基于约束的协同设计冲突检测模型

Conflict detection model in collaborative design based on constraint

针对协同设计冲突无法准确全面检测的问题,提出了一种基于约束的冲突检测模型。在分析了协同设计中约束分层和约束满足问题的基础上,该检测模型将约束划分为已知约束关系集合和未知约束关系集合两部分,分别对其进行冲突检测。采用区间传播算法验证已知约束关系集合;提出用免疫算法优化反向传播(BP)神经网络来模拟未知约束关系集合进行冲突检测,并与遗传算法优化BP神经网络进行对比,收敛速度提高了62.96%,证明了算法具有较快的收敛速度和较强的全局收敛能力。为实现计算机支持的冲突检测,研究了基于可扩展标记语言(XML)文档的约束关系集合表达方法,设计了基于约束满足的冲突检测系统的架构体系,并以C#和Matlab为平台开发了行星齿轮箱协同设计冲突检测系统。最后,通过实例验证了冲突检测模型的可行性和有效性。

Focusing on the issue that conflict is hard to detect accurately and comprehensively in collaborative design, a conflict detection model based on constraint was proposed. Considering the hierarchical constraints and constraint satisfaction, the detection model divided constraints into two sets： one set is with known constraints and the other set is with unknown constraints. The constraints of two sets were detected respectively. The set with known constraints was detected by interval propagation algorithm. Meanwhile, Back Propagation （BP） neural network was used to detect the set with unknown constraints. Immune Algorithm （IA） was utilized to optimize the weights and thresholds of BP neural network, and the steps of optimization process were put forward. In the comparison experiments with BP neural network optimized by Genetic Algorithm （ GA）, the convergent speed was increased by 69.96%, which indicated that BP neural network optimized by IA has better performance in convergent speed and global searching ability. The constraints were described by eXtensible Markup Language （XML）, so that computers could automatically recognize and establish the constraint network. The implementation of conflict detection system based on constraint satisfaction was designed. Taking co-design of wind planetary gear train as an example, a conflict detection system in collaborative design was developed on Matlab with C#. The conflict detection model is proved to be feasible and effective, and provides a solution of conflict detection for collaborative design.