The current research work investigates the possibility of using machine learning models to deduce the relationship between WAAM (wire arc additive manufacturing) sensor responses and defect presence in the printed part. The work specifically focuses on three materials from the nickel alloy family (Inconel 718, Invar 36 and Inconel 625) and uses three sensor responses for data analysis, which are welding voltage, welding current and welding audio. Two different machine learning models are used—artificial neural networks (ANNs) and random forests (RF). The results for each of the materials, separately, indicate that the accuracies range from 60% to 90% and the correlation coefficient is less than 0.5 (indicating weak positive correlation), depending on the model and material. In addition to separate material analysis, a cross-material data analysis was formed to test the models’ general prediction capabilities. This led to predictions that are significantly worse, with accuracies ranging from 20% to 27% and very weak correlation coefficients (less than 0.1), indicating that the choice of material is still important as a boundary condition. Analysis of the results indicates that the relative importance of audio sensor response depends on the nature of defect formation. Random forests are found to perform the best for single material analysis, with the comparatively inferior performance of ANNs possibly being due to lack of sufficient datapoints.@en

Stellite 6 is a cobalt-based superalloy which has a good wear and corrosion resistance and retains these properties at high temperatures. In this study, wire and arc additive manufacturing (WAAM based on the GMAW) was employed to deposit Stellite 6 wire on low alloy high strength steel (S355) and stainless steel (AISI 420) plates. One of the main interests of this study is to produce WAAM Stellite 6 deposits with quality comparable with laser deposition. The advantages of the WAAM process include the high deposition rate, high productivity, high material usage, and energy efficiency with low cost. However, superalloy deposition generally requires maintaining a low dilution level to avoid jeopardizing the integrity of the deposit. As a result, it is important to manage the excess heat input during the WAAM deposition process through a parametric optimization of WAAM deposition of Stellite 6 on S355 steel substrates. In this study, a WAAM process window is established to guide the process optimization. The optimization method used in this study has been applied in our previous laser cladding work. The generated process window also shows some correlations among the heat input, bead geometry, and dilution. The effects of heat input on the resulting microstructure, elemental distribution, and hardness were discussed. Dilution, microstructure, and hardness were considered for comparison from previous studies of laser cladding deposits. In addition, the obtained optimal conditions were adapted to apply WAAM deposition of Stellite 6 layers on AISI 420 stainless steel substrates. The XRD result shows that WAAM deposits contain Co-Cr-Fe solid solution (FCC), Cr₇C₃ and Cr₃C₂ carbides, and Co₄W₂C intermetallic compound. The results obtained through this study lay a foundation for future research on the wear properties of WAAM Stellite 6 deposits. This can contribute to further development of automated deposition of Stellite 6 using WAAM process for industrial applications.

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Wire and arc additive manufacturing (WAAM) is a 3D metal printing technique based on the arc welding process. WAAM is considered to be suitable to produce large-scale metallic components by combining high deposition rate and low cost. WAAM uses conventional welding consumable wires as feedstock. In some applications of steel components, one-off spare parts need to be made on demand from steel grades that do not exist as commercial welding wire. In this research, a specifically produced medium carbon steel (Grade XC-45), metal-cored wire, equivalent to a composition of XC-45 forged material, was deposited with WAAM to produce a thin wall. The specific composition was chosen because it is of particular interest for the on-demand production of heavily loaded aerospace components. The microstructure, hardness, and tensile strength of the deposited part were studied. Fractography studies were conducted on the tested specimens. Due to the multiple thermal cycles during the building process, local variations in microstructural features were evident. Nevertheless, the hardness of the part was relatively uniform from the top to the bottom of the construct. The mean yield/ultimate tensile strength was 620 MPa/817 MPa in the horizontal (deposition) direction and 580 MPa/615 MPa in the vertical (build) direction, respectively. The elongation in both directions showed a significant difference, i.e., 6.4% in the horizontal direction and 11% in the vertical direction. Finally, from the dimple-like structures observed in the fractography study, a ductile fracture mode was determined. Furthermore, a comparison of mechanical properties between WAAM and traditionally processed XC-45, such as casting, forging, and cold rolling was conducted. The results show a more uniform hardness distribution and higher tensile strength of the WAAM deposit using the designed metal-cored wires.@en