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Nickel–titanium alloys are the most widely used shape memory alloys due to their outstanding shape memory effect and superelasticity. Additive manufacturing has recently emerged in the fabrication of shape memory alloy but despite substantial advances in powder-based techniques, less attention has been focused on wire-based additive manufacturing. This work reports on the preliminary results for the process-related microstructural and phase transformation changes of Ni-rich nickel–titanium alloy additively manufactured by wire-based electron beam freeform fabrication. To study the feasibility of the process, a simple 10-layer stack structure was successfully built and characterized, exhibiting columnar grains and achieving one-step reversible martensitic–austenitic transformation, thus showing the potential of this additive manufacturing technique for processing shape memory alloys.

The Refill Friction Stir Spot Welding is an innovative spot like solid state process befitting of overlap joint configurations of similar and dissimilar materials. This process caught the interest and is rapidly growing in the aerospace sector due to its potential to substitute traditional mechanical fasteners, surpassing their mechanical performance while maintaining the so desired lightweight “rationale.” In the current study, process parameters, namely plunge depth, plunge time and rotational speed, are optimized in order to obtain the highest Ultimate Lap Shear Force (ULSF) of 2024-T3 Aluminum Alloy similar joints. The optimization campaign was carried out using a second order multivariate polynomial regression machine learning (ML) algorithm. The trained ML model was able to generalize and accurately predict the Ultimate Lap Shear Force on the holdout set, having a R2 of 88.0%. Moreover, the model suggested an optimum parameter combination (Rotational Speed = 2,310 rpm, Welding Time = 5.3 s and Plunge Depth = 2.6 mm) from which the predicted maximum ULSF was computed. Confirmation tests were carried out to evaluate the agreement between the predicted and the experimental values.

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