Wei Xiong*

Department of Mechanical Engineering and Materials Science,
University of Pittsburgh, PA 15261, USA

EXTENDED ABSTRACT: Additive manufacturing is a versatile approach for fabricating parts with complex geometry [1]. With the development of such a technique, it is more often used as a high-throughput processing approach producing samples with graded composition for materials characterization to develop comprehensive process-structure-property databases. In this talk, we will review the existing efforts using additive manufacturing as the materials design tool supporting the materials genome database. In addition, some case studies will be demonstrated to emphasize the importance of data curation after generating the data by using the AM techniques. Based on the case studies, we emphasize the importance of synergistic efforts in developing databases, models, and high-throughput experimental techniques. Pitfalls and opportunities will be identified regarding future efforts on high-throughput experimentation using additive manufacturing [2]. This discussion also calls for the high-throughput microstructure and mechanical characterization of materials, which are critical to generating useful support for the materials genome modeling [3].

Keyword: high-throughput; integrated computational materials engineering; additive manufacturing REFERENCES

[1] J. Smith, W. Xiong, W. Yan, S. Lin, P. Chen, O.L. Kafka, G.J. Wagner, J. Cao, W.K. Liu, Computational Mechanics, 57 (2016) 583-610.

⑵ W. Xiong, Journal of Materials Informatics, 2 (2022) 12.

[3] Wei Xiong, Gregory B. Olson, npj Computational Materials, 2 (2016) 15009.