Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning

Abstract

It is difficult to rapidly design the process parameters of copper alloys by using the traditional trial-and-error method and simultaneously improve the conflicting mechanical and electrical properties. The purpose of this work is to develop a new type of Cu-Ni-Co-Si alloy saving scarce and expensive Co element, in which the Co content is less than half of the lower limit in ASTM standard C70350 alloy, while the properties are as the same level as C70350 alloy. Here we adopted a strategy combining Bayesian optimization machine learning and experimental iteration and quickly designed the secondary deformation-aging parameters (cold rolling deformation 90%, aging temperature 450°C, and aging time 1.25 h) of the new copper alloy with only 32 experiments (27 basic sample data acquisition experiments and 5 iteration experiments), which broke through the barrier of low efficiency and high cost of trial-and-error design of deformation-aging parameters in precipitation strengthened copper alloy. The experimental hardness, tensile strength, and electrical conductivity of the new copper alloy are HV (285 ± 4), (872 ± 3) MPa, and (44.2 ± 0.7)% IACS (international annealed copper standard), reaching the property level of the commercial lead frame C70350 alloy. This work provides a new idea for the rapid design of material process parameters and the simultaneous improvement of mechanical and electrical properties.

Keywords

copper alloy / process design / machine learning / Bayesian optimization / utility function

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