Development and Application of an Intelligent Green Design Tool: An Innovative Pathway Supporting Modularity and the Circular Economy

Abstract

As global focus on sustainable development continues to grow, the shift toward a circular economy has become a crucial direction for the manufacturing industry, bringing new and complex challenges to product design. Despite this urgency, many widely used green design methods and tools still lack sufficient intelligence and system-level integration. In practice, there remains a clear disconnect between theoretical frameworks and real-world application—especially when it comes to effectively combining modular design principles with full product life cycle strategies to support early-stage design decisions.To overcome these limitations, this study introduces a novel intelligent green design approach called the Intelligent Green Extension Design Method (IGEDM). The method integrates three complementary components: the formal innovation logic of Extenics, the data processing and optimization capabilities of machine learning, and the environmental evaluation strength of Life Cycle Assessment (LCA). Together, these elements form an intelligent tool prototype designed to support green decision-making at the early stages of product design.A smart speaker is used as a case study to illustrate the complete workflow of the proposed tool. By building a multi-dimensional matter-element model of the product, machine learning techniques such as Random Forests and neural networks are applied to optimize multiple green performance objectives, including carbon footprint, ease of disassembly, and material recyclability. These optimization results are then combined with extension transformation theory to generate a range of innovative modular design solutions.The findings show that, compared with conventional design approaches, the solutions produced using the IGEDM tool deliver substantial environmental benefits. Specifically, the optimized designs achieve an estimated 25% reduction in total life cycle carbon emissions and a 40% increase in modularity, while also clearly identifying key opportunities for green improvement. This study not only provides a practical and quantifiable intelligent pathway for green design research, but also demonstrates the feasibility of embedding artificial intelligence into the front end of product design to drive sustainable innovation. Ultimately, it offers enterprises a powerful decision-support tool and a forward-looking design paradigm for developing products aligned with the circular economy.