AI-BASED DESIGN OPTIMIZATION IN FASHION, INTERIOR, AND INDUSTRIAL DESIGN: A DATA-DRIVEN APPROACH
DOI:
https://doi.org/10.29121/shodhkosh.v7.i5s.2026.7661Keywords:
Artificial Intelligence Ai, Design Optimization, Generative Adversarial Networks Gans, Deep Learning, Data-Driven Design, Fashion Design, Interior Design, Industrial DesignAbstract [English]
The rapid growth of the Artificial Intelligence (AI) has transformed the manner designers operate significantly, enabling optimization of data, and decision making based on the intelligence in different areas. The paper at hand presents one design-optimization system relying on AI that can be implemented in fashion, interior, and industrial design to enhance its creativity, efficiency, and performance. The proposed solution integrates machine and deep learning with generative models, such as Generative Adversarial Networks (GANs) and diffusion models to generate and train design solutions. A multi-layered system is designed, which involves data gathering, feature generation, AI-based design generation, optimization and human-AI interaction to facilitate iterative and adaptive design procedures. The study design is a combination of a research design which combines both qualitative and quantitative research design. Multi-source datasets are used to train and validate the model using the user preferences, market trends, design repositories and environmental data. An optimization plan is developed with multi-objective approach to balance the important design criteria, such as aesthetic quality, functionality, and cost efficiency, sustainability, and user satisfaction. The experimental evidence shows that the proposed model achieves a great improvement in design efficiency, personalization, and optimization potentials in all three areas compared to the traditional design methods. Comparative analysis and graphical evaluations also demonstrate the functionality of the framework in generating quality and scalable design solutions. Human-in-the-loop mechanisms are also integrated to make sure that creative control and contextual relevance is retained and that the computational intelligence is exploited. In spite of the issues surrounding the quality of the data, the complexity of computations, and the interpretation, the suggested framework offers a solid basis of the next-generation intelligent design systems. The study helps to fill the gap between human creativity and optimization by AI and provides more innovative, adaptive, and sustainable design practices.
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Copyright (c) 2026 Soni Kiran Kendre, Kanchan Dhote, Manali Patil, Manasi Patil, Dr. Parul Bhanarkar, Dr. Rajesh Kulkarni
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