Machine learning design of 400 MPa grade biodegradable Zn-Mn based alloys with appropriate corrosion rates

Abstract

The commonly used trial-and-error method of biodegradable Zn alloys is costly and blindness. In this study, based on the self-built database of biodegradable Zn alloys, two machine learning models are established by the first time to predict the ultimate tensile strength (UTS) and immersion corrosion rate (CR) of biodegradable Zn alloys. A real-time visualization interface has been established to design Zn-Mn based alloys; a representative alloy is Zn-0.4Mn-0.4Li-0.05Mg. Through tensile mechanical properties and immersion corrosion rate tests, its UTS reaches 420 MPa, and the prediction error is only 0.95%. CR is 73 µm/a and the prediction error is 5.5%, which elevates 50 MPa grade of UTS and owns appropriate corrosion rate. Finally, influences of the selected features on UTS and CR are discussed in detail. The combined application of UTS and CR model provides a new strategy for synergistically regulating comprehensive properties of biodegradable Zn alloys.

Keywords

Zn alloys / machine learning / alloy design / performance prediction

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