Biomaterials research-driven design visualized by AI text-prompt-generated images

Abstract

AI text-to-image generated images have revolutionized the design process and its rapid development since 2022. Generating various iterations of perfect renders in few seconds by textually expressing the design concept. This high-potential tool has opened wide possibilities for biomaterials research-driven design. That is based on developing biomaterials for multi-scale applications in the design realm and built environment. From furniture to architectural elements to architecture. This approach to the design process has been augmented by the massive capacity of AI text-to-image models to visualize high-fidelity and innovative renders that reflect very detailed physical characteristics of the proposed biomaterials from micro to macro. However, this biomaterials research-driven design approach aided by AI text-to-image models requires criteria for evaluating the role and efficiency of employing AI image generation models in this design process. Furthermore, since biomaterials research-driven design is focused not only on design studies but also the biomaterials engineering research and process, it requires a sufficient method for protecting its novelty and copyrights. Since their emergence in late 2022, AI text-to-image models have been raising alarming ethical concerns about design authorship and designer copyrights. This requires the establishment of a referencing method to protect the copyrights of the designers of these generated renders as well as the copyrights of the authors of their training data referencing by proposing an auxiliary AI model for automatic referencing of these AI-generated images and their training data as well. Thus, the current work assesses the role of AI text-to-image models in the biomaterials research-driven design process and their methodology of operation by analyzing two case studies of biomaterials research-driven design projects performed by the authors aided by AI text-to-image models. Based on the results of this analysis, design criteria will be presented for a fair practice of AI-aided biomaterials research-driven process.