometry, size and volume fraction), which can potentially lead to the improvement of some mechanical properties such as the toughness in steels. In this study, different amounts of RE were added to a niobium microalloyed steel in as-cast condition to investigate its influence on: (i) type of inclusions and (ii) precipitation of niobium carbides. The characterization of the microstructure by optical, scanning and transmission electron microscopy shows that: (1) the addition of RE elements change the inclusion formation route during solidification; RE > 200 ppm promote formation of complex inclusions with a (La,Ce)(S,O) matrix instead of Al₂O₃-MnS inclusions; (2) the roundness of inclusions increases with RE, whereas more than 200 ppm addition would increase the area fraction and size of the inclusions; (3) it was found that the presence of MnS in the base and low RE-added steel provide nucleation sites for the precipitation of coarse niobium carbides and/or carbonitrides at the matrix–MnS interface. Thermodynamic calculations show that temperatures of the order of 1200 °C would be necessary to dissolve these coarse Nb-rich carbides so as to reprecipitate them as nanoparticles in the matrix.

In this paper the effect of tool geometry and welding parameters on friction stir welded lap joints with AA2099-T83 and AA2060-T8E30 aluminium alloys has been investigated through the study of the material flow and weld formation along with the reaction forces during friction stir welding (FSW) for various sets of welding parameters and two FSW tools with different geometrical features. The results showed that welding parameters and tool probe geometry strongly affect the characteristics of the typical defect features (hook and cold lap defects) of the friction stir welded lap joints. From the relationship established between the welding parameters, tool probe geometry and the hook and cold lap defect formation, some guidelines are concluded with the objective of guaranteeing appropriate FSW lap joint properties.

The development of thermomechanical treatments (TMT) has a high potential for improving creep-strength in 9Cr-1Mo ferritic/martensitic steel (ASTM T/P91) to operate at temperatures beyond 600 °C. To maximize the number of nanoscale MX precipitates, an ausforming procedure has been used to increase the number of nucleation sites for precipitation inside the martensite lath. Relative to standard heat treatments (consisting of austenitization at about 1040 °C followed by tempering at about 730 °C) this processing concept has enabled achieving a microstructure containing approximately three orders of magnitude higher number density of MX precipitates having a size around four times smaller in ASTM T/P91 steel. On the other hand; this TMT has little effect on the size and number density of M₂₃C₆ particles. The optimized microstructure produced by this TMT route proposed is expected to improve the creep strength of this steel.

Flow-related defects in friction stir welding are critical for the joints affecting their mechanical properties and functionality. One way to identify them, avoiding long and sometimes expensive destructive and nondestructive testing, is using machine learning tools with monitored physical quantities as input data. In this work, artificial neural network and decision tree models are trained, validated, and tested on a large dataset consisting of forces, torque, and temperature in the stirred zone measured when friction stir welding three aluminum alloys such as 5083-H111, 6082-T6, and 7075-T6. The built models successfully classified welds between sound and defective with accuracies over 95%, proving their usefulness in identifying defects on new datasets. Independently from the models, the temperature in the stirred zone is found to be the most influential parameter for the assessment of friction stir weld quality.