清华大学材料科学与工程研究院《材料科学论坛》学术报告

报告时间：2024年4月16日上午10:30

报告人：Prof. Pedro E. J. Rivera-Diaz-del-Castillo（Royal Academy of Engineering Research Chair, Director of Research, Faculty of Engineering and Physical Sciences, University of Southampton）

报告地点：清华大学逸夫技术科学楼A205学术报告厅

邀请人：陈浩老师

Physical and data-driven modelling approaches for the design of immiscible joints: welding, functional grading, and alloy design

报告简介：

A common feature shared by the processes of welding, additive manufacturing, and functionally graded materials is the provision of a localised source of energy, the use of alloys amenable to concentrated heating, rapid heating/cooling, and the adoption of dissimilar materials. Miscibility is the property of two substances to mix in all proportions forming a homogeneous mixture or solution; miscible alloys have shaped manufacturing technology since metals started to be combined. Joining immiscible alloys represent a great technological and commercial opportunity; this can be achieved by adding a third surrogate alloy or element to promote the bond.

In this talk, we consider three practical cases of alloy design for immiscible systems: (1) The welding of magnesium with steel alloys mediated by a copper. (2) Functional grading of titanium and copper alloys in horizontal laser powder bed fusion. (3) Grading strategies to additively manufacture nickel-based superalloys with titanium alloys mediated by pure copper. General observations of these cases are the need to quantify energy absorption and temperature distributions, the formation and inhibition of specific intermetallic compounds, and solute redistribution. The three successful cases of alloy joining/grading are reviewed in light of deep learning and physical modelling strategies to discover a broad set or principles and approaches for immiscible multi-material alloy design.

The presenter also takes the opportunity to introduce the world-class characterisation and modelling facilities at the University of Southampton, putting them in the context of our research in additive manufacturing.