【作者】 罗丹；

【导师】 罗家祥；

【作者基本信息】 华南理工大学 ， 控制理论与控制工程， 2018， 硕士

【摘要】 随着科学技术的飞速发展,特别是自动化、计算机等技术的进步,在工业过程现代化加速的进程中,当代制造业生产过程不可避免地朝着复杂化、大型化、自动化的方向发展,使得保证其生产过程的安全可靠性及其产品质量的难度大大增加。另外,制造业生产过程还伴随着十分苛刻的生产条件和环境,如高温、高压、易燃和易爆等,所以生产的安全可靠运行是至关重要的,也是保证产品质量的关键因素。因此,为了提高产品质量、确保过程运行状况满足给定的性能指标,需要对过程的异常状况或故障进行检测、诊断和消除,因此在线监控、故障检测及质量控制成为科研人员一个迫切的研究课题。柔性电路板,作为最重要的电子元器件或IC芯片的基板,凭借其高精密与高可靠的优越性能得到越来越广泛的应用。随着电子元器件尺寸的减少,柔性电路板线路布局越来越密集化,保证产品的质量也变得更加重要。柔性电路板制造过程包括上百道工序,其中蚀刻工序是最关键工序之一。本文以柔性电路板制造过程中的蚀刻工序为例,研究了基于极限学习机的过程监控方法及应用。本文主要研究内容如下:(1)提出一种带权重变化和决策融合的极限学习机方法,该方法在学习过程中,先用当前模型对新加入的样本进行检测,再在模型更新中增加错误分类样本的权重,提高错误分类新样本对模型的影响。此外,为了提高模型在未知样本上的泛化能力,引入决策级融合的方法进行集成决策。(2)提出一种带有权重机制、简化核和差分进化(DE)优化参数的正则化在线极限学习机方法,简称WOS-DE-RKELM。在该算法中,使用权重机制和简化核映射结合来使正则化的在线极限学习机具有更好的性能。另外,隐含层节点个数、正则化参数和核参数会影响算法性能,因此使用差分进化(DE)优化算法来优化选择。(3)总结柔性电路板制造过程工艺及其过程故障和工艺参数特性,介绍并分析柔性电路板制造过程中的蚀刻工序以及过程参数,并使用数学仿真的方法仿真蚀刻工序过程,最后将提出的方法用于柔性电路板制造过程中的蚀刻工序中,实现在线过程故障检测和变量的监控。更多还原

【Abstract】 With the rapid development of science and technology,especially the progress of automation and computer,modern manufacturing process become more and more complex,large-scale,automatic,which makes it difficult to guarantee the production process’ s safety and reliability.In addition,the manufacturing process is accompanied by very strict production conditions and environment,such as high temperature,high pressure,inflammable and explosive,so safe and reliable operation is crucial,and is key factors to ensure the quality of products.Therefore,in order to improve product quality and ensure that the process operation conditions satisfy the given performance index,it is necessary to detect,diagnosis and elimination abnormal conditions or fault,so online monitoring,fault detection and quality control become an urgent and necessary research subject to the scientific researcher.Flexible Printed Circuit(FPC),as one of the most important electronic interconnection technology,has been widely used in various electronic products because of its superior performance.However,as a high-precision product,the manufacturing process of FPC is pretty complex and precise,it becomes more and more important to ensure safe and reliable manufacturing process of FPC.The manufacturing process of FPC includes hundreds of processes,among which the etching process is one of the key processes.In this paper,the process monitoring methods and application of extreme learning machine are researched based on the etching process.The research works are shown as following:(1)A new online extreme learning machine algorithm with varying weights and decision level fusion has been proposed,which increases the weights of the new samples predicted wrongly by current data monitoring model in learning,and introduced decision level fusion to improve integrated decision-making ability of the model.Performance comparisons of the method are presented using UCI datasets and Tennessee Eastman process.The results show that the proposed algorithm produces comparable or better performance with higher accuracies and lower training time.(2)An online regularized sequential extreme learning machine with ensemble strategies including kernel strategy,weight mechanism and differential evolution optimized parameters(WOS-DE-RKELM)is proposed.Regularization,weight mechanism and kernel strategy have been introduced into an online sequential extreme learning machine(OS-ELM)separately to improve algorithm performance.In this paper,to take advantage of different strategies,regularization,weight strategy and reduced kernel projection strategy are combined in one OS-ELM to make the algorithm more outperformance.However,the ensemble strategies have side effects as several parameters which directly affect algorithm performance are introduced at the same time;therefore,to wake the effects,an effective optimization tool,differential evolution is adopted to tune these parameters.Experiments are carried out on benchmark regression datasets from the UCI repository and time-varying datasets.The results show that the proposed algorithm is more efficient than the popular OS-ELMs in term of prediction accuracy and robustness.(3)The thesis analyses the characteristics of the technology in the manufacturing process of high density FPC and the etching process of manufacturing process of FPC,while summarizes the possible types,source and faults,then simulation data of etching process generated accordingly.The proposed OS-ELMs are employed to manufacture etching process and recognize fault.更多还原