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Under complex service conditions involving moisture, icing, and repeated friction, liquid retention and ice adhesion on Q355B steel can alter the contact-interface state and increase the risk of frictional wear and surface-performance degradation. Laser microtexturing is of considerable interest for enhancing surface hydrophobicity, reducing liquid retention, and improving the adaptability to service conditions. Existing studies on laser microtexturing have mainly focused on improving the initial contact angle, with insufficient consideration of hydrophobicity retention after mechanical loading. In addition, process selection involving multiple coupled parameters still relies heavily on trial-by-trial experiments, making it difficult to balance optimization efficiency and experimental cost. This study investigated the effects of laser power, scanning speed, scanning times, filling space, and aging time on groove morphology and wetting responses. A two-stage classification–regression ensemble surrogate model was developed using 138 experimental observations generated through a sequential multiregion Box–Behnken design and integrated with constrained NSGA-II for multiobjective optimization. The results showed that Ra, Rp, and Rv decreased by 66.4, 53.0, and 54.0% as the scanning speed increased from 400 to 1600 mm/s. When the laser power increased from 6 to 14 W, valley ablation was markedly intensified, resulting in an 81.5% increase in Rv. The initial contact angle and contact-angle loss exhibited distinct responses to the processing parameters, revealing a clear trade-off between initial hydrophobicity and its retention after mechanical action. The recommended condition comprised a laser power of 9.5 W, a scanning speed of 1150 mm/s, 22 scanning times, a filling space of 7.5 μm, and an aging time of 9.5 weeks. Under this condition, the measured initial contact angle, contact-angle loss, and postwear contact angle were 142.50, 3.90, and 138.60°. These findings provide a methodological basis for the multiperformance design and data-driven process optimization of metallic hydrophobic microtextures.
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Gao et al. (2026) studied this question.
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