The adoption of wire and arc additive manufacturing (WAAM) in the market has been retained by the need to find a suitable method to ensure the quality of the parts produced. WAAM processes build up parts through the deposition of weld beads, consequently components with rough finish surfaces are characteristics of the method. Non-destructive testing (NDT) by ultrasonic (UT) method, namely the phased array technique (PAUT), is usually used to detect these defects in welding. However, the roughness of the parts represents a challenge for the UT application, since these variations influence the interaction between the emitted UT beam and the component. This study is thus focused on assessing the capability of detecting WAAM defects. The effectiveness of phased array ultrasonic testing to detect defects in aluminium WAAM components with several degrees of surface finish was evaluated. Simulations were first performed with CIVA software to characterize the beam emitted and select the probes and inspection parameters. Afterwards, physical inspections were performed on three reference specimens. Experimental outcomes prove that PAUT techniques are suitable for WAAM defects detection, including sizing, morphology and location. In addition, the experimental results were consistent with the simulated ones. The probes were able to overcome the limitations caused by the surface roughness of the samples, for a maximum of 89.6 μm average waviness profile. Also, defects ranging from 2 to 5 mm were characterized, in size and depth. These preliminary results represent an essential step for the development of an NDT system for inspecting WAAM parts.
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Research financed by Fundação para a Ciência e a Tecnologia (FCT-MCTES) under the PhD Research Grant PD/BD/114153/2016. This work was supported by FCT-MCTES, through IDMEC, under LAETA, Project UID/EMS/50022/2013 TGS knowledge FCT-MCTES for its financial support via the Project UID/EMS/00667/2019. The authors would also like to acknowledge the opportunity of manufacture the samples at Cranfield University.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 723600 - LASIMM Project.
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