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Engineering product design specification decomposition method from the perspective of product system evolution

产品设计说明书 组分(热力学) 计算机科学 分解 产品设计 产品(数学) 可靠性工程 工程类 数学 物理 几何学 生物 生态学 热力学
作者
Kai Ma,Haizhu Zhang,Shanqiang Gu,Shengfeng Qin,Rong Li,Guofu Ding
标识
DOI:10.1177/09544054251343422
摘要

An engineering product usually consists of multiple subsystems or modules that determine its overall functionality and performance. In the forward design process, the overall product design specifications must be systematically decomposed into subsystem and component specifications. Traditional design specification decomposition methods focus on optimizing individual products but often overlook interactions among specifications during product system (family) evolution, leading to inaccurate results. This study introduces a novel methodology for decomposing product design specifications through product evolution. First, we conceptualize a product system and its component specifications as technologies and use a relationship matrix to qualitatively characterize their interactions. Then, these interactions are modeled using Lotka-Volterra equations, with coefficients optimized by Runge-Kutta integration and least squares estimation. The finite difference method is used to transform the design specification decomposition into a technology evolution prediction problem. The prediction results quantitatively reveal the evolution of design specifications and the influence of component specifications on system performance, enabling designers to better understand the interaction relationships between component specifications and system performance, accurately decompose design specifications of engineering products, control manufacturing costs, and improve product design efficiency. The methodology’s validity is empirically verified through a case study analyzing energy consumption specification decomposition for high-speed trains. Comparative analysis demonstrated its superior accuracy, with specification deviations of 0.13%, 2.8%, and 13.2% from actual next-generation train values, outperforming existing methods.
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