Fuzzy neural networks for intelligent design retrieval using associative manufacturing features

被引:13
作者
Tsai, CY
Chang, CA
机构
[1] Yuan Ze Univ, Dept Ind Engn & Management, Chungli 320, Taiwan
[2] Univ Missouri, Dept Ind & Mfg Syst Engn, Columbia, MO 65211 USA
基金
美国国家科学基金会;
关键词
soft computing; fuzzy set theory; neural networks; manufacturing features; intelligent design retrieval;
D O I
10.1023/A:1022951430109
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In the conceptual design stage, designers usually initiate a design concept through an association activity. The activity helps designers collect and retrieve reference information regarding a current design subject instead of starting from scratch. By modifying previous designs, designers can create a new design in a much shorter time. To computerize this process, this paper proposes an intelligent design retrieval system involving soft computing techniques for both feature and object association functions. A feature association method that utilizes fuzzy relation and fuzzy composition is developed to increase the searching spectrum. In the mean time, object association functions composed by a fuzzy neural network allow designers to control the similarity of retrieved designs. Our implementation result shows that the intelligent design retrieval system with two soft computing based association functions can retrieve target reference designs as expected.
引用
收藏
页码:183 / 195
页数:13
相关论文
共 22 条
[1]   Recognizing patterns in information retrieval: a memory-based classifier for inferring relevancy [J].
Aradhye, H ;
Heger, AS .
ARTIFICIAL INTELLIGENCE IN ENGINEERING, 1998, 12 (1-2) :99-105
[2]   From fuzzy input requirements to crisp design [J].
Bahrami, Ali ;
Dagli, Cihan H. .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 1993, 8 (01) :52-60
[3]   Model-based design indexing and index learning in engineering design (Reprinted from Proceedings of the Internation Joint Conference on Artificial Intelligence) [J].
Bhatta, SR ;
Goel, AK .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 1996, 9 (06) :601-609
[4]   NEURAL NETWORKS AND THE PART FAMILY MACHINE GROUP FORMATION PROBLEM IN CELLULAR MANUFACTURING - A FRAMEWORK USING FUZZY ART [J].
BURKE, L ;
KAMAL, S .
JOURNAL OF MANUFACTURING SYSTEMS, 1995, 14 (03) :148-159
[5]   FUZZY ART - FAST STABLE LEARNING AND CATEGORIZATION OF ANALOG PATTERNS BY AN ADAPTIVE RESONANCE SYSTEM [J].
CARPENTER, GA ;
GROSSBERG, S ;
ROSEN, DB .
NEURAL NETWORKS, 1991, 4 (06) :759-771
[6]   Using integer linear programming for instruction scheduling and register allocation in multi-issue processors [J].
Chang, CM ;
Chen, CM ;
King, CT .
COMPUTERS & MATHEMATICS WITH APPLICATIONS, 1997, 34 (09) :1-14
[7]   Issues for integrating knowledge in new product development: reflections from an empirical study [J].
Court, AW .
KNOWLEDGE-BASED SYSTEMS, 1998, 11 (7-8) :391-398
[8]   Variant design for mechanical artifacts: A state-of-the-art survey [J].
Fowler, JE .
ENGINEERING WITH COMPUTERS, 1996, 12 (01) :1-15
[9]  
Garza AGD, 1996, KNOWL-BASED SYST, V9, P151, DOI 10.1016/0950-7051(95)01016-5
[10]  
GROSSBERG S, 1976, BIOL CYBERN, V23, P187