Feasible Change Path Search and Optimization Method 
Based on Driving Parameter Modeling

Abstract

Abstract: To solve the problem of difficultly analyzing the feasible change paths in the product design, a feasible change path search and optimization method was proposed based on drivenparameter modeling. The phenomenon of change propagation was described from the viewpoint of set theory. Based on traditional product parameter modeling, a product drivenparameter associated network was constructed, and the preprocess approach of product model was introduced. The depth first search (DFS) algorithm was adopted to realize the search of feasible change paths. The assessment indexes and criterions were proposed to instruct the selection of optimal change path, and technique for order preference by similarity to an ideal solution (TOPSIS) method was utilized to select the optimal change path. A case was provided to verify the feasibility of the method by a developed prototype.

Key words: design change, driving parameter, feasible change path, path search

CLC Number:

TP 391

CHEN Jinping1,ZHANG Shusheng1,HE Weiping1,WANG Mingwei1,HUANG Hui2. Feasible Change Path Search and Optimization Method
Based on Driving Parameter Modeling[J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1220-1227.

References

［1］YANG F, DUAN G J. Developing a parameter linkagebased method for searching change propagation paths［J］. Research in Engineering Design, 2011, 23(4): 353372.
［2］CLARKSON P J, SIMONS C, ECKERT C. Predicting change propagation in complex design［J］. Journal of Mechanical Design, 2004, 126(5): 788797.
［3］宫中伟, 莫蓉, 杨海成, 等. 基于经验的工程变更传播路径预测方法［J］. 航空学报, 2013, 34(3): 677685.
GONG Zhongwei, MO Rong, YANG Haicheng, et al. Historybased engineering change propagation predicting［J］. Acta Aeronautica Et Astronautica Sinica, 2013, 34(3): 677685.
［4］LI Y L, ZHAO W, MA Y S. A shortest path method for sequential change propagations in complex engineering design processes［J］. Artificial Intelligence for Engineering Design Analysis & Manufacturing, 2016, 30(1): 107121.
［5］王祯.基于本体的机械产品工程变更影响分析方法研究［D］. 济南：山东大学机械工程学院, 2015.
［6］刘夫云, 祁国宁. 配置产品尺寸参数传递方法及其应用［J］. 机械工程学报, 2007, 43(4): 144151.
LIU Fuyun, QI Guoning. Configuration product size parameter method and its application［J］. Journal of Mechanical Engineering, 2007, 43(4): 144151.
［7］耿杰, 蔡伯根, 王剑,等. 基于深度优先搜索的铁路站场遍历算法研究［J］. 铁道学报, 2012, 34(4): 5156.
GENG Jie, CAI Bogen, WANG Jian, et al. Traversing algorithm of railway yard based on DFS［J］. Journal of the China Railway Society, 2012, 34(4): 5156.
［8］MEHTA C, PATIL L, DUTTA D. An approach to predict impact of proposed engineering change effect［J］. Journal of Computing & Information Science in Engineering, 2012, 12(2): 0210090102100908.
［9］CHEN T Y. The inclusionbased TOPSIS method with intervalvalued intuitionistic fuzzy sets for multiple criteria group decision making［J］. Applied Soft Computing, 2015, 26(1): 5773.
［10］杨帆, 唐晓青, 段桂江. 基于特性关联网络模型的机械产品工程更改传播路径搜索方法［J］. 机械工程学报, 2011, 47(19): 97106.

[1]	YANG Zhen,FU Zhuang,GUAN Enguang,XU Jiannan,TIAN Shihe,ZHENG Hui. The Kinematic Analysis and Structure Optimization of MLattice Modular Robot [J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1153-1159.
[2]	ZHAO Jun1,YU Haidong2. Dynamic Analysis of TwoLink Flexible Manipulators Based on the Absolute Nodal Coordinate Formulation [J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1160-1165.
[3]	ZHAO Ziren1,DU Shichang1,HUANG Delin1,REN Fei2,LIANG Xinguang2. Modelling and Bottleneck Analysis of Product Quality in Transient Phase of MultiStage Manufacturing Systems Based on Markovian Chains [J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1166-1173.
[4]	HUANG Xuangui. Algorithm for Relatively Small Planted Clique with Small Edge Probability [J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1202-1206.
[5]	LUO Jingjinga,YU Haidonga,ZHAO Chunzhanga,b,WANG Haoa,b. Study on Motion Stability of Variable CrossSection Flexible Beams Based on the Absolute Nodal Coordinate Formulation [J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1174-1180.
[6]	WANG Yibo1,HUANG Yixiang1,LI Bingchu1,LING Xiao1 ZHAO Shuai1,LIU Chengliang1,ZHANG Daqing2. An Improved Modal Simulation Method for Switched Reluctance Motor Based on Static PreComputation [J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1181-1188.
[7]	ZHOU Binghai,LI Ming. Scheduling Method of Manufacturing Cells with Robot Restricted Processing [J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1214-1219.
[8]	ZHOU Penghui,MA Hongzhan,CHEN Dongping,CHEN Mengyue,CHU Xuening. Identification of Product Redesign Modules Based on Fuzzy Random Failure Mode and Effects Analysis [J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1189-1195.
[9]	PENG Cheng,ZHU Jianyun,CHEN Li. Mode Transition for a Hybrid Electric Vehicle Based on Model Reference Control [J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1196-1201.
[10]	LIU Wei,YANG Chao . A Study on Injection Mould Part Quotation Model Based on Back Propagation Neural Network [J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1207-1213.
[11]	CHEN Suting，WANG Zhuo，WANG Qi. Aerial Scene Classification Based on Nonlinear Scale Space [J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1228-1234.
[12]	CHEN Ning,HE Xiaobin,GUI Weihua,YANG Chunhua. Research on Image Encryption System Based on Chaotic Discrete Sequence [J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1273-1280.
[13]	LIU Kaia，ZHANG Liminb，ZHOU Lijuna. Design of Random Restricted Boltzmann Machine Group [J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1235-1240.
[14]	ZHU Xinyao1，SONG Baowei2,XU Gang1,YANG Songlin1. Research on Landing Strategy and Influencing Factors of an Autonomous Underwater Vehicle with Supporting Mechanism [J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1241-1251.
[15]	KONG Xiaobing1,2,FAN Chang2,LIU Xiangjie1. Distributed Supervisory Predictive Control of Main Steam Temperature for UltraSupercritical Unit [J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1252-1259.