Purpose: The purpose of this paper is to develop a simple analytical model for predicting the through-thickness distribution of residual stresses in a cold spray (CS) deposit-substrate assembly.Design/methodology/approach: Layer-by-layer build-up of residual stresses induced by both the peening dominant and thermal mismatch dominant CS processes, taking into account the force and moment equilibrium requirements. The proposed model has been validated with the neutron diffraction measurements, taken from the published literature for different combinations of deposit-substrate assemblies comprising Cu, Mg, Ti, Al and Al alloys.Findings: Through a parametric study, the influence of geometrical variables (number of layers, substrate height and individual layer height) on the through-thickness residual stress distribution and magnitude are elucidated. Both the number of deposited layers and substrate height affect residual stress magnitude, whereas the individual layer height has little effect. A good agreement has been achieved between the experimentally measured stress distributions and predictions by the proposed model.Originality/value: The proposed model provides a more thorough explanation of residual stress development mechanisms by the CS process along with mathematical representation. Comparing to existing analytical and finite element methods, it provides a quicker estimation of the residual stress distribution and magnitude. This paper provides comparisons and contrast of the two different residual stress mechanisms: the peening dominant and the thermal mismatch dominant. The proposed model allows parametric studies of geometric variables, and can potentially contribute to CS process optimisation aiming at residual stress control.

This study aims to demonstrate the capability of the digital image correlation (DIC) technique for evaluating full-field residual stresses in wire and arc additive manufactured (WAAM) components. Investigations were carried out on WAAM steel parts (wall deposited on a substrate) with two different wall heights: 24 mm and 48 mm. Mild steel solid wire AWS ER70S-6 was used to print WAAM walls on substrates that were rigidly clamped to H-profiles. DIC was used to monitor the bending deformation of WAAM parts during unclamping from the H-profiles, and residual stresses were calculated from the strain field captured during unclamping. Residual stresses determined from the proposed DIC-based method were verified with an analytical model and validated by the results from established residual stress measurement techniques, i.e., the contour method and X-ray diffraction.

This study aims at investigating the effect of various post-deposition thermal treatments on improving tensile properties of cold spray (CS) deposited titanium alloy Ti6Al4V. Dogbone-shaped tensile specimens were designed considering two application scenarios: ‘fully CS’ specimens, and ‘CS repair’ specimens. For both specimen types, tests were carried out in four conditions: (i) as-deposited (AD), and after three different thermal treatments, i.e., (ii) solution treatment and ageing (STA), (iii) hot isostatic pressing (HIP), and (iv) HIP followed by STA (HIP + STA). Complementary to tensile testing, characterisation of CS deposited material was also carried out in terms of microstructure and hardness. The STA process resulted in the highest improvement in ultimate tensile strength by more than 200%, reaching 868 MPa for ‘fully CS’ and 951 MPa for ‘CS repair’ specimens. However, no appreciable improvement in elongation at failure was achieved, highest being 1.2% for ‘fully CS’ after STA, and 4.3% for ‘CS repair’ after HIP. In addition to experimental investigation, a comprehensive collection of data from the open literature on the effect of various thermal treatments on improving the tensile properties of CS Ti6Al4V deposits is reported and discussed.

Porosity poses a challenge to the mechanical properties of cold sprayed coatings, especially when it is open or surface-connected, limiting the coatings’ capabilities to act as a barrier. The porosity formation is dependent on the feedstock powder characteristics and the cold spray process parameters. We present a machine learning-based approach to predict porosity based on the above-mentioned factors. Nine different machine learning models based on linear regression (LR), decision trees, random forests, gradient boosting, support vector machine (SVM), and neural networks were explored. Considering the excellent properties of high entropy alloys, Cantor alloy was taken as the consumable. Our dataset, derived from the literature and experiments, identified SVM with a linear kernel and LR as the top-performing models based on the Pearson correlation coefficient (PCC) and root mean square error, where the PCC values exceeded 0.8. The SHapley Additive exPlanations method helped in identifying that the type of gas and powder are the top two factors in pore formation.