Thermo-mechanical response of aluminum alloy in the additive friction-stir deposition process

• A novel computational approach has been implemented to predict thermo-mechanical response in the additive friction-stir deposition (AFSD) process. • A coupled eulerian-lagrangian (CEL) model is developed using AA6061-T6 alloy based on the plastic deformation of the material. • The model uses tool rotation speed, tool traverse speed, and material deposition rate as major input parameters to predict the material flow, temperature, and stress distribution. • Effects of changing the major input parameters on the peak temperature are analyzed to optimize the process parameters in AFSD. Additive Friction Stir Deposition (AFSD) is an emerging solid-state additive manufacturing (AM) technique that creates fully dense metallic structures with equiaxed fine microstructures. The feedstock material is plasticized via frictional heating and deposited in the solid state. Due to the complex multi-physics nature of the process, an in-depth understanding of the interplay between material flow, temperature variations, and stress distribution within the deposited layers under various process parameters is crucial for achieving desired outcomes. This study focuses on the development of a plasticity-based computational model that employs a coupled Eulerian-Lagrangian (CEL) finite element methodology to analyze the thermo-mechanical response of the AA6061-T6 alloy in the AFSD process. By incorporating essential AFSD process variables namely, tool rotation speed, tool traverse speed, and material deposition rate, the model can accurately forecast the flow of material, temperature fluctuations, and stress distribution across different operational settings. For instance, an optimal solid-state deposition of AA 6061-T6 alloy is achieved with 380 RPM tool rotation speed, 0.9 mm/s tool traverse speed, and 0.3 mm/s material deposition rate for the geometry reported in this study. The CEL model is validated by comparing its results (e.g., peak temperature) with the experimental data and published computational results for the same combination of process parameters, giving the maximum errors of 8 % and 2.8 %, respectively. Through the utilization of this proposed model, a practical and efficient means of predicting process results is established, enabling a rapid and cost-effective optimization of the AFSD process parameters for different scale of the feed material, tool, and substrate. Ultimately, this advancement contributes to the progression of solid-state AM techniques and development of digital twins by streamlining the process with scalability, multifunctionality, and a variety of material selections.