A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

Nonparametric Control Scheme for Monitoring Phase Ⅱ Nonlinear Profiles with Varied Argument Values

Profile monitoring is used to check the stability of the quality of a product over time when the product quality is best represented by a function at each time point.However,most previous monitoring approaches have not considered that the argument values may vary from profile to profile,which is common in practice.A novel nonparametric control scheme based on profile error is proposed for monitoring nonlinear profiles with varied argument values.The proposed scheme uses the metrics of profile error as the statistics to construct the control charts.More details about the design of this nonparametric scheme are also discussed.The monitoring performance of the combined control scheme is compared with that of alternative nonparametric methods via simulation.Simulation studies show that the combined scheme is effective in detecting parameter error and is sensitive to small shifts in the process.In addition,due to the properties of the charting statistics,the out-of-control signal can provide diagnostic information for the users.Finally,the implementation steps of the proposed monitoring scheme are given and applied for monitoring the blade manufacturing process.With the application in blade manufacturing of aircraft engines,the proposed nonparametric control scheme is effective,interpretable,and easy to apply.

Multivariate Statistical Process Monitoring and Control: Recent Developments and Applications to Chemical Industry

Multivariate statistical process monitoring and control (MSPM& C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper,The four-step procedure of performing MSPM &C for chemical process ,modeling of processes ,detecting abnormal events or faults,identifying the variable(s) responible for the faults and diagnosing the source cause for the abnormal behavior,is analyzed,Several main research directions of MSPM&C reported in the literature are discussed,such as multi-way principal component analysis (MPCA) for batch process ,statistical monitoring and control for nonlinear process,dynamic PCA and dynamic PLS,and on -line quality control by infer-ential models,Industrial applications of MSPM&C to several typical chemical processes ,such as chemical reactor,distillation column,polymeriztion process ,petroleum refinery units,are summarized,Finally,some concluding remarks and future considerations are made.

A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-Gaussian mixture model

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.

Integration Between Enterprise Process Monitoring and Controlling System and Enterprise Application

The relationships and the features of integration between Enterprise ProcessMonitoring and Controlling System (EPMCS) and Enterprise Process Related Applications (EPRA) wereanalyzed. An integration architecture centered on EPMCS was presented, in which there were fourlayers to connect from EPMCS to EPRA: EPMCS, application integration layer, transport layer andEPRA, and there were four layers used to etstablish integration: presentation layer, function layer,data layer and system layer. The frameworks to connect EPMCS and EPRA were designed, thatEnterprise-Independent Model (EIM), Enterprise-Specific Model (ESM) and meta-model to describe thesetwo models were defined. The method to integrate data based on XML was designed to exchange datafrom EPMCS to EPRA according to the mapping between EIM and ESM. The approches are suitable forintegrating EPMCS and systems in Product Data Management (PDM), project management and enterprisebusiness management.

Control performance monitoring and advanced control towards energy conservation

High performance control of an interactive process such as iron and steel plant relies on ability to honor safety and operational constraints;reduce the standard deviations of variables that need to be controlled(e.g.product quantity,quality );de-bottlenecking the process;and,maximize profitability or lower cost(e.g.energy savings, improve hot metal content).These objectives may be prioritized in this order,but can vary and are very difficult to achieve optimally through conventional control.A multivariable predictive controller solution,along with its extensive inferential sensor and built-in optimizer,provides online closed loop control and optimization for many interactive metal and mining processes to lower the energy cost,increase throughput,and optimize product quality and yield. Control loop performance is also a key factor to improve iron and steel plant automation and operation result; Honeywell CPM offers vender-independent product which provides monitoring,tuning,modeling of control loop and sustainable loop performance analysis and maintenance solution towards operation stability and energy saving.

Experimental Study of Monitoring and Controlling of Composite Cure Process in Autoclave Featured with Fiber Optic Sensor

With the aid of the latest fiber optic sensing technology parameters in the cure process of ther- mosetting resin-matrix composite, such as temperature, viscosity,void and residual stress, can be monitored entirely and efficiently.In this paper, experiment results of viscosity measurement in composite cure process in autoclave using fiber optic sensors are presented. Based on the sensed information, a computer program is utilized to control the cure process. With this technology, the cure process becomes more apparent and controllable, which will greatly improve the cured products and reduce the cost.

Some Group Runs Based Multivariate Control Charts for Monitoring the Process Mean Vector

In this article, we propose two control charts namely, the “Multivariate Group Runs’ (MV-GR-M)” and the “Multivariate Modified Group Runs’ (MV-MGR-M)” control charts, based on the multivariate normal processes, for monitoring the process mean vector. Methods to obtain the design parameters and operations of these control charts are discussed. Performances of the proposed charts are compared with some existing control charts. It is verified that, the proposed charts give a significant reduction in the out-of-control “Average Time to Signal” (ATS) in the zero state, as well in the steady state compared to the Hotelling’s T2 and the synthetic T2 control charts.

Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control

Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions.This ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing assets.This article builds upon the Industry 4.0 concept to improve the efficiency of manufacturing systems.The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding(FSW)process.It consists of a CNC manufacturing machine,sensors,edge,cloud systems,and deep neural networks,all working cohesively in real time.The edge device,located near the FSW machine,consists of a neural network that receives sensory information and predicts weld quality in real time.It addresses time-critical manufacturing decisions.Cloud receives the sensory data if weld quality is poor,and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine.Several experiments are conducted for training the neural networks.The framework successfully tracks process quality and improves the welding by controlling it in real time.The system enables faster monitoring and control achieved in less than 1 s.The framework is validated through several experiments.

复杂曲面机器人磨抛技术研究现状与趋势展望综述

在航空、能源、交通、军工等国家战略领域中,针对复杂曲面零件的自动化、高质量和高效率磨抛需求,对近年来国内外工业机器人磨抛加工关键技术及集成系统的研究和应用进展进行了综述。首先,从复杂曲面机器人磨抛机理及工艺优化、磨抛运动轨迹规划、磨削力控制等方面总结了复杂曲面机器人磨抛技术的研究成果;然后,介绍了国内外机器人磨抛集成系统应用现状;最后,分析了复杂曲面机器人磨抛技术的主要问题以及发展趋势,为该技术的发展提供了重要的指导和方向。研究结果表明:当前该技术存在的主要问题包括磨抛机理不够清晰,数学模型不够准确,复杂曲面机器人磨抛轨迹规划效率不高,磨抛力的控制仍不够精准等;另外,磨抛工艺参数优化、机器人力位混合控制、机器人高精度标定与误差补偿、基于数字孪生的机器人磨抛在线监控、机器人磨抛细分应用场景等方面的研究和实践将极大地推动机器人磨抛技术的发展和应用。

主成分分析在PCM测试数据处理中的应用

应用主成分技术进行PCM(ProcessControlModuleorMonitor)测试数据的分析,能够从大量数据中提取其结构。作为征兆,某种特定的数据结构与一类工艺缺陷有对应关系,因此,主成分分析技术成为集成电路工艺分析和诊断的有力工具。文章介绍了主成分技术在工艺诊断中的应用,深入讨论了该技术在实际应用中应当注意的问题。

生态环境监测过程中多环节质量控制措施分析

该文阐述多环节质量控制措施在生态环境监测全程中的重要性,从生态环境监测的流程、监测的方法、仪器设备、质量控制的目标和原则、采样、样品分析和数据处理等质量控制措施方面进行论述。通过对这些环节的控制,可以保证监测数据的准确性、可靠性和可比性,为更好地保护环境和人类健康提供坚实的依据支撑。对于生态环境监测工作来说,质量控制是一个非常重要的环节,需要不断加强和完善。

替诺福韦合成工艺的优化及抗菌活性研究

为了优化替诺福韦合成工艺,以腺嘌呤和(R)-碳酸丙烯酯为起始原料,经开环缩合、取代、水解得到替诺福韦。通过高效液相色谱法(HPLC)监控整个反应过程,其中开环缩合的最佳反应条件为反应温度120℃,反应时间22 h;取代的最佳反应条件为反应温度60℃,反应时间6 h;水解的最佳反应时间20 h。在最优化条件下总收率达69.2%。通过紫外-可见吸收光谱和荧光发射光谱探讨其光物理性能,发现可以根据不同化合物的荧光发射波长差异来监测反应方向和反应程度;通过抗菌活性测试,发现对低浓度的金黄色葡萄球菌有一定的抗菌活性。该合成工艺实现了副产物回收套用,节约生产成本,提高产能。

自动测试技术在MEMS硅腔滤波器工艺中的应用

在微波通信系统中,滤波器作为通信系统中不可缺少的微波无源器件,其性能指标直接影响整个通信系统的性能。与传统滤波器相比,微机电系统(Micro-Electro-Mechanical System,MEMS)滤波器具有集成度高、小型化等优点,其中腔体结构在RF MEMS滤波器中有着广泛的应用。感应耦合等离子刻蚀是实现三维腔体结构的关键技术,其刻蚀均匀性直接影响腔体滤波器的性能指标。过程控制监控(Process Control Monitoring,PCM)是MEMS工艺控制中的重要监控手段,利用自动测试系统,论述了用于自动测试的PCM图形,通过数据处理得到硅腔深度分布图,并拟合出硅腔深度和中心频率对应关系,反应了当前刻蚀的工艺能力,为后续产品设计、提高刻蚀均匀性及优化版图设计提供了数据支撑。

基于统计过程控制的硅转接板良率优化系统设计

针对2.5D封装用硅通孔(through silicon via,TSV)硅转接基板批量化生产过程中缺乏可靠性评价与优化技术的问题,提出基于统计过程控制(statistical process control,SPC)的评估控制系统,实现在线工艺状态监控及评价,设计硅转接板测试用工艺控制检测(process control monitor,PCM)结构,阐述自动光学检测(automated optical inspection,AOI)中常见的缺陷对系统可靠性的影响。提出的SPC系统对硅转接板批量化生产良率提升具有重要意义。

隧道变形自动化监控数据校验与应用分析

以一新建双线盾构隧道近距离下穿既有暗挖隧道为工程依托,通过数值模拟分析既有隧道结构的变形规律,确定变形控制关键区域,布设自动化监测点。将自动化监测值与数值模拟值进行对比分析,及时对异常数据进行去噪处理,必要时调整数值计算模型,确保无人工复测数据验证时自动化监测数据的可靠性。确定监测数据可靠后,再对关键工序(如二次注浆等)既有隧道结构进行高频实时自动化监测,及时分析既有隧道变形情况,调整施工参数,从而实现对既有隧道结构变形的精准控制。

基于污染物总量控制的资源环境承载力监测预警研究

当资源需求和环境破坏超过资源环境承载力时,后果不堪设想。因此,文章设计基于污染物总量控制的资源环境承载力监测预警,通过测算大气、水、土壤的污染物总量,结合资源承载力指标,构建监测指标体系。利用层次分析法确定各指标权重。根据承载力数值,划分预警等级和污染物排放量控制等级。研究显示:2个区域为红色预警区,需控制污染物排放;1个区域为橙色预警区,可少量排放低污染物;6个区域为黄色预警区,需稍加控制污染物排放;8个区域为蓝色预警区,暂不需控制污染物排放;4个区域为绿色预警区,当前不需控制污染物排放。

PCM参数的多元回归模型及其应用

介绍了建立工艺监控模块(PCM)参数数值模型的多元回归方法。多元回归是一种多元统计的处理方法,由该方法得到的一组数值模型中的每一个,都能够与数据源良好拟合。利用单值模拟器,取得了统计性的模拟结果,应用多元回归法得到了回归模型,它在一定范围内能有效地替代耗时的数值模拟。该方法可广泛应用于IC工艺开发、优化、诊断和电子制造自动化等方面。

自动控制系统中的智能化技术运用

阐述智能化技术的特点,电气自动控制技术中智能化技术应用,包括生产系统中的操作流程、故障处理、控制模型、远程监控设备运转状况,分析PLC技术、神经网络体系的应用。