Exploring biomimetic inspiration and biomechanical optimization in brand design strategy
Abstract
Nature’s principles serve as a source of inspiration for biomimetic design incorporating them to achieve the best functionality and creativity. Biomechanical optimization takes this approach further by focusing on the effective use of physical resources and dynamics. They offer a solid foundation for brand design. This research seeks to look into a new method for biomimetic and biomechanical brand design using a deep learning (DL) model called Resilient Ant Colony Optimized Generative Adversarial Networks (RAC-GAN). The dataset comes from primary sources, including current brand logos and biomimetic images from nature such as plant structures, animal shapes, and natural patterns. These biomimetic images show a range of organic forms and textures that can flash creative and practical design elements. The team filtered the collected images to get rid of duplicates and adjusted to have the same resolution. They applied techniques similar to contrast enhancement to make sure the training data was high-quality. After pre-processing, the dataset into the RAC-GAN model used biomimetic principles to copy organic patterns, while biomechanical optimization made sure the created designs balanced creativity with functionality. The suggested model combines generative modeling with ant colony optimization to guide the creation process. The aim is to make sure the design is strong and works well by using ant-like paths that change to find the best setups. The RAC-GAN methodology demonstrated its ability to generate new concepts for logos that remained accurate to the brand’s values.
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