期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing 被引量:3
1
作者 Xiaoyu Xie jennifer bennett +4 位作者 Sourav Saha Ye Lu Jian Cao Wing Kam Liu Zhengtao Gan 《npj Computational Materials》 SCIE EI CSCD 2021年第1期767-778,共12页
Metal additive manufacturing provides remarkable flexibility in geometry and component design,but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties,significantly com... Metal additive manufacturing provides remarkable flexibility in geometry and component design,but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties,significantly complicating the materials design process.To this end,we develop a mechanistic data-driven framework integrating wavelet transforms and convolutional neural networks to predict location-dependent mechanical properties over fabricated parts based on process-induced temperature sequences,i.e.,thermal histories.The framework enables multiresolution analysis and importance analysis to reveal dominant mechanistic features underlying the additive manufacturing process,such as critical temperature ranges and fundamental thermal frequencies.We systematically compare the developed approach with other machine learning methods.The results demonstrate that the developed approach achieves reasonably good predictive capability using a small amount of noisy experimental data.It provides a concrete foundation for a revolutionary methodology that predicts spatial and temporal evolution of mechanical properties leveraging domain-specific knowledge and cutting-edge machine and deep learning technologies. 展开更多
关键词 ADDITIVE properties PREDICTION
原文传递
Data-driven analysis of process,structure,and properties of additively manufactured Inconel 718 thin walls 被引量:1
2
作者 Lichao Fang Lin Cheng +3 位作者 jennifer A.Glerum jennifer bennett Jian Cao Gregory J.Wagner 《npj Computational Materials》 SCIE EI CSCD 2022年第1期1168-1182,共15页
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 p... 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. 展开更多
关键词 INCONEL PROCESS ADDITIVE
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部