Fig. 9: GA-based optimization to minimize material loss. | npj Materials Degradation

Fig. 9: GA-based optimization to minimize material loss.

From: Accelerating the design and discovery of tribocorrosion-resistant metals by interfacing multiphysics modeling with machine learning and genetic algorithms

Fig. 9

a Evolution of each generation in parameter space of Young’s modulus, strength, Ecorr, and icorr. The effectiveness of the GA predictions is validated through b tribocorrosion profiles, c quantitative analysis of similarity with respect to FEA simulations. d The radar plots of 9 validated GA optimized cases (clockwise), including material properties Young’s modulus, yield strength, Ecorr, and icorr; and the resultant material losses (counterclockwise) such as tribocorrosion volume loss, wear, corrosion, synergy loss, and the fractions of synergy loss and wear loss.

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