Data-Driven Microstructure and Microhardness Design in Additive Manufacturing Using a Self-Organizing Map

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.

Data-driven analysis of process,structure,and properties of additively manufactured Inconel 718 thin walls

In additive manufacturing of metal parts,the ability to accurately predict the extremely variable temperature field in detail,and relate it quantitatively to structure and properties,is a key step in predicting part performance and optimizing process design.In this work,a finite element simulation of the directed energy deposition(DED)process is used to predict the space-and time-dependent temperature field during the multi-layer build process for Inconel 718 walls.The thermal model results show good agreement with dynamic infrared images captured in situ during the DED builds.The relationship between predicted cooling rate,microstructural features,and mechanical properties is examined,and cooling rate alone is found to be insufficient in giving quantitative property predictions.Because machine learning offers an efficient way to identify important features from series data,we apply a 1D convolutional neural network data-driven framework to automatically extract the dominant predictive features from simulated temperature history.Very good predictions of material properties,especially ultimate tensile strength,are obtained using simulated thermal history data.To further interpret the convolutional neural network predictions,we visualize the extracted features produced on each convolutional layer and compare the convolutional neural network detected features of thermal histories for high and low ultimate tensile strength cases.A key result is the determination that thermal histories in both high and moderate temperature regimes affect material properties.