Integration of artificial neural network with finite element analysis for residual stress prediction of direct metal deposition process

Direct Metal Deposition (DMD) process is considered to be an efficient and reliable manufacturing method for the production of complex parts in many design applications. The thermo-mechanical nature of the DMD process induces a significant amount of residual stresses and distortions on fabricated parts. Evaluation of residual stress distributions requires considerable amount of modeling and experimental works. Therefore, there is a need for an accurate, and feasible assessment method(s) for engineers to estimate residual stresses based on chosen process parameters and geometrical features of DMD built parts. A novel artificial neural network-based modelling approach integrated with finite element analysis is proposed to address shortcomings of conventional thermo-mechanical finite element-based models and improve the computational efficiency of predicting residual stresses of AISI 304 L parts built on the basis of the DMD process. Predicted results showed that the novel approach is capable of accurate and efficient prediction of residual stress distributions of three different geometric structures e.g. a plane wall shape, L-shape wall, and rectangular box structures. Furthermore, the computational time of predicting the residual stresses for the wall, L-shape wall, and rectangular box structures is significantly improved with respect to the classical finite element thermo-mechanical analysis.

Automated summary

The DMD process is efficient for manufacturing complex parts, but results in significant residual stresses which require accurate assessment methods. A novel artificial neural network-based modeling approach improves the prediction of residual stresses for different geometric structures and enhances computational efficiency.