Data-driven designing on mechanical properties of biodegradable wrought zinc alloys

Abstract

A small dataset of ~300 datapoints of zinc (Zn) alloys were collected and 125 entries containing alloying elements, extrusion parameters (temperature (ET), speed (ES) and ratio (ER)), and mechanical properties (yield strength (YS), ultimate tensile strength (UTS), and final elongation (EL)) were selected. Machine learning models were applied to predict mechanical properties, in which random forest (RF) model exhibited the best performance and further validated by a new experimental sample of Zn-0.05Mg-0.5Mn, with the mean absolute percentage error (MAPE) less than 10%. [Correction added on 13 May 2025, after first online publication: In the preceding sentence, the value ‘12%’ has been changed to ‘10%’.]. Moreover, an empirical formula was induced by the clustering model (CL). Control over strain softening/hardening behavior was achieved through only process parameter adjustment. Finally, by combining multi-objective genetic algorithm and RF models, the optimization alloy composition and extrusion parameters was carried out, targeting high-strength, strength/plasticity synergy, and high plasticity for biodegradable purpose. A notable optimized scheme for strength/plasticity synergy in Zn-0.20Mg-0.60Mn (wt.%) achieves the YS of 303 MPa, UTS of 354 MPa, and EL of 25.1% with the MAPE less than 10%, and exhibits the strain-hardening response, associated with ER of 16, ET of 170°C, and ES of 3.21 mm/s. [Correction added on 13 May 2025, after first online publication: In the preceding sentence, the value ‘345 MPa’ has been changed to ‘354 MPa’. and the value ‘3.33 mm/s’ has been changed to ‘3.21 mm/s’].

Keywords

composition design / machine learning / mechanical properties / process optimization / zinc alloy

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2025 The Author(s). Materials Genome Engineering Advances published by Wiley-VCH GmbH on behalf of University of Science and Technology Beijing.