Multi-objective optimization of fracturing ball strength and corrosion rate with genetic algorithms and interpretable machine learning

Traditional alloy design typically relies on trial and error and experience. Machine learning can significantly accelerate the discovery and design process of new materials. However, as the number of elements in the alloy and target performance metrics increase, alloy optimization becomes more challenging. To address this, this paper proposes a machine learning–based multi-objective optimization method for magnesium alloy fracturing balls. The machine learning model trained on the magnesium alloy corrosion and ultimate compressive strength database achieves an accuracy of 0.98 on the training set and 0.93 on the test set. By using a multi-objective genetic algorithm to optimize the element ratios of the magnesium alloy, Mg-6.4Al-3.4Zn-4.6Cu was obtained, with a corrosion rate of 538 mm/year and an ultimate compressive strength of 369 MPa. This provides a new method for the efficient, rapid, and precise preparation of novel degradable magnesium alloys.