[1]杨东升,康 琦,刘 波,等.面向生产系统的残次品主次成因的群体智能分析[J].智能系统学报,2009,4(06):502-507.[doi:10.3969/j.issn.1673-4785.2009.06.006]
 YANG Dong-sheng,KANG Qi,LIU Bo,et al.Swarm intelligence analysis of primary and secondary causes of defective products for manufacturing system[J].CAAI Transactions on Intelligent Systems,2009,4(06):502-507.[doi:10.3969/j.issn.1673-4785.2009.06.006]
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面向生产系统的残次品主次成因的群体智能分析(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第4卷
期数:
2009年06期
页码:
502-507
栏目:
出版日期:
2009-12-25

文章信息/Info

Title:
Swarm intelligence analysis of primary and secondary causes of defective products for manufacturing system
文章编号:
1673-4785(2009)06-0502-06
作者:
杨东升13康    琦12刘    波1汪    镭12吴启迪12
1. 同济大学 电子与信息工程学院 上海 201804; 2.同济大学 嵌入式系统与服务计算教育部重点实验室 上海 201804; 3.上海 经济和信息化委员会, 上海 200040
Author(s):
YANG Dong-sheng13 KANG Qi12 LIU Bo1 WANG Lei12 WU Qi-di12
1. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China; 2. Key Laboratory of Embedded System and Computerservice of Ministry of Education, Tongji University, Shanghai 201804, China; 3. Shanghai Economic and Information Committee, Shanghai 200040, China
关键词:
群体智能半导体制造主次成因智能分析
Keywords:
swarm intelligence semiconductor manufacturing primary and secondary causes intelligent analysis
分类号:
TP18
DOI:
10.3969/j.issn.1673-4785.2009.06.006
文献标志码:
A
摘要:
残次品成因的主次分析与优化是生产质量控制的一个重要环节.以焊球回流工序为例,提出了一种基于群体智能的优化计算理念的残次品主次成因智能分析方法,利用微粒群启发式信息来引导对大量生产数据的分析计算,降低了计算复杂度.通过实际生产数据仿真验证了该方法的有效性.
Abstract:
Understanding and reducing both primary and secondary causes of defective products is an important part of quality control in semiconductor manufacturing. By applying a swarm intelligence optimization concept to the bump reflow process, the authors were able to formulate a novel analytical method for analyzing the primary and secondary causes of defective products. Their method used swarm heuristic information to reduce computational complexity when processing large amounts of production data. Numerical simulations based on actual production data were performed to verify the proposed method. The results showed its validity.

参考文献/References:

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[9]LIU B, WANG L, JIN Y H. An effective PSObased memetic algorithm for flow shop scheduling[J].IEEE Transactions on Systems, Man, and CyberneticsB,2007,37(1):18-27.

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备注/Memo

备注/Memo:
收稿日期:2009-03-18.
基金项目:国家自然科学基金资助项目(70531020,70871091);教育部科学研究重大资助项目(306023);国家自然科学基金委主任基金专项资助项目(G0525002);同济大学青年优秀人才培养行动计划资助项目(2009KJ028).
作者简介:
杨东升,男,1973年生,高级信息技术主管(SIO),博士研究生,主要研究方向为信息安全和群体智能等.主持信息安全相关项目多项,并获上海市科技进步一等奖一项.发表学术论文7篇.
康    琦,男,1980年生,博士,讲师,IEEE会员,IEEE CIS GOLD委员,中国自动化学会和中国人工智能学会会员.主要研究方向为计算智能和智能控制等.出版著作1部,发表学术论文20余篇.
刘    波,男,1979年生,工程师,博士研究生,主要研究方向为系统动力学、物流与供应链管理等.
更新日期/Last Update: 2010-02-22