DATA-DRIVEN MULTI-SCALE MULTI-PHYSICS MODELLING OF THERMAL STRESS AND DISTORTION IN ADDITIVE MANUFACTURING

We construct a multi-scale multi-physics modeling framework to predict the thermal stress and distortion in AM fabrications. With the proposed approach, the rough surfaces and internal voids can be well incorporated. The coupled CFD-FEM approach are utilized to simulate the two typical powder-based AM processes, i.e., powder bed based process and powder fed process. In order to reduce the computational cost and optimize the model construction, different temperature loading schemes and effects of different stress attribution patterns are explored and discussed. The findings show that our data-driven approach can accurately predict the geometry features of isotherms and temperature profiles around molten pool regions. Based on the mechanical model of data-driven track-scale simulation, the non-uniform inherent strain mapping method is investigated to accurately and efficiently model thermal stress and distortion at the part scale. The position-dependent inherent strain attribution results show good agreement with the experimental measurement and reference.

Author dc:creator

• CHEN FAN

• Additive Manufacturing, Data-driven modelling, Temperature field, Thermal-fluid flow, Finite Element, Thermal Stress