基于C5.0改进算法的焊接工艺参数选择决策树数据挖掘模型及其应用
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- 英文论文题名：The Decision Tree Data Mining Model for Welding Parameters Selection Based on C5.0 Improved Algorithm and Its Application
- 论文作者：赵丽 ; 程铁信 ; 莫莹 ; 李奇 ; 刘铭杰
- 英文论文作者：ZHAO Li ; CHENG Tie-xin ; MO Ying ; LI Qi ; LIU Ming-jie ; College of Management ; Tianjin Polytechnic University
- 年：2016
- 作者机构：天津工业大学管理学院;
- 论文关键词：数据挖掘 ; 压力容器 ; 焊接 ; 质量控制
- 英文论文关键词：data mining ; pressure vessel ; welding ; quality control
- 会议召开时间：2016-11-12
- 会议录名称：第十八届中国管理科学学术年会论文集
- 英文会议录名称：Proceedings of the 18th Chinese Annual Conference on Management Science
- 语种：中文
- 分类号：TG457.5;TP311.13
- 学会代码：ZHYJ
- 会议名称：第十八届中国管理科学学术年会
- 会议地点：中国陕西西安
- 主办单位：中国优选法统筹法与经济数学研究会、西安交通大学、中国科学院科技战略咨询研究院、《中国管理科学》编辑部
- 学会名称：中国优选法统筹法与经济数学研究会
- 页数：6
- 文件大小：1203k
- 原文格式：O
- 会议级别：全国
摘要
焊接变形对焊接工艺参数的选择依赖性很强,在影响焊接的各种因素中占主导地位。本文在经典ID3算法的基础上,建立了决策树数据挖掘模型并给出了决策树的C5.0改进算法,并将该模型应用于焊接工艺参数选择。针对具有代表性的压力容器焊接工艺设计过程,综合分析各种工艺参数对焊接变形的影响,挖掘出能够提升焊件质量的焊接工艺参数,降低焊接缺陷。然后,将该模型与神经网络、Logistic回归模型比较,结果显示该模型和算法具有较高准确度,可以对压力容器焊接质控提供很好的决策支持。
Welding deformation strongly depends on welding parameters selection,which can affect the welding quality.The decision tree data mining model is established for welding parameters selection and the C5.0 improved algorithm for the model is given on the basis of the classic ID3 algorithm.With the model,the typical pressure vessel welding process is analyzed,and the welding process parameters are correctly selected,which can improve the quality of welding and reduce welding defects.Then,compared with neural networks,Logistic regression model,the model above is Uustrated with higher accuracy,which can provide the decision making support for pressure vessel welding quality control.
Welding deformation strongly depends on welding parameters selection,which can affect the welding quality.The decision tree data mining model is established for welding parameters selection and the C5.0 improved algorithm for the model is given on the basis of the classic ID3 algorithm.With the model,the typical pressure vessel welding process is analyzed,and the welding process parameters are correctly selected,which can improve the quality of welding and reduce welding defects.Then,compared with neural networks,Logistic regression model,the model above is Uustrated with higher accuracy,which can provide the decision making support for pressure vessel welding quality control.
引文
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[18]柯新利,边馥苓.基于C5.0决策树算法的元胞自动机土地利用变化模拟模型[J].长江流域资源与环境,2010,19(4):403.
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[2]刘彩梅.压力容器焊接质量控制[J].化学工程与装备,2010,(8):77-80.
[3]章晓春,李如光,李小建.浅淡如何控制压力容器焊接质量[J].化工装备技术,2011,32(5):59-62.
[4]廖庆富.数据挖掘在提高SMT焊接质量中的应用研究[D].广州:广东工业大学,2013.
[5]钱学君,胡小建.决策树算法在焊接工艺设计中的应用[J].焊接技术,2009,38(6):5-8.
[6]董小格.基于数据挖掘的焊件质量诊断技术研究[D].大庆:东北石油大学,2012.
[7]Chao yongsheng,Sun wenlei.Welding type oriented association rules acquisition based on rough sets[J].Computer Engineering and Applications,2015,51(15):244-8 260.
[8]Zhang hongjie,Houyanyan,Zhang jianye.A new method for nondestructive quality evaluation of the resistance spot welding based on the radar chart method and the decision tree classifier[J].international Journal of Advanced Manufacturing Technology,2015,78(5-8):841-851.
[9]He Shuguang,Wang G A,Zhang Min.Multivariate process monitoring and fault identification using multiple decision tree classifiers[J].International Journal of Production Research,2013,51(15):3355-71.
[10]Akbar H.Suryana N,Akbar F.Surface defect detection and classification based on statistical filter and decision tree[J].International Journal of Computer Theory and Engineering,2013,5(5):774-9.
[11]Ratnayake R M C.An algorithm to prioritize welding quality deterioration factors:a case study from a piping component fabrication process[J].International Journal of Quality&Reliability Management,2013,30(6):616-38.
[12]Nan TT.Development of a soldering quality classifier system using a hybrid data mining approach[J].Expeet Systems With Applications,2012,39(5):5727-5738.
[13]Fakhr M.Elsayad A M.Steel plates faults diagnosis with data mining models[J].Journal of Computer Science,2012,8(4):506-14.
[14]张亮,张玲玲,陈懿冰,等.基于信息融合的数据挖掘方法在公司财务预警中的应用[J].中国管理科学,2015,23(10):170-176.
[15]肖进,唐静,刘敦虎,等.基于改进GMDH的目标客户选择模型研究[J].中国管理科学,2015,23(10):161-169.
[16]闫业良,李晓泉,邹华.大厚壁压力容器封头焊接工艺研究[J].焊接技术,2014,12:35-37+5.
[17]冯秀荣,冷静,刘海.C5.0决策树算法在移动阅读用户流失中的应用[J].北京信息科技大学学报,2016,31(1):84-89.
[18]柯新利,边馥苓.基于C5.0决策树算法的元胞自动机土地利用变化模拟模型[J].长江流域资源与环境,2010,19(4):403.
[19]马鸣,张亚奇,李春光.低温钢制压力容器焊接工艺综述[J].机械制造文摘(焊接分册),2011,(6):28-32.