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.

High-strength, low-alloy (HSLA) steel has attracted much attention in the manufacturing industry because of its good combination of high strength and toughness, low cost, and good formability. Wire and arc additive manufacturing (WAAM) technology can realize the rapid prototyping of HSLA steel parts. This study investigated a 26-layer HSLA steel component fabricated with the WAAM technique. The microstructure of the deposited wall of ER120S-G is mainly acicular ferrite, and there are longitudinal, preferentially growing dendrites along the deposition direction. With the deposition height accumulation, the top sample’s interlayer temperature increases and the amount of acicular ferrite in the microstructure decreases, while the amount of quasi-polygonal ferrite, Widmanstatten ferrite increases. The changes in microhardness were consistent with the corresponding microstructure gradients: the microhardness of the top sample showed a decreasing trend along the deposition direction, while the microhardness of the middle sample was uniform and stable. The present work shows that the mechanical properties of HSLA steel parts deposited using WAAM technology have good strength and toughness. The microstructure gradient of the sample along the deposition direction did not lead to a significant difference in the tensile strength of the sample at different heights. On the contrary, the ductility of the longitudinal sample is slightly lower than that of the transverse sample, indicating some anisotropy in the deposited sample, which is related to the directional growth of grains along the direction of heat flow. From the current work, the thin wall of HSLA steel prepared with the WAAM process has good mechanical properties, which indicates that it is feasible to replace the traditional processing method with the WAAM process to rapidly manufacture an HSLA steel structure meeting the performance requirements.

Three methods are used to predict the martensitic transformation start temperature (M_s ) of steel. Based on the database containing 832 compositions and corresponding M_s data, prediction models are built, modified, and trained. Firstly, M_s was re-calculated by establishing a thermodynamic model to link the martensitic transformation driving force (Gibbs free energy difference of martensite and austenite) with resistance (elastic strain energy, plastic strain energy, interface energy, and shearing energy). Secondly, the existing M_s data is cleaned and re-predicted using traditional empirical formulas within different composition application ranges. Thirdly, four different algorithms in machine learning including random forest, k nearest neighbor, linear regression, and decision tree are trained to predict 832 new M_s values. By comparing the M_s results re-predicted by the mentioned three methods with the original M_s values, the accuracy is evaluated to identify the optimal prediction model.