多工序下刀具磨损的不完备信息系统数据挖掘
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摘要
针对实际生产中多道工序对精刀磨损存在影响,工序间的影响因素相互关联耦合,且不可避免地发生数据缺失的问题,提出一种基于动态层次聚类和相似关系的不完备信息系统数据挖掘方法。通过工序间的关联性将多工序关联耦合因素进行分解,得到相互独立的影响因素。利用层次聚类法将连续属性离散化,并采用基于相似关系的不完备信息系统属性约简算法计算各影响因素的重要度。设定决策表的不相容度阈值,并根据决策表的分类质量动态调节离散化结果获得最优约简属性,进而挖掘影响刀具磨损的多工序关键工艺要素。以轮槽铣刀为例进行了实验分析,验证了所提方法的实用性和有效性。
Aiming at the influence of multistage machining processes on the finishing tool wear, the interrelated coupling between the process factors and the inevitable data loss problems, a data mining method of incomplete information system based on dynamic hierarchical clustering and similarity relation was proposed. The correlated coupling factors were decomposed considering the correlation between different processes, and the independent factors were obtained. The continuous attributes were discretized by using dynamic hierarchical clustering method, and the importance degree of each influencing factor was calculated by using the attribute reduction algorithm of incomplete information system based on similarity relation. The incompatibility threshold of the decision table was set, and the optimal reduction attribute such as the key factors that influence finishing tool wear were obtained by adjusting the discretization result according to the approximation quality of the decision table. The practicality and validity of the proposed method were verified by an illustrative example of slotting cutter.
Aiming at the influence of multistage machining processes on the finishing tool wear, the interrelated coupling between the process factors and the inevitable data loss problems, a data mining method of incomplete information system based on dynamic hierarchical clustering and similarity relation was proposed. The correlated coupling factors were decomposed considering the correlation between different processes, and the independent factors were obtained. The continuous attributes were discretized by using dynamic hierarchical clustering method, and the importance degree of each influencing factor was calculated by using the attribute reduction algorithm of incomplete information system based on similarity relation. The incompatibility threshold of the decision table was set, and the optimal reduction attribute such as the key factors that influence finishing tool wear were obtained by adjusting the discretization result according to the approximation quality of the decision table. The practicality and validity of the proposed method were verified by an illustrative example of slotting cutter.
引文
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[22] JIAO Lei,LIU Xiaojun,LIU Tingyu,et al.Data discretization method for rules discovery of production scheduling[J].Computer Integrated Manufacturing Systems,2016,22(1):257-264(in Chinese).[焦磊,刘晓军,刘庭煜,等.一种面向生产调度规则挖掘的数据离散化方法[J].计算机集成制造系统,2016,22(1):257-264.]
[23] GUPTA A,MEHROTRA K G,MOHAN C.A clustering-based discretization for supervised learning[J].Statistics & probability letters,2010,80(9/10):816-824.
[24] XU Suping,WANG Pingxin,LI Jinhai,et al.attribute reduction:an ensemble strategy[C]//Proceedings of the International Joint Conference on Rough Sets.Berlin,Germany:Springer-Verlag,2017:362-375.
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[2] YAN Jiwang,MURAKAMI Y,DAVIM J P.Tool design,tool wear and tool life[M].Berlin,Germany:Springer-Verlag,2009:117-149.
[3] NAPRSTKOVA N,CAIS J,INGALDI M.Modification of AlSi9CuMnNi alloy by antimony and heat treatment and their influence on tool wear after turning[J].Manufacturing Technology,2016,16(1):209-214.
[4] ISHIGURO T,NAKAMURA K,HAYAKAWA N,et al.Influence of product material property on tool wear in shearing process[J].Journal of the Iron and Steel Institute of Japan,2011,97(3):99-107.
[5] DENKENA B,KOHLER J,MEYER R,et al.Modification of thetool-workpiece contact conditions to influence the tool wear and workpiece loading during hard turning[J].International Journal of Automation Technology,2011,5(3):353-361.
[6] KARPUSCHEWSKI B,BEUTNER M,K?CHIG M,et al.Influence of the tool profile on the wear behaviour in gear hobbing[J].CIRP Journal of Manufacturing Science and Technology,2017,18:128-134.
[7] POSTNOV V V,IDRISOVA Y V,FETSAK S I.Influence of machine-tool dynamics on the tool wear[J].Russian Engineering Research,2016,35(12):936-940.
[8] BAJI? D,CELENT L,JOZI? S.Modeling of theinfluence of cutting parameters on the surface roughness,tool wear and cutting force in face milling in off-line process control[J].Journal of Mechanical Engineering,2012,58(11):673-682.
[9] SAYIT E,ASLANTAS K,?I?EK A.Tool wear mechanism in interrupted cutting conditions[J].Materials and Manufacturing Processes,2009,24(4):476-483.
[10] DEBNATH S,REDDY M M,YI Q S.Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process using Taguchi method[J].Measurement,2016,78:111-119.
[11] MOTORCU A R.Determination of the parameters providing optimum tool life by taguchi technique and observation of tool wear in the machining of AISI1050 and AISI4140 steels with the ceramic cutting tools[J].Journal of the Faculty of Engineering and Architecture of Gazi University,2009,24(4):699-708.
[12] ZHAO T,ZHOU J M,BUSHLYA V,et al.Effect of cutting edge radius on surface roughness and tool wear in hard turning of AISI 52100 steel[J].The International Journal of Advanced Manufacturing Technology,2017,91(9/10/11/12):3611-3618.
[13] FULEMOVA J,JANDA Z.Influence of thecutting edge radius and the cutting edge preparation on tool life and cutting forces at inserts with wiper geometry[J].Procedia Engineering,2014,69:565-573.
[14] ZHOU Xianzhong.Rough set theory and method of knowledge acquisition in incomplete information system[M].Nanjing:Nanjing University Press,2010(in Chinese).[周献中,黄兵,李华雄,等.不完备信息系统知识获取的粗糙集理论与方法[M].南京:南京大学出版社,2010.]
[15] STEFANOWSKI J,TSOUKIàS A.On theextension of rough sets under incomplete information[C]//Proceedings of the International Workshop on Rough Sets,Fuzzy Sets,Data Mining,and Granular-Soft Computing.Berlin,Germany:Springer-Verlag,1999:73-81.
[16] LI Cong,LIANG Changyong,YANG Shanlin.Null values estimation method based on rough set for incomplete information systems[J].Computer Integrated Manufacturing Systems,2009,15(3):604-608,617(in Chinese).[李聪,梁昌勇,杨善林.基于粗糙集的不完备信息系统空值估算方法[J].计算机集成制造系统,2009,15(3):604-608,617.]
[17] LUO Gongzhi,YANG Xiaojiang.Rough analysis model of multi-attribute decision making based on limited similarity dominance relation[J].System Engineering Theory and Practice,2009,29(9):134-140.
[18] PAWLAK Z.Rough sets[J].International Journal of Computer & Information Sciences,1982,11(5):341-356.
[19] THANGAVEL K,PETHALAKSHMI A.Dimensionality reduction based on rough set theory:a review[J].Applied Soft Computing,2009,9(1):1-12.
[20] PANG Jihong,ZHANG Genbao,ZHOU Hongming,et al.Study on reverse mapping of accuracy design quality characteristics for numerical control machine based on rough set[J].Journal of Mechanical Engineering,2012,48(5):101-107.
[21] XU Xuesong.An industrial data reduction method based on immune memory sharing mechanism[J].Computer Integrated Manufacturing Systems,2013,19(11):2864-2870(in Chinese).[徐雪松.基于免疫记忆共享机制的工业数据约简方法[J].计算机集成制造系统,2013,19(11):2864-2870.]
[22] JIAO Lei,LIU Xiaojun,LIU Tingyu,et al.Data discretization method for rules discovery of production scheduling[J].Computer Integrated Manufacturing Systems,2016,22(1):257-264(in Chinese).[焦磊,刘晓军,刘庭煜,等.一种面向生产调度规则挖掘的数据离散化方法[J].计算机集成制造系统,2016,22(1):257-264.]
[23] GUPTA A,MEHROTRA K G,MOHAN C.A clustering-based discretization for supervised learning[J].Statistics & probability letters,2010,80(9/10):816-824.
[24] XU Suping,WANG Pingxin,LI Jinhai,et al.attribute reduction:an ensemble strategy[C]//Proceedings of the International Joint Conference on Rough Sets.Berlin,Germany:Springer-Verlag,2017:362-375.
[25] DENG Jiangao,BIAN Yijie,XU Xukan.Attribute reduction of risk indices in ERP system implementation based on rough set theory[J].Control and Decision,2010,25(11):1742-1746(in Chinese).[邓建高,卞艺杰,徐绪堪.基于粗糙集理论的ERP系统实施风险控制指标属性约简[J].控制与决策,2010,25(11):1742-1746.]