摘要
随着制造过程越来越集成化、智能化,在很多大型制造企业都可以找到复杂制造过程。随着市场对产品质量要求的不断提高,企业需要越来越关注其制造过程的控制问题,因为质量是制造出来的。复杂制造过程往往具有过程变量多、变量交互影响复杂、多工序多平台等特点,因此很难或几乎不可能找到一个合适的差分或微分方程来描述过程响应和过程变量之间的关系,从而使得实施过程层面的质量控制变得困难。本文针对复杂制造过程的参数控制问题,在已有的“基于回归模型的健壮参数控制”方法基础上,进一步完善其方法学体系和框架,讨论了过程变量的新分类和实验设计效应排序新原则,提出了谨慎控制律的设计方法,开发了一类自适应可变参数控制图技术,提出了旨在降低在线调节频次的节约型控制律设计方法,解决了非计量型过程响应的在线观测问题。同时也通过介绍所提方法在钢铁行业、半导体制造业、成型过程中的应用分析,证明所提方法是有效且实用的。论文的主要研究工作包括以下五大部分:
一、在现有健壮参数控制方法基础上,总结并完善了该方法学的理论框架,讨论了实验设计建模阶段需要考虑和改进的方面,并提出具有一般性的方法学求解模型。
二、明确考虑模型系数估计误差和噪声观测误差的存在,在分析了两类误差对所提方法控制性能的影响机理之后,设计了相应的谨慎控制策略,该谨慎策略能使原先的基于统计回归的健壮参数控制方法不敏感于这两类误差,提高了该方法在不确定性环境下的控制性能。
三、针对已经应用了健壮参数控制方法的过程,提出一类自适应可变参数控制图技术。通过整合控制律信息,对系统响应进行预测,基于预测进行控制图参数的在线自动调节。新的自适应控制图分为两类:一类主要提高过程变异的跟踪能力,另一类则着重优化控制图的使用成本。新发展的控制图能够有效地配合基于统计回归的健壮参数控制方法,共同改善复杂制造过程的质量。
四、在健壮参数控制方法基础上,考虑尽量减少在线不必要的调节,提出一种节约型控制律的设计方法。首先提出两个新的概念:质量边际和噪声变化的自补偿,并定量分析了两个概念是如何创造了减少调节频次的可能。最后提出全新的控制律设计方法,通过和传统的控制方法进行比较,指出所提方法在保证工程要求的质量标准的前提下,能够用更少的调节频次达到控制目标。
五、明确考虑非计量型过程响应的在线观测问题和在所提方法中的应用问题。非计量型过程响应往往不能在线进行直接测量,但对复杂制造过程进行统计回归建模时,一个必要条件就是过程响应(即过程质量指标)必须是可以定量观测的。针对这一问题,提出了使用机器视觉和图像处理技术,实现非计量型过程响应的在线观测。结合钢铁行业中的连铸过程,开发了高温连铸过程钢坯表面质量的在线检测系统的关键算法,为后续的回归建模提供了必要的过程响应数据。针对非计量型过程响应和计量型过程变量之间的不匹配关系,提出了使用逻辑回归方法对连铸过程进行建模,基于所得逻辑回归模型,应用所提健壮参数控制方法设计出优化控制律,最后得到钢厂现场工程师的支持,建议得到了成功应用。
本文通过整合和应用统计方法、自动控制理论、谨慎控制理论、控制图技术、最优控制理论和图像处理方法,提高了基于统计回归的健壮参数控制方法的鲁棒性、自适应性、经济性和实际应用性。
When the manufacturing processes are getting more and more integrated and intelligent, there are a lot of complex manufacturing processes in various manufacturing companies. With the increasing demand on quality of products, manufacturers care more of the control performance of their manufacturing processes since the quality is produced. Generally, complex manufacturing processes are featured with a large number of process variables, complex interactions among those variables, multiple stages and platforms. Hence, it is pretty hard or even impossible to find an appropriate differential or difference equation to describe the complex manufacturing process, which makes controlling of complex manufacturing processes much harder. This article aims at the problem of process variable control in complex manufacturing processes. Based on the previous work on“regression model based robust parameter control”, this article further perfects its framework and system; discusses the new classification of process variables and the new effect hierarchy principle in experimental design; proposes the design approach of cautious control law; develops an adaptive control charts with variable parameters; proposes an economical control law with reduced adjustment frequency; solves the online observation of attributes data. By implementing the proposed methods in steel industry, semiconductor industry, forming industry, the methods are verified to be effective and practical. There are five main sections in this article.
1. Based on the existing method, we summarize and perfect its framework. Additionally, modeling approaches regarding experimental design are researched to improve and revise. The concrete solving procedures of the proposed method are also given.
2. It is explicitly pointed out that the estimation error of regression model coefficients and the observation error of observable noises should be considered. A cautious control strategy is developed after the influence of those two types of errors on control performance is studied. The proposed cautious control law is capable of improving the robustness of control performance to uncertainties.
3. An adaptive control charts with variable parameters is designed for a robust parameter control method applied process. Integrated with the applied control law, the proposed control charts can automatically adjust control chart parameters based on the prediction of system responses. There are two types of adaptive control charts developed. One enhances the tracking performance of process changes. The other one reduces the SPC run cost. The proposed SPC monitoring strategy, together with the robust parameter control method, effectively improves the process quality of complex manufacturing processes.
4. An economical control law with reduced adjustment frequency is proposed based on the robust parameter control method. New concepts quality margin and self-compensation of noise change are proposed. We analyze the chances for adjustment frequency reduction that those two concepts could create. The innovative design of control law fully utilizes those two concepts which is quite different from the existing control law design. With a comparison study, we show that the proposed reduced control law requires much less in-line adjustments while guarantees the specified process quality.
5. It is explicitly to consider the observation problem and implementing problem of attributes data process responses. Generally, attributes data process responses are hard to be measured online. However, one of the prerequisites for conducting regression modeling in complex manufacturing processes is process response should be quantified and observed in real time. In order to solve this problem, an image processing algorithm based on sensing camera is developed, which is able to realize the online observation of attributes data. By implementing this technique into continuous casting process, a core algorithm capable of automatically detecting surface defects of casting billets is developed. Then, a full form of data structure can be expected. In order to solve the modeling of attributes process response and variables process data, a logistical regression is built for casting process modeling. Based on this logistical regression, the proposed control method is implemented and optimal control law is obtained. The suggestions on control are accepted by steel plant, which verifies a successful implementation case of the proposed method in casting processes.
Through integrating statistical methods, automatic process control, cautious control, control charts, optimal control, and image processing method, the article makes the proposed method more robust, adaptive, economical, and practical.
引文
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