To design microstructure and microhardness in the additive manufacturing(AM)of nickel(Ni)-based superalloys,the present work develops a novel data-driven approach that combines physics-based models,experimental measur...To design microstructure and microhardness in the additive manufacturing(AM)of nickel(Ni)-based superalloys,the present work develops a novel data-driven approach that combines physics-based models,experimental measurements,and a data-mining method.The simulation is based on a computational thermal-fluid dynamics(CtFD)model,which can obtain thermal behavior,solidification parameters such as cooling rate,and the dilution of solidified clad.Based on the computed thermal information,dendrite arm spacing and microhardness are estimated using well-tested mechanistic models.Experimental microstructure and microhardness are determined and compared with the simulated values for validation.To visualize process-structure-properties(PSPs)linkages,the simulation and experimental datasets are input to a data-mining model-a self-organizing map(SOM).The design windows of the process parameters under multiple objectives can be obtained from the visualized maps.The proposed approaches can be utilized in AM and other data-intensive processes.Data-driven linkages between process,structure,and properties have the potential to benefit online process monitoring control in order to derive an ideal microstructure and mechanical properties.展开更多
基金Jian Cao,Gregory J.Wagner,and Wing K.Liu acknowledge support from the National Science Foundation(NSF)Cyber-Physical Systems(CPS)(CPS/CMMI-1646592)Hengyang Li acknowledges support from the Northwestern Data Science Initiative(DSI+6 种基金171474500210043324)Jian Cao,Gregory J.Wagner,Wing K.Liu,Jennifer L.Bennett,and Sarah J.Wolff acknowledge support from the Digital Manufacturing and Design Innovation Institute(DMDII15-07)Jian Cao,Wing K.Liu,Zhengtao Gan,and Jennifer L.Bennett acknowledge support from the Center for Hierarchical Materials Design(CHiMaD70NANB14H012)This work made use of facilities at DMG MORI and Northwestern UniversityIt also made use of the MatCI Facility,which receives support from the MRSEC Program(NSF DMR-168 1720139)of the Materials Research Center at Northwestern University.
文摘To design microstructure and microhardness in the additive manufacturing(AM)of nickel(Ni)-based superalloys,the present work develops a novel data-driven approach that combines physics-based models,experimental measurements,and a data-mining method.The simulation is based on a computational thermal-fluid dynamics(CtFD)model,which can obtain thermal behavior,solidification parameters such as cooling rate,and the dilution of solidified clad.Based on the computed thermal information,dendrite arm spacing and microhardness are estimated using well-tested mechanistic models.Experimental microstructure and microhardness are determined and compared with the simulated values for validation.To visualize process-structure-properties(PSPs)linkages,the simulation and experimental datasets are input to a data-mining model-a self-organizing map(SOM).The design windows of the process parameters under multiple objectives can be obtained from the visualized maps.The proposed approaches can be utilized in AM and other data-intensive processes.Data-driven linkages between process,structure,and properties have the potential to benefit online process monitoring control in order to derive an ideal microstructure and mechanical properties.
