Large-scale aluminum parts are used in aerospace and automotive industries, due to excellent strength, light weight, and the good corrosion resistance of the material. Additive manufacturing processes enable both cost and time savings in the context of component manufacturing. Thereby, wire arc additive manufacturing (WAAM) is particularly suitable for the production of large volume parts due to deposition rates in the range of kilograms per hour. Challenges during the manufacturing process of aluminum alloys, such as porosity or poor mechanical properties, can be overcome by using arc technologies with adaptable energy input. In this study, WAAM of AlMg5Mn alloy was systematically investigated by using the gas metal arc welding (GMAW) process. Herein, correlations between the energy input and the resulting temperature–time-regimes show the effect on resulting microstructure, weld seam irregularities and the mechanical properties of additively manufactured aluminum parts. Therefore, multilayer walls were built layer wise using the cold metal transfer (CMT) process including conventional CMT, CMT advanced and CMT pulse advanced arc modes. These processing strategies were analyzed by means of energy input, whereby the geometrical features of the layers could be controlled as well as the porosity to area portion to below 1% in the WAAM parts. Furthermore, the investigations show the that mechanical properties like tensile strength and material hardness can be adapted throughout the energy input per unit length significantly.
A Hybrid Machine Learning Approach for Energy Consumption Prediction in Additive Manufacturing
