About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing of Metals: Microstructure, Properties and Alloy Development
|
| Presentation Title |
Rapid Data Acquisition and Machine Learning-Assisted Composition Design of Functionally Graded Alloys via Wire-Feed Additive Manufacturing |
| Author(s) |
Xin Wang, Soumya Sridar, Wei Xiong |
| On-Site Speaker (Planned) |
Soumya Sridar |
| Abstract Scope |
The application of artificial intelligence in materials design is hindered by the lack of high-quality datasets, necessitating the development of rapid techniques for acquiring reliable data. Wire-feed additive manufacturing (WFAM) offers a promising approach for fabricating functionally graded alloys with precise composition control, thereby enabling the generation of extensive datasets to investigate process-structure-property relationships and facilitating machine learning (ML)-assisted alloy design. Leveraging high-throughput experiments, calculations, and genetic algorithms applied to WFAM-built graded alloys, an ML model was developed based on a database with 32 material descriptors and hundreds of data entries capable of predicting hardness and porosity. The ML model has demonstrated its efficacy by successfully designing a gradient alloy with enhanced properties, which can thus be used in the functionally graded alloy printing from P91 steel to Inconel 740H superalloy. This work represents a significant advancement in coupling ML and additive manufacturing for materials design. |