About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Advances in Surface Engineering IV
|
Presentation Title |
Simultaneous Effects of MC Carbide Formers on Hardness of Wear Resistant Overlays by Design of Experiments and Machine Learning |
Author(s) |
Jing Li, Bing Cao, Haohan Chen, Leijun Li |
On-Site Speaker (Planned) |
Jing Li |
Abstract Scope |
Optimizing the composition and improving the wear-resistant properties of complex alloys have always been difficult by traditional, one-variable-at-a-time, methods. Here we propose a Design of Experiments and machine learning strategy to design alloys with a targeted hardness in the overlay system containing MC-type primary carbides and eutectic matrix (austenite and M7C3-type carbides) with various Nb, Ti, and V combinations.
An interactive contour map has been built to correlate the chemical composition and microhardness of the overlay through a simple generalized linear model. Three chemical compositions with a predicted hardness of 600 to 610 HV have been used to verify and refine the model. The proposed strategy provides an efficient way to design overlays with targeted hardness without exhausting experiments. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Surface Modification and Coatings, Solidification |