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Towards An Instant Structure-Property Prediction Quality Control Tool For Additive Manufactured Steel Using A Crystal Plasticity Trained Deep Learning Surrogate, Yuhui Tu, Zhongzhou Liu, Luiz Carneiro, Caitriona M. Ryan, Andrew C. Parnell, Sean B. Leen
Towards An Instant Structure-Property Prediction Quality Control Tool For Additive Manufactured Steel Using A Crystal Plasticity Trained Deep Learning Surrogate, Yuhui Tu, Zhongzhou Liu, Luiz Carneiro, Caitriona M. Ryan, Andrew C. Parnell, Sean B. Leen
Research Collection School Of Computing and Information Systems
The ability to conduct in-situ real-time process-structure-property checks has the potential to overcome process and material uncertainties, which are key obstacles to improved uptake of metal powder bed fusion in industry. Efforts are underway for live process monitoring such as thermal and image-based data gathering for every layer printed. Current crystal plasticity finite element (CPFE) modelling is capable of predicting the associated strength based on a microstructural image and material data but is computationally expensive. This work utilizes a large database of input–output samples from CPFE modelling to develop a trained deep neural network (DNN) model which instantly estimates the …