Additive Manufacturing (AM) techniques have become an important constituent of the fourth industrial revolution, often termed as Industry 4.0 or Smart Manufacturing. With the manufacturing industry requiring new techniques which reduces human decision making in real time, AM techniques are well-suited for this approach. Although most AM techniques focus on melting metal powders layer-by-layer to build components, these techniques are not without faults. One of the most significant challenges faced in making such AM techniques mainstream is the porosity produced in the specimens. Such pores are intrinsic to the process and are difficult to be removed. However, their presence is detrimental to the life of the component especially in terms of their mechanical properties. Another problem with such AM techniques is the anisotropy and heterogeneity produced in the components. These can be in terms of microstructure, mechanical properties, and crystallographic texture. Such problems can be overcome if the 3D printing is performed without melting. In this regard, additive friction stir deposition (AFSD) exhibits the highest potential for industrialization. AFSD involves extruding a solid metal feedstock bar for layer-by-layer 3D printing while being heated by a rotating tool due to friction between the tool and the material being deposited.

 In order to obtain components with the most appropriate mechanical properties, it is important that the optimum process parameters be used for manufacturing. For AFSD, systematic studies in this regard have not been performed. In our work, we have used the response surface methodology (RSM), in particular, the central composite design (CCD) to optimize the process parameters to obtain the highest strength for the AFSD of Al 6082 alloys. A 3-factor CCD was created by taking into consideration three process parameters namely, the spindle rotation speed, the table traverse speed and the ratio of the table traverse speed and the actuator feed rate (Ω). 17 experiments were performed for various process parameter combinations. In-situ monitoring of the temperature during those experiments were also made using an infrared bolometer. Microstructural analysis and hardness measurements were performed at the specimen cross-section. The optimized process parameters were obtained numerically that maximized the hardness.