Abstract
This research investigates the transformative potential of integrating generative artificial intelligence (Gen-AI) into product design frameworks for Aircraft Interior Design, specifically examining its impact on accelerating the iterative design processes. Iterative design is a process in which product designers undergo repeated cycles of prototyping, feedback, and refinement. This is a cornerstone of any Design Thinking Framework, while effective, these methods are constrained by inherent time limitations and the costs of prototyping. An infusion of generative AI technologies into these processes stands as a promising solution to redefine the dynamics of this iterative design journey.
The rapid raise of generative AI in recent times has been driven by the growing accessibility of large datasets and progress in deep learning techniques. The technology's capacity to produce data mirroring real-world features holds significant promise in addressing challenges like data augmentation, anomaly detection, and creative content generation. By thoroughly understanding the requirements, models, types of generative algorithms, and evaluation metrics within this field, researchers and practitioners can make well-informed choices when designing and deploying generative systems for applications such as aircraft seat design.
The hypothesis underpinning this study asserts that the integration of generative AI into iterative design processes can expedite the process cycle, leading to quicker refinement and optimization of product designs based on real-time feedback. Exploration of the dynamics of human-AI collaboration within iterative design frameworks will be undertaken, evaluating the extent to which AI-driven designs enhance the speed and effectiveness of the design evolution process.
This research aspires to contribute comprehensive insights into the symbiotic relationship between generative AI and iterative design processes, offering a holistic understanding of the collaboration dynamics that accelerate the evolution of product designs. By critically assessing existing literature and building upon state-of-the-art methodologies, this study aims to inform the ongoing discourse on the transformative potential of generative AI in the realm of product design frameworks.