Modelling and parametric optimization of induction-aided hot embossing process to maximize the hydrophobicity of embossed PMMA polymer surface via metaheuristic techniques

Many researchers are interested in superhydrophobic surfaces because of their potential uses in science and industry. Superhydrophobic surfaces are often used to address environmental challenges due to their many capabilities, including preventing ice formation in refrigeration systems, protecting against fouling and corrosion in maritime settings, enabling self-cleaning in solar cells, etc. This work attempts to fabricate the superhydrophobic polymer surface by embossing the micron-size patterns on the polymethyl methacrylate polymer workpiece via an in-house developed induction-aided hot embossing (IHE) setup. This embossed pattern makes the surface roughen, and as the air gets entrapped in the micro-cavities, it helps to increase the water contact angle (WCA). Firstly, parametric analysis was performed by considering the main four parameters of IHE, i.e., embossing temperature (T_e), time, pressure, and deembossing temperature. This analysis reveals that T_e has a major impact on the WCA compared to other parameters. The percentile increment in WCA in the case of T_e is 7.79%. The regression analysis developed a correlation between WCA and IHE operating parameters. The WCA model was optimized via TLBO and the JAYA algorithm to maximize the WCA. The TLBO algorithm predicts the maximum WCA \((127.07^\circ \pm 2^\circ )\) $(127.{07}^{\circ }\pm 2^{\circ })$ compared to the JAYA algorithm \((113.41^\circ \pm 3^\circ )\) $(113.{41}^{\circ }\pm 3^{\circ })$ with corresponding optimal parameters setting. Lastly, the parameters predicted by the TLBO and Jaya algorithms were used in a confirmation test. Its result confirms that the predicted value of WCA by the TLBO algorithm closely matches the experimental value, i.e., (percentile error = 2.63%).