Developing the AM G-code based thermomechanical finite element platform for the analysis of thermal deformation and stress in metal additive manufacturing process
English
Mashhood, Muhammad[University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)]
Peters, Bernhard[University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)]
Zilian, Andreas[University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)]
Baroli, Davide[Euler Institute, Università della Svizzera italiana]
[en] The 3D printing process known as SLM involves the melting of the metal powder, which results in a melt-pool. When this melt-pool solidifies, the solidified metal undergoes cooling and reheating in the presence of air and multiple laser passes for continuous material consolidation. As a result of such thermal cycles, the manufactured part develops permanent thermal deformation and residual stresses. The current work proposes the FEM and AM G-code based numerical strategy to qualitatively analyze the formation of such deformations and stresses at part scale. A multi-physics model was developed by coupling of transient thermal heat equation with non-linear structural solver. To mimic the consolidation of material with laser motion, the finite elements were activated as per the pattern of metal deposition under the influence of AM G-code. A numerical experiment was conducted to virtually manufacture the part with mechanical properties of 15--5PH stainless steel [1]. We found that the thermomechanical FEM model interfaced with the AM G-code translated data helps to evaluate the comparable trends of thermal deformation and residual stress results with already established studies. This demonstrates that with a given set of operational instructions, how the thermal conduction, convection and radiation drive the AM process by thermally loading the deposited material. Furthermore, the AM G-code interfacing facilitated the communication of laser scanning path with numerical FEM solver. We anticipate that such development may enable the manufacturing and simulation engineers to early estimate the possible final deformation of the AM fabricated part. Additionally, the developed strategy may also be the initial step for the physically informed neural networks to optimize the laser scan path for precise manufacturing of the metal parts.
[University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)]
