The case retrieval process will be simultaneously speeded up by restricting the research space into a subgroup of designs. 5. Summary In this paper, a fuzzy-rough approach for stamping die design knowledge discovery is proposed. Its an attempt to mine die design knowledge from various resources, including existent successful die designs, journal papers and so on. The knowledge mining module can work together with convectional RBR or CBR system and improve their performance. References [1] Cheok, B.T., A.Y.C. Nee, Trends and developments in the automation of design and manufacture of tools for metal stampings. Journal of Materials Processing Technology, 1998. 75(1-3): p. 240-252. [2] Tang, D.B., L. Zheng, Z.Z. Li, An intelligent feature-based design for stamping system. International Journal of Advanced Manufacturing Technology, 2001. 18(3): p. 193-200. [3] Tor, S.B., G.A. Britton, W.Y. Zhang, Indexing and Retrieval in Metal Stamping Die Design Using Case-based Reasoning. Journal of Computing and information Science in Engineering, 2003. 3(4): p. 353~362. [4] Park, C.-S., I. Han, A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems with Applications, 2002. 23(3): p. 255-264. [5] Salomons, O.W., F.J.A.M. Van Houten, H.J.J. Kals, Review of research in feature-based design. Journal of Manufacturing Systems, 1993. 12(2): p. 113-132. [6] Chang, H.C., et al., Indexing and retrieval in machining process planning using case-based reasoning. Artificial Intelligence in Engineering, 2000. 14(1): p. 1. [7] Hongji, L., Basic fuzzy mathematics with applied algorithm. 2005, Beijing: Science Publisher. [8] Cao, G., S.C.K. Shiu, X. Wang, A fuzzy-rough approach for the maintenance of distributed case-based reasoning systems. Soft Computing, 2003. 7(8): p. 491-499. [9] Pawlak, Z., Decision rules, Bayes' rule and rough sets, in New Directions In Rough Sets, Data Mining, And Granular-Soft Computing. 1999. p. 1-9. [10] Fdez-Riverola, F., F. Diaz, J.M. Corchado, Applying rough sets reduction techniques to the construction of a fuzzy rule base for case based reasoning. Advances in Artificial Intelligence - IBERAMIA 2004. 9th Ibero-American Conference on AI. Proceedings (Lecture Notes in Artificial Intelligence Vol.3315), 2004: p. 83-92. [11] Øhrn, A., Discernibility and Rough Sets in Medicine: Tools and Applications, in Department of Computer and Information Science. 1999, Norwegian University of Science and Technology: Trondheim. ¯
摘要：在针对传统的情况下检索技术模具设计的缺点，一个基于粗糙集的情况下，检索方法示于模具的设计。分析和处理使用粗糙集理论模案例库，并且通过使用等级分级使用的方法和决策属性支持度的离散数量特征。它证实的重要程度所有类型的特征属性。致力于建立一个基于案例的关键属性检索方法。它使用相似的方法来进行相似度检索，检索从最接近外壳设计的情况下的数据库采取作为设计案例参考。建议方法也证明了一个应用实例。该技术保证了案例检索的有效性,降低了系统的依赖，提高了效率案例检索。论文网
1.简介
基于案例推理设计方法（CBR）在过去式案例中的价值和那些关于设计基准来解决新的设计问题的经验，克服了知识获取的瓶颈，解决的复杂的问题，。案例检索的关键技术CBR。它获取的效率影响较大答案和推理准确性。由于设计案例有很多特征属性的重要性度是不同的，加权系数是困难的计算，我们通常得到的假设的结果平均或加权系数法的基础上经验和知识。因此，这个结果不能证明其正确性和保证的情况下检索的有效性。粗糙集理论（RST）用于处理权案件的系数问题属性。它利用现有信息来计算的重要性所有属性的程度，并提供客观，对于类似的匹配合理的证据[1]。因此，本文提出了基于案例检索技术粗糙集理论，实现了类似的匹配对象的情况下和原始壳体之间。 冲压模具设计挖掘规则英文文献和中文翻译(4):http://www.751com.cn/fanyi/lunwen_33960.html