A hybrid multi-objective optimization framework for designing superhydrophobic coatings on magnesium alloys for biomedical applications.

A novel eco-friendly coating process was developed to enhance the corrosion resistance and biocompatibility of Magnesium alloys for biomedical applications. The coating, composed of stearic acid and ZnCl₂, was optimized using a hybrid framework integrating experimental design, machine learning, and multi-objective optimization algorithms. Response Surface Methodology with Central Composite Design (RSM-CCD) was employed to systematically explore the effects of the process parameters on the surface roughness, surface free energy, and corrosion resistance efficiency. After evaluating all response data, an Artificial Neural Network (ANN) model was developed to train and predict outcomes. Following the data augmentation process, a highly accurate model was obtained, yielding an R value exceeding 0.99. The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was coupled with an ANN to generate a diverse Pareto front, which was further refined using Teaching-Learning-Based Optimization (TLBO) and Multiobjective Particle Swarm Optimization (MOPSO). The optimized coatings exhibited a superhydrophobic surface with a water contact angle of 152° ± 1°, significantly enhanced corrosion resistance (92.4 % efficiency), and reduced corrosion rate (0.180 mm/year) compared with the uncoated substrate. Characterization techniques, including XRD, SEM, EDS, FTIR, and Raman spectroscopy, confirmed the formation of protective metal stearate compounds (Zn[CH(CH)COO], Mg[CH(CH)COO]) and the presence of key functional groups. Live/dead cell assays demonstrated the biocompatibility of the optimized coatings, with increased cell proliferation observed after 48 h. This study presents a comprehensive, data-driven approach for developing high-performance, eco-friendly coatings for biomedical Mg alloys, offering a promising solution for biodegradable implant applications.