印刷电路板表面贴装生产过程质量监控系统的研究
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摘要
当前电子产品朝着小型化、薄型化、批量小、品种多的方向发展,使得现代表面贴装技术(Surface Mounting Technology,SMT)的生产过程变得越来越复杂,传统产品质量管理渐渐变得难以适应日趋严格的质量管理要求。SMT生产线的数据采集系统中储存了海量的数据,这些数据背后隐含了大量的知识,而数据挖掘可以从大量的数据中智能地、自动地提取有价值的知识和信息。本论文针对现代SMT生产线的特点,从传统品质管理系统引入数据挖掘技术来研究和开发一种开放的、可扩展的、智能化的新型SMT生产的品质监控系统。
本文引入数据挖掘技术的决策树算法,在分析SMT生产工艺的基础上建立了挖掘生产线数据的流程。然后,提出了一种离散化算法使决策树算法能处理连续值属性,并且引入了AdaBoost强化学习机思想来提高算法的精度。最后,获得改进的决策树算法可以有效地分析SMT生产线的历史数据,获取工艺参数评价的标准,辅助工作人员快速优化工艺参数。
本文研究关联规则技术中的Apriori算法,引入有监督学习的思想,对Apriori算法进行改进,并应用于产品缺陷诊断。与原有Apriori算法相比,本文的算法效率更高,可以帮助工作人员快速解决质量缺陷问题,缩短解决质量异常的时间。
最后,本文在Visual Basic.NET 2005集成开发环境,采用大型数据库系统SQL Server 2005结合与网络通讯技术对表面贴装技术产品质量监控系统进行了软件实现。并将系统应用到现场生产线上进行实验,实验结果表明了本系统的工艺参数评价功能有较高的准确度和缺陷诊断功能有良好的实用性。
With electronic products's development orientation toward smaller, thinner, small quantity and variety, Surface Mounting Technology (SMT) manufacturing process becomes more complex, traditional quality management becomes more and more difficult to adapt to the increasingly stringent quality management requirements. The SMT production line data acquisition system stored a mass amount of data, which implied a lot of knowledge; data mining can intelligently and automatically extract the valuable knowledge and information hidden in the large database. This thesis focuses on the specialty of modern SMT production line and the traditional production quality management system, using data mining to research and develop an open, scalable, intelligent new SMT production quality control system.
This thesis introduced the decision tree algorithm of data mining technology, and based on the analysis of SMT production line to set up the flow of the mining production line data. Then, the discretization algorithm is proposed to let the decision tree algorithm in general be capable of handling continuous-valued attributes, also the AdaBoost reinforcement learning machine algorithm is introduced to improve the accuracy of the algorithm. Finally, we achieved an improved decision tree algorithm, which can effectively analyse the historical data of SMT production line to get the criteria evaluation of process parameters to support technicians for an optimization of the process parameters.
In this thesis the author did a research about the application of Apriori algorithm, introduced the idea of supervised learning to improve the Apriori algorithm, and applied to diagnose the product defects. Finally, the improved algorithm we achieved is more efficient than the original Apriori algorithm, and can assist the technicians to faster solve the quality defects problem.
Finally, in this thesis the development of the product quality control system software is base on the combination of Visual Basic.NET 2005 environment, SQL Server 2005 database system and network communication technology. After the experiment of the system on the real product line, the results indicated that the evaluation of process parameters function has a high accuracy and the defect diagnosis function has the right practicability.
本文引入数据挖掘技术的决策树算法,在分析SMT生产工艺的基础上建立了挖掘生产线数据的流程。然后,提出了一种离散化算法使决策树算法能处理连续值属性,并且引入了AdaBoost强化学习机思想来提高算法的精度。最后,获得改进的决策树算法可以有效地分析SMT生产线的历史数据,获取工艺参数评价的标准,辅助工作人员快速优化工艺参数。
本文研究关联规则技术中的Apriori算法,引入有监督学习的思想,对Apriori算法进行改进,并应用于产品缺陷诊断。与原有Apriori算法相比,本文的算法效率更高,可以帮助工作人员快速解决质量缺陷问题,缩短解决质量异常的时间。
最后,本文在Visual Basic.NET 2005集成开发环境,采用大型数据库系统SQL Server 2005结合与网络通讯技术对表面贴装技术产品质量监控系统进行了软件实现。并将系统应用到现场生产线上进行实验,实验结果表明了本系统的工艺参数评价功能有较高的准确度和缺陷诊断功能有良好的实用性。
With electronic products's development orientation toward smaller, thinner, small quantity and variety, Surface Mounting Technology (SMT) manufacturing process becomes more complex, traditional quality management becomes more and more difficult to adapt to the increasingly stringent quality management requirements. The SMT production line data acquisition system stored a mass amount of data, which implied a lot of knowledge; data mining can intelligently and automatically extract the valuable knowledge and information hidden in the large database. This thesis focuses on the specialty of modern SMT production line and the traditional production quality management system, using data mining to research and develop an open, scalable, intelligent new SMT production quality control system.
This thesis introduced the decision tree algorithm of data mining technology, and based on the analysis of SMT production line to set up the flow of the mining production line data. Then, the discretization algorithm is proposed to let the decision tree algorithm in general be capable of handling continuous-valued attributes, also the AdaBoost reinforcement learning machine algorithm is introduced to improve the accuracy of the algorithm. Finally, we achieved an improved decision tree algorithm, which can effectively analyse the historical data of SMT production line to get the criteria evaluation of process parameters to support technicians for an optimization of the process parameters.
In this thesis the author did a research about the application of Apriori algorithm, introduced the idea of supervised learning to improve the Apriori algorithm, and applied to diagnose the product defects. Finally, the improved algorithm we achieved is more efficient than the original Apriori algorithm, and can assist the technicians to faster solve the quality defects problem.
Finally, in this thesis the development of the product quality control system software is base on the combination of Visual Basic.NET 2005 environment, SQL Server 2005 database system and network communication technology. After the experiment of the system on the real product line, the results indicated that the evaluation of process parameters function has a high accuracy and the defect diagnosis function has the right practicability.
引文
[1]陈世哲.微电子产品视觉检测中关键技术研究[D].哈尔滨:哈尔滨工业大学工学博士学位论文, 2006
[2]张文典.实用表面组装技术[M].北京:电子工业出版社, 2002
[3] Bothe D.R. JIT Calls for SPC: A Control Chart for Short Production Runs[J]. Job Shop Technology, 1987: 21-23
[4] Quesenberry C.P. SPC Q Charts for A Binomial Parameter p: Short or Long Runs[J]. Journal of Quality Technology, 1991, 23(3): 239-246
[5] Del Castillo E.; Montgomery D.C. Short Run Statistical Process Control: Q Chart Enhancements and Altemative Methods[J]. Quality and Reliablity Engineering Intenational, 1994, 10(2): 87-97
[6]崔敬巍,谢里阳.面向小批量制造环境的控制图理论的研究[J].组合机床与自动化加工技术, 2006: 1-4
[7] Philip W.W.; John C.N. An application of Bayesian methods in SPC[J]. The Statistician, 1993, 42(4): 461-469
[8]余忠华,吴昭同.面向小批量制造过程的质量控制方法研究[J].机械工程学报, 2001, 37(8): 61-65
[9]刘笑男.面向多品种小批量微小偏差的SPC及应用研究[D].天津:天津大学, 2008
[10]邓士娟.基于数据挖掘技术的轴承寿命预测的研究[D].大连:大连海事大学, 2006
[11]前锋.基于数据挖掘技术的地铁盾构机故障诊断方法研究[D].上海:上海大学, 2007
[12] JiaWei Han, Micheline Kamber. Data Mining:Concepts and Techniques[M]. Morgan Kaufmann Publishers, 2000
[13] Michael Halasz, Francois Dube, Bob Orchard. The Integrated Diagnostic System (IDS): Remote Monitoring and Decision Support for Commercial Aircraft - Putting Theory into Practice[J]. AAAI Technical Report, 1999, 04: 88-94
[14] Aleksander Ohrn, Komorowski J. ROSETTA: A Rough Set Toolkit for Analysis of Data, Proc[J]. Third International Joint Conference on Information Sciences, 1997, 3: 403-407
[15] Terje Loken. Vibration Analysis using ROSETTA A Practical Application of RoughSet[D]. Norway: Normegian University of Science and Technology, 1998
[16] Marte Skarstein Bjanger. Vibration Analysis in Rotating Machinery using Rough Set theory and ROSETTA[D]. Norway: Normegian University of Science and Technology, 1999
[17]余忠华,吴昭同.面向多品种、小批量制造环境的SPC实施模型的研究[J].工程设计, 2000, (3): 28-31
[18] Taho Yang and Tsung-Nan Tsai. Modeling and implementation of a neurofuzzy system for surface mount assembly defect prediction and control[J]. IIE Transactions, 2002(34):637-646
[19] Soman K.P., Shyam Diwakar, Ajay V. Insight into Data Mining Theory and Practice[M].北京:机械工业出版社, 2009
[20] Cecil J., Erraguntla M., Mayer R. A fuzzy inferential approach for the diagnosis of defects in electronics assembly[J]. Proceedings of the IEEE/CPMT International Electronics Manufacturing Technology Symposium, La Jolla, CA, 1994: 70-74
[21] Breiman L.; Friedman J.H.; Olshen R.A.; et al. Classification and Regression Trees[J], 1984
[22] Ignizio J.P. An Introduction to Expert Systems: The Development and Implementation of Rule Based Expert Systems[J], McGraw Hill, 1991: 402
[23] Dougherty J.; Kohavi R.; Sahami M. Supervised and Unsupervised Discretization of Continuous Features[J]. Proc Twelfth International Conference on Machine Learning, Morgan Kaufmann, CA, 1995
[24] Kearns M.; Valiant L.G. Learning boolean formulae or factoring[J]. Technical Report TR-1488, 1988
[25] Schapire R.E. The Strength of weak learnability[J]. Machine Learning, 1990, 5(2): 197-227
[26] Freund Y.; Schapire R.E. A decision-theoretic generaliation of on-line learning and an application to boosting[J]. Journal of Computer and System Sciences, 1997, 55(1): 119-139
[27]万鹏.机械故障诊断中基于神经网络的数据挖掘方法研究[D].湖北:湖北工业大学硕士学位论文, 2006
[28]祝丽华,姚竹亭.基于数据挖掘的电路板故障诊断系统设计[J].工业控制计算机, 2009, 22(9): 42-43
[29]张朝晖,张跋.挖掘多值属性的关联规则[J].软件学报, 1998(11): 801-805
[30]董连京.应用于SMT行业先进的在线检测设备—自动光学检查系统[J].国内外机电一体化技术, 2002,5(3): 33-36
[31]贾忠中. SMT工艺质量控制[M].北京:电子工业出版社, 2007
[32]陆志香.数据挖掘技术及其应用[J].科技广场, 2008, 8
[33]王桂芹,黄道.数据挖掘技术综述[J].电脑应用技术, 2007, 69
[34]钮轶君,钱省三,任建华.基于数据挖掘的半导体制造质量异常研究[J].半导体技术, 2006, 12(31)
[35]苏圣超,张正道,朱大奇.基于时间序列数据挖掘的旋转机械故障预报[J].南京航空航天大学学报, 2006, 7(38)
[36]何小龙,吴先献.对电子元件质量监督抽查结果的综合分析[J].电子产品可靠性与环境试验, 2002, 12(6): 38-39
[37]邵峰晶,于忠清.数据挖掘原理与算法[M].北京:中国水利水电出版社, 2003
[38]朱德利. SQL Server 2005数据挖掘与商业智能完全解决方案[M].北京:电子工业出版社, 2007
[39]李春葆,曾平.数据库原理与应用——基于SQL Server 2000[M].北京:清华大学出版社, 2006
[40]钟朝嵩.品质管理大全(上)[M].厦门大学出版社, 2005
[41]俞明南,丁正平.质量管理[M].大连理工大学出版社, 2005
[42]文放怀. SPC实战——新工厂管理[M].广州:广东经济出版社, 2005
[43]王金鹤,李玉红.基于电子标签的SPC过程控制分析[J].微计算机信息, 2008, 7(24)
[44]赵利伟.基于数据采集的SPC系统的研究[D].青岛科技大学机械制学院, 2007
[45]殷建军,余忠华,吴昭同.基于现场总线的嵌入式SPC系统研究[J].浙江大学学报, 2004, 6(38)
[46]傅明,阚树林,张赋杰.基于SPC的焊接生产线质量控制及相关分析[J].现代制造工程, 2008, 1
[47]张宪民,卢盛林.一种用于电路板自动光学检测设备的光源装置[P].中国:200710031093.4, 2007
[48]张宪民,卢盛林.无铅焊点检测的光源分析与优化设计[J].华南理工大学学报自然科学版, 2008.3
[49]卢盛林,张宪民,邝泳聪.基于神经网络的PCB焊点检测方法[J].华南理工大学学报(自然科学版), 2008, 5(36): 135-139
[50]卢盛林,张宪民.无铅焊点检测光源的分析与优化设计[J].光学精密工程, 2008, 8(16): 1376-1383
[51] Lu Sheng-lin, Zhang Xian-min. Analysis and optimal design of illuminator for leadless tin solder joint inspection[J].Optics and Precision Engineering, 2008, 8: 1377-1383
[52] Driels M.R.; Nolan D.J. Automatic defect classification of printed wiring board solder joints [J]. IEEE Transactions on components, hybrids, and manufacturing technology, 1990, 13(2) 331-340
[53]刘保顺. Visual Basic .Net数据库开发[M].清华大学出版社,2004
[54]张牧笛,罗驱波,白静宜. ADO.NET数据访问模型研究[J].微计算信息, 2008,24:78-80
[2]张文典.实用表面组装技术[M].北京:电子工业出版社, 2002
[3] Bothe D.R. JIT Calls for SPC: A Control Chart for Short Production Runs[J]. Job Shop Technology, 1987: 21-23
[4] Quesenberry C.P. SPC Q Charts for A Binomial Parameter p: Short or Long Runs[J]. Journal of Quality Technology, 1991, 23(3): 239-246
[5] Del Castillo E.; Montgomery D.C. Short Run Statistical Process Control: Q Chart Enhancements and Altemative Methods[J]. Quality and Reliablity Engineering Intenational, 1994, 10(2): 87-97
[6]崔敬巍,谢里阳.面向小批量制造环境的控制图理论的研究[J].组合机床与自动化加工技术, 2006: 1-4
[7] Philip W.W.; John C.N. An application of Bayesian methods in SPC[J]. The Statistician, 1993, 42(4): 461-469
[8]余忠华,吴昭同.面向小批量制造过程的质量控制方法研究[J].机械工程学报, 2001, 37(8): 61-65
[9]刘笑男.面向多品种小批量微小偏差的SPC及应用研究[D].天津:天津大学, 2008
[10]邓士娟.基于数据挖掘技术的轴承寿命预测的研究[D].大连:大连海事大学, 2006
[11]前锋.基于数据挖掘技术的地铁盾构机故障诊断方法研究[D].上海:上海大学, 2007
[12] JiaWei Han, Micheline Kamber. Data Mining:Concepts and Techniques[M]. Morgan Kaufmann Publishers, 2000
[13] Michael Halasz, Francois Dube, Bob Orchard. The Integrated Diagnostic System (IDS): Remote Monitoring and Decision Support for Commercial Aircraft - Putting Theory into Practice[J]. AAAI Technical Report, 1999, 04: 88-94
[14] Aleksander Ohrn, Komorowski J. ROSETTA: A Rough Set Toolkit for Analysis of Data, Proc[J]. Third International Joint Conference on Information Sciences, 1997, 3: 403-407
[15] Terje Loken. Vibration Analysis using ROSETTA A Practical Application of RoughSet[D]. Norway: Normegian University of Science and Technology, 1998
[16] Marte Skarstein Bjanger. Vibration Analysis in Rotating Machinery using Rough Set theory and ROSETTA[D]. Norway: Normegian University of Science and Technology, 1999
[17]余忠华,吴昭同.面向多品种、小批量制造环境的SPC实施模型的研究[J].工程设计, 2000, (3): 28-31
[18] Taho Yang and Tsung-Nan Tsai. Modeling and implementation of a neurofuzzy system for surface mount assembly defect prediction and control[J]. IIE Transactions, 2002(34):637-646
[19] Soman K.P., Shyam Diwakar, Ajay V. Insight into Data Mining Theory and Practice[M].北京:机械工业出版社, 2009
[20] Cecil J., Erraguntla M., Mayer R. A fuzzy inferential approach for the diagnosis of defects in electronics assembly[J]. Proceedings of the IEEE/CPMT International Electronics Manufacturing Technology Symposium, La Jolla, CA, 1994: 70-74
[21] Breiman L.; Friedman J.H.; Olshen R.A.; et al. Classification and Regression Trees[J], 1984
[22] Ignizio J.P. An Introduction to Expert Systems: The Development and Implementation of Rule Based Expert Systems[J], McGraw Hill, 1991: 402
[23] Dougherty J.; Kohavi R.; Sahami M. Supervised and Unsupervised Discretization of Continuous Features[J]. Proc Twelfth International Conference on Machine Learning, Morgan Kaufmann, CA, 1995
[24] Kearns M.; Valiant L.G. Learning boolean formulae or factoring[J]. Technical Report TR-1488, 1988
[25] Schapire R.E. The Strength of weak learnability[J]. Machine Learning, 1990, 5(2): 197-227
[26] Freund Y.; Schapire R.E. A decision-theoretic generaliation of on-line learning and an application to boosting[J]. Journal of Computer and System Sciences, 1997, 55(1): 119-139
[27]万鹏.机械故障诊断中基于神经网络的数据挖掘方法研究[D].湖北:湖北工业大学硕士学位论文, 2006
[28]祝丽华,姚竹亭.基于数据挖掘的电路板故障诊断系统设计[J].工业控制计算机, 2009, 22(9): 42-43
[29]张朝晖,张跋.挖掘多值属性的关联规则[J].软件学报, 1998(11): 801-805
[30]董连京.应用于SMT行业先进的在线检测设备—自动光学检查系统[J].国内外机电一体化技术, 2002,5(3): 33-36
[31]贾忠中. SMT工艺质量控制[M].北京:电子工业出版社, 2007
[32]陆志香.数据挖掘技术及其应用[J].科技广场, 2008, 8
[33]王桂芹,黄道.数据挖掘技术综述[J].电脑应用技术, 2007, 69
[34]钮轶君,钱省三,任建华.基于数据挖掘的半导体制造质量异常研究[J].半导体技术, 2006, 12(31)
[35]苏圣超,张正道,朱大奇.基于时间序列数据挖掘的旋转机械故障预报[J].南京航空航天大学学报, 2006, 7(38)
[36]何小龙,吴先献.对电子元件质量监督抽查结果的综合分析[J].电子产品可靠性与环境试验, 2002, 12(6): 38-39
[37]邵峰晶,于忠清.数据挖掘原理与算法[M].北京:中国水利水电出版社, 2003
[38]朱德利. SQL Server 2005数据挖掘与商业智能完全解决方案[M].北京:电子工业出版社, 2007
[39]李春葆,曾平.数据库原理与应用——基于SQL Server 2000[M].北京:清华大学出版社, 2006
[40]钟朝嵩.品质管理大全(上)[M].厦门大学出版社, 2005
[41]俞明南,丁正平.质量管理[M].大连理工大学出版社, 2005
[42]文放怀. SPC实战——新工厂管理[M].广州:广东经济出版社, 2005
[43]王金鹤,李玉红.基于电子标签的SPC过程控制分析[J].微计算机信息, 2008, 7(24)
[44]赵利伟.基于数据采集的SPC系统的研究[D].青岛科技大学机械制学院, 2007
[45]殷建军,余忠华,吴昭同.基于现场总线的嵌入式SPC系统研究[J].浙江大学学报, 2004, 6(38)
[46]傅明,阚树林,张赋杰.基于SPC的焊接生产线质量控制及相关分析[J].现代制造工程, 2008, 1
[47]张宪民,卢盛林.一种用于电路板自动光学检测设备的光源装置[P].中国:200710031093.4, 2007
[48]张宪民,卢盛林.无铅焊点检测的光源分析与优化设计[J].华南理工大学学报自然科学版, 2008.3
[49]卢盛林,张宪民,邝泳聪.基于神经网络的PCB焊点检测方法[J].华南理工大学学报(自然科学版), 2008, 5(36): 135-139
[50]卢盛林,张宪民.无铅焊点检测光源的分析与优化设计[J].光学精密工程, 2008, 8(16): 1376-1383
[51] Lu Sheng-lin, Zhang Xian-min. Analysis and optimal design of illuminator for leadless tin solder joint inspection[J].Optics and Precision Engineering, 2008, 8: 1377-1383
[52] Driels M.R.; Nolan D.J. Automatic defect classification of printed wiring board solder joints [J]. IEEE Transactions on components, hybrids, and manufacturing technology, 1990, 13(2) 331-340
[53]刘保顺. Visual Basic .Net数据库开发[M].清华大学出版社,2004
[54]张牧笛,罗驱波,白静宜. ADO.NET数据访问模型研究[J].微计算信息, 2008,24:78-80
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