摘要
This paper presents our latest work on comprehensive modeling of process-structure-property relationships for additive manufacturing （AM） materials, including using data-mining techniques to close the cycle of design-predict-optimize. To illustrate the process- structure relationship, the multi-scale multi-physics pro- cess modeling starts from the micro-scale to establish a mechanistic heat source model, to the meso-scale models of individual powder particle evolution, and finally to the macro-scale model to simulate the fabrication process of a complex product. To link structure and properties, a high- efficiency mechanistic model, self-consistent clustering analyses, is developed to capture a variety of material response. The model incorporates factors such as voids, phase composition, inclusions, and grain structures, which are the differentiating features of AM metals. Furthermore, we propose data-mining as an effective solution for novel rapid design and optimization, which is motivated by the numerous influencing factors in the AM process. We believe this paper will provide a roadmap to advance AM fundamental understanding and guide the monitoring and advanced diagnostics of AM processing.
This paper presents our latest work on comprehensive modeling of process-structure-property relationships for additive manufacturing （AM） materials, including using data-mining techniques to close the cycle of design-predict-optimize. To illustrate the process- structure relationship, the multi-scale multi-physics pro- cess modeling starts from the micro-scale to establish a mechanistic heat source model, to the meso-scale models of individual powder particle evolution, and finally to the macro-scale model to simulate the fabrication process of a complex product. To link structure and properties, a high- efficiency mechanistic model, self-consistent clustering analyses, is developed to capture a variety of material response. The model incorporates factors such as voids, phase composition, inclusions, and grain structures, which are the differentiating features of AM metals. Furthermore, we propose data-mining as an effective solution for novel rapid design and optimization, which is motivated by the numerous influencing factors in the AM process. We believe this paper will provide a roadmap to advance AM fundamental understanding and guide the monitoring and advanced diagnostics of AM processing.
基金
Acknowledgements W. Liu and W. Yan acknowledge the support by the National Institute of Standards and Technology （NIST） and Center for Hierarchical Materials Design （CHiMaD） （Grant Nos. 70NANB13H194 and 70NANBI4H012）. S. Lin and O. L. Kafka acknowledge the support of the National Science Foundation Graduate Research Fellowship （Grant No. DGE-1324585）.
