Design is a complex, iterative, and innovative process. By traditional methods, it is difficult for designers to have an integral priori design experience to fully explore a wide range of design solutions. Therefore, refined intelligent design has become an important trend in design research. More powerful design thinking is needed in intelligent design process. Combining cognitive dynamics and a cobweb structure, an intelligent design method is proposed to formalize the innovative design process. The excavation of the dynamic mechanism of the product evolution process during product development is necessary to predict next-generation multi-image product forms from a larger design space. First, different design thinking stimulates the information source and is obtained by analyzing the designers’ thinking process when designing and mining the dynamic mechanism behind it. Based on the nonlinear cognitive cobweb process proposed by Francisco and a natural cobweb structure, the product image cognitive cobweb model (PICCM) is constructed. Then, natural cobweb predation behavior is simulated using a stimulus information source to impact the PICCM. This process uses genetic algorithms to obtain numerous offspring forms, and the PICCM’s mechanical properties are the energy loss parameters in the impact information. Furthermore, feasible solutions are selected from intelligent design sketches by the product artificial form evaluation system based on designers’ cognition, and a new product image cognitive cobweb system is reconstructed. Finally, a case study demonstrates the efficiency and feasibility of the proposed approach.

中文翻译：

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基于设计认知动力学和蛛网结构的产品形态智能设计

设计是一个复杂，反复和创新的过程。通过传统方法，设计人员很难拥有完整的先验设计经验来全面探索各种设计解决方案。因此，精巧的智能设计已成为设计研究的重要趋势。在智能设计过程中需要更强大的设计思想。结合认知动力学和蜘蛛网结构，提出了一种智能设计方法来规范创新设计过程。在产品开发过程中挖掘产品演化过程的动态机制对于从更大的设计空间预测下一代多图像产品形式很有必要。第一，在设计和挖掘背后的动力机制时，通过分析设计师的思维过程可以获得不同的设计思维，从而激发了信息源。基于Francisco提出的非线性认知蜘蛛网过程和自然蜘蛛网结构，构建了产品图像认知蜘蛛网模型（PICCM）。然后，使用刺激信息源模拟自然的蜘蛛网捕食行为以影响PICCM。该过程使用遗传算法来获取大量后代，而PICCM的机械性能是冲击信息中的能量损失参数。此外，基于设计人员的认知，通过产品人工表格评估系统从智能设计草图中选择可行的解决方案，并重建新的产品图像认知蜘蛛网系统。最后，