论文标题

朝着计算参数建模中的完整参数范围

Towards computing complete parameter ranges in parametric modeling

论文作者

Tang, Zhihong, Zou, Qiang, Gao, Shuming

论文摘要

在参数设计中,几何模型是通过更改参数模型中的相关参数编辑的,该参数模型通常是在多个参数上依次完成的。如果无需指导允许参数范围可以保证几何约束系统的溶解度,则用户可以为模型的参数分配不正确的参数值,这将进一步导致模型更新的失败。但是,当前的商业CAD系统几乎没有支持适当的参数分配。尽管现有方法可以计算各个参数的允许范围,但它们在处理多参数情况方面遇到困难。特别是,这些方法可能会错过一些可行的参数值,并提供不完整的允许参数范围。为了解决此问题,本文提出了一种自动方法来计算多参数编辑中的完整参数范围。 In the approach, a set of variable parameters are first selected to be sequentially edited by the user;在每次编辑操作之前,将变量参数的一维范围作为指导。为了计算一维范围,每个变量参数均表示为相等限制的函数,并且通过计算功能范围来获得其一维允许范围。为了有效地获得几乎无法以正常方式计算的函数范围,将功能范围问题转换为约束优化问题,然后通过Lagrange乘数法和奈奇粒子群体群优化算法(Nichepso)求解。通过几个实验结果验证了所提出方法的有效性和效率。

In parametric design, the geometric model is edited by changing relevant parameters in the parametric model, which is commonly done sequentially on multiple parameters. Without guidance on allowable parameter ranges that can guarantee the solvability of the geometric constraint system, the user could assign improper parameter values to the model's parameters, which would further lead to a failure in model updating. However, current commercial CAD systems provide little support for the proper parameter assignments. Although the existing methods can compute allowable ranges for individual parameters, they face difficulties in handling multi-parameter situations. In particular, these methods could miss some feasible parameter values and provide incomplete allowable parameter ranges. To solve this problem, an automatic approach is proposed in this paper to compute complete parameter ranges in multi-parameter editing. In the approach, a set of variable parameters are first selected to be sequentially edited by the user; before each editing operation, the one-dimensional ranges of the variable parameters are presented as guidance. To compute the one-dimensional ranges, each variable parameter is expressed as an equality-constrained function, and its one-dimensional allowable range is obtained by calculating the function range. To effectively obtain the function range which can hardly be calculated in a normal way, the function range problem is converted into a constrained optimization problem, and is then solved by Lagrange multiplier method and the Niching particle swarm optimization algorithm (the NichePSO). The effectiveness and efficiency of the proposed approach is verified by several experimental results.

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