Despite tremendous efforts in improving metal 3D printers’ accuracy and reliability, mainstream insertion of additive manufacturing (AM) in industrial shopfloors is still limited by uncertainty and inconsistency in the AM process. Computer modeling and simulation is the natural answer to address such issues before printing, thus reducing the cost of trial-and-error. However, an exhaustive feature-rich, high-fidelity simulation of AM is extremely challenging, due to the tight coupling between different length scales (from part-scale to powder-scale) and time scales (from build time to scan vector time). By leveraging our in-house capabilities, we have developed an integrated digital platform which combines a thermal simulation at the scale of the part, a discrete element method simulation of powder spreading [1], a ray-tracing simulation of laser-matter interaction, a powder-scale simulation of powder melting and solidification and microstructure evolution [2][3], two phase-field simulations of dendritic and precipitates formation, a crystal plasticity calculation for prediction of mechanical properties, and a part-scale simulation of residual stress and distortion, to provide a multiscale simulation platform for AM. Importantly, we also developed a physics-based classification capability which, given an overall thermal history of the component, identifies the regions that have experiences similarly, or different, thermal histories for microstructure evolution. These capabilities were integrated into a single, end-to-end platform tailored to Nickel alloys, the EOS M290 printer, robot-guided directed energy deposition, with more materials and printers in pipeline to be added. For model validation, test coupons were printed and analysed in terms of porosity, microstructure, and mechanical properties, while distortion was validated with an actual industrial component. Here, we intend to show that a focus on computational speed and a seamless integration among length scales provide the user a holistic view of the manufacturing process and supports informed decisions on material choice, part design, and process parameters.

References
[1] Dai et al., Powder Technol., 2022, 408, 117790
[2] Wei et al., Additive Manuf., 2022, 54, 102779
[3] Laskowski et al., Additive Manuf., 2022, 60, 103266