06 Multi-objective and context-adaptive product morphology innovations by machine learning

Abstract

This study presents a machine-learning-driven framework for product morphology innovation that overcomes traditional limitations in multi-objective optimization, design-space exploration, and contextual adaptability. The framework introduces four key innovations: four-dimensional morphology modeling, a library of 2,400 parametric primitives, a database of 180 materials and 45 processes, and a conditional GAN-based inverse-design algorithm. Validated across seven categories—consumer electronics, furniture, automotive, medical devices, appliances, sports equipment, and packaging—the framework executed 1,847 iterations, 342 prototypes, and UX studies with 2,156 participants in 12 countries. Results show a 65% efficiency gain, 42% rise in user satisfaction, and 28% cost reduction, while generating culturally tailored designs. Its modular architecture integrates seamlessly into existing workflows, offering manufacturers a powerful, low-cost innovation tool.