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Intelligent Design of Product Forms Based on Design Cognitive Dynamics and a Cobweb Structure

机译:基于设计认知动力学和蜘蛛网结构的产品形式智能设计

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摘要

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.
机译:设计是复杂,迭代,创新的过程。通过传统方法,设计人员难以实现一体的先验设计体验,以充分探索各种设计解决方案。因此,精致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维。组合认知动力学和蜘蛛网结构,提出了一种智能设计方法来形式化创新的设计过程。产品开发期间产品进化过程的动态机制的挖掘是必要的,以预测来自更大的设计空间的下一代多图像产品。首先,不同的设计思维刺激信息源,并通过在设计和挖掘它背后的动态机制时分析设计人员的思维过程来获得。基于Francisco提出的非线性认知蜘蛛网和天然蜘蛛网结构,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谱捕食行为来影响PICCM。该过程使用遗传算法获得多种后代形式,并且PICCM的机械性能是影响信息中的能量损耗参数。此外,基于设计人员认知的产品人工形式评估系统,从智能设计草图中选择可行的解决方案,重建了一种新的产品图像认知蜘蛛网系统。最后,案例研究表明了所提出的方法的效率和可行性。

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