Superhydrophobic surfaces (SHSs) have received increasing attention in the last decade, and have been generally developed from hydrophobic materials. In this study, a facile fabrication method based on ultrasonic imprinting is proposed to develop SHSs from a hydrophilic polymer, poly(methyl methacrylate) (PMMA). To fabricate SHSs on PMMA substrates, micro electrical discharge machining, micromachining and ultrasonic imprinting were sequentially used. The ultrasonic imprinting was performed for various channel designs and imprinting conditions, and the resulting water contact angles were measured for the replicated samples. As a result, superhydrophobic characteristics could be obtained on a hydrophilic PMMA replica without any chemical treatments. The effects of nanoscale roughness on the replicated channel as well as composition change are discussed with respect to the analyses using an atomic force microscope, x-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy.

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A convolutional neural network (CNN), which exhibits excellent performance in solving image-based problem, has been widely applied to various industrial problems. In general, the CNN model was applied to defect inspection on the surface of raw materials or final products, and its accuracy also showed better performance compared to human inspection. However, surfaces with heterogeneous and complex backgrounds have difficulties in separating defects region from the background, which is a typical challenge in this field. In this study, the CNN model was applied to detect surface defects on a hierarchical patterned surface, one of the representative complex background surfaces. In order to optimize the CNN structure, the change in inspection performance was analyzed according to the number of layers and kernel size of the model using evaluation metrics. In addition, the change of the CNN’s decision criteria according to the change of the model structure was analyzed using a class activation map (CAM) technique, which can highlight the most important region recognized by the CNN in performing classification. As a result, we were able to accurately understand the classification manner of the CNN for the hierarchical pattern surface, and an accuracy of 93.7% was achieved using the optimized model.

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