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This paper aims to present a systematic approach in the literature survey related to metal additive manufacturing (AM) processes and its multi-physics continuum modelling approach for its better understanding.

A systematic review of the literature available in the area of continuum modelling practices adopted for the powder bed fusion (PBF) AM processes for the deposition of powder layer over the substrate along with quantification of residual stress and distortion. Discrete element method (DEM) and finite element method (FEM) approaches have been reviewed for the deposition of powder layer and thermo-mechanical modelling, respectively. Further, thermo-mechanical modelling adopted for the PBF AM process have been discussed in detail with its constituents. Finally, on the basis of prediction through thermo-mechanical models and experimental validation, distortion mitigation/minimisation techniques applied in PBF AM processes have been reviewed to provide a future direction in the field.

The findings of this paper are the future directions for the implementation and modification of the continuum modelling approaches applied to PBF AM processes. On the basis of the extensive review in the domain, gaps are recommended for future work for the betterment of modelling approach.

This paper is limited to review only the modelling approach adopted by the PBF AM processes, i.e. modelling techniques (DEM approach) used for the deposition of powder layer and macro-models at process scale for the prediction of residual stress and distortion in the component. Modelling of microstructure and grain growth has not been included in this paper.

This paper presents an extensive review of the FEM approach adopted for the prediction of residual stress and distortion in the PBF AM processes which sets the platform for the development of distortion mitigation techniques. An extensive review of distortion mitigation techniques has been presented in the last section of the paper, which has not been reviewed yet.

Gas metal arc-based directed energy deposition (GMA-DED) process experiences residual stress (RS) developed due to heat accumulation during successive layer deposition as a significant challenge. To address that, monitoring of transient temperature distribution concerning time is a critical input. Finite element analysis (FEA) is considered a decisive engineering tool in quantifying temperature and RS in all manufacturing processes. However, computational time and prediction accuracy has always been a matter of concern for FEA-based prediction of responses in the GMA-DED process. Therefore, this study aims to investigate the effect of finite element mesh variations on the developed RS in the GMA-DED process.

The variation in the element shape functions, i.e. linear- and quadratic-interpolation elements, has been used to model a single-track 10-layered thin-walled component in Ansys parametric design language. Two cases have been proposed in this study: Case 1 has been meshed with the linear-interpolation elements and Case 2 has been meshed with the combination of linear- and quadratic-interpolation elements. Furthermore, the modelled responses are authenticated with the experimental results measured through the data acquisition system for temperature and RS.

A good agreement of temperature and RS profile has been observed between predicted and experimental values. Considering similar parameters, Case 1 produced an average error of 4.13%, whereas Case 2 produced an average error of 23.45% in temperature prediction. Besides, comparing the longitudinal stress in the transverse direction for Cases 1 and 2 produced an error of 8.282% and 12.796%, respectively.

To avoid the costly and time-taking experimental approach, the experts have suggested the utilization of numerical methods in the design optimization of engineering problems. The FEA approach, however, is a subtle tool, still, it faces high computational cost and low accuracy based on the choice of selected element technology. This research can serve as a basis for the choice of element technology which can predict better responses in the thermo-mechanical modelling of the GMA-DED process.