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1
Trentin, Marcelo Gonçalves. "Monitoramento e controle estatístico integrado ao controle de engenharia de processo". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2010. http://hdl.handle.net/10183/28819.
Testo completoAbstract (sommario):
Com o aumento da produção mundial em proporções cada vez maiores, os processos industriais têm se tornando um desafio pela complexidade do seu gerenciamento. A identificação rápida e precisa de não conformidades é cada vez mais necessária e mais difícil de ser realizada. Este estudo propõe a integração do Controle de Engenharia com o Controle Estatístico de Processo, no monitoramento e controle de processos industriais, almejando a percepção mais rápida de anormalidades, visando à redução de problemas de especificação de produtos. Uma forma de autoajuste do controlador Proporcional-Integral e Derivativo (PID) é proposta, aumentando a robustez do sistema, empregando-se técnicas comumente utilizadas nos processos. O modelo matemático do processo, equacionando as relações das variáveis envolvidas, é estabelecido para determinação e especificação do controlador e de forma conjunta as cartas de controle são configuradas. O controlador projetado para a situação normal de operação atua no sentido de manter as variáveis de saída (controladas) dentro de especificações através do conhecimento de sua relação com as de entrada e de processo. As cartas de controle baseadas em modelos, monitorando os resíduos provenientes de ajustes de modelos ARIMA, acompanham as variações do processo, evitando variabilidade excessiva e possibilitando a detecção de comportamentos anormais, inclusive monitorando o desempenho do próprio controlador. Com a sinalização das cartas de controle, é realizada uma interferência na equação de ajuste do controlador. Empregando-se a simulação numérica, analisam-se os comportamentos do controlador e este, combinado com as cartas de controle. Falhas inseridas propositalmente, em cada variável controlada, foram devidamente sinalizadas. Estas sinalizações ocorreram mesmo em situações em que as variáveis estiveram mantidas dentro da especificação pelo controlador. Para o sistema de autoajuste, o aumento dos ganhos de contribuição das cartas de controle proporcionou maior acurácia das variáveis controladas (monitoradas). A integração proposta apresentou melhores resultados, quanto à manutenção das variáveis de saída próximas aos seus alvos, quando comparada com o controlador operando isoladamente.With the increase of world production at bigger and bigger proportions, the industrial processes have become a challenge by the complexity of their management. The fast and precise identification of non-conformities is increasingly necessary and more difficult to be performed, preferably even before problems with product specification or waste can be considered. This study proposes the integration of Engineering Process Control with the Statistical Process Control, in monitoring and controlling of industrial processes, aiming the quicker perception of abnormalities, looking for the reduction of products specification problems. A form of self-adjustment of the Proportional-Integral and Derivative Controller (PID) is proposed, increasing the system robustness applying techniques commonly used in the processes. The mathematical model of the process, equating the relationship of the variables involved, is established to the determination and specification of the controller, and the control charts are configure in an integrated way. The controller projected to the normal operation situation, acts in the sense of keeping the exit variables (controlled) within the specifications through the knowledge of its relationship with that of the entrance and that of the process. The control charts based in models, monitoring the residues coming from the models adjustment ARIMA, follow up the process variations avoiding excessive variability and making it possible the detection of abnormal behaviors, even monitoring the performance of the controller itself. With the signalling of the control charts, an interference in the equation of adjustment of the controller is performed. Applying the numerical simulation, the controller behaviors are analyzed and this combined with the control charts. Intentionally inserted failures, in each controlled variable, were properly signalized. These signallings have happened even in situations where the variables were kept by the controller within the specification.. To the self-adjustment system, the increase of contribution gains of the control charts has provided greater accuracy of the controlled variables. The integration proposed has presented better results, in relation to maintain of the exit variables next to their targets, when compared to the controller operating in isolation.
2
Chang, Hsuan-Kai. "An integrated SPC/EPC system for fault diagnosis". Diss., Online access via UMI:, 2009.
Cerca il testo completoAbstract (sommario):
Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2009.Includes bibliographical references.
3
Ramalho, Octávio Ferreira. "Implementação integrada de SPC e EPC na melhoria contínua do processo". Master's thesis, Faculdade de Ciências e Tecnologia, 2008. http://hdl.handle.net/10362/3937.
Testo completoAbstract (sommario):
Dissertação apresentada na faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia IndustrialDurante os últimos anos tem havido nos meios académicos, por parte de um sector da engenharia industrial um esforço no sentido de integrar duas metodologias de controlo que, em muitas situações reais, estão implementadas de forma autónoma. A primeira, mais ligada à engenharia da qualidade, consiste na monitorização dos processos com recurso a cartas de controlo, ou controlo estatístico do processo (SPC - Statistical Process Control). A segunda abordagem está baseada no ajustamento dos processos recorrendo à informação sobre os níveis actuais das respostas ou desvios em relação aos valores de referência e constitui a engenharia de controlo do processo (EPC). Ambas as metodologias têm por base a identificação e modelação do processo. O presente trabalho apresenta metodologias abrangentes para a identificação e modelação dos processos, baseadas em estimativas dos mínimos quadrados e de máxima verosimilhança, com o objectivo de se captar as dinâmicas internas dos processos e assim obter os modelos necessários à implementação integrada, ou não, do controlo estatístico (SPC) e engenharia de controlo (EPC - Engineering Process Control). Pretendendo-se ser o mais abrangente possível, toda a abordagem foi feita para lidar com sistemas de múltiplas entradas e múltiplas saídas (MIMO Multi-Input/Multi-Output), considerando-se os outros modelos (SISO ou MISO) como casos particulares deste. A metodologia de identificação implementada foi posteriormente utilizada com dados reais provenientes de um processo MISO, obtendo-se assim o modelo que serviu de base para simular o sistema de integrado SPC/EPC. Com base nos resultados obtidos por simulação, pode-se concluir que o sistema integrado permite simultaneamente um melhor ajustamento do processo em relação aos valores de referência, reduzir a sua variabilidade, detectar alterações verificadas no processo e, caso seja possível, alterar o algoritmo de controlo de forma a responder às alterações verificadas.
4
Tezic, Kerem. "Essays on immigrants' economic integration /". Göteborg : Dept. of Economics [Nationalekonomiska institutionen], Univ, 2004. http://www.handels.gu.se/epc/archive/00003616/01/Kerem_full.pdf.
Testo completo
5
Erlandsson, Mattias. "On monetary integration and macroeconomic policy". Göteborg : Dept. of Economics, School of Economics and Commercial Law, [Nationalekonomiska institutionen, Handelshögsk.], Univ, 2003. http://www.handels.gu.se/epc/archive/00002715/01/Erlandsson.avhandl.pdf.
Testo completo
6
Rohani, Jafri Mohd. "The development and analysis of quality control adjustment schemes for process regulation". Ohio : Ohio University, 1995. http://www.ohiolink.edu/etd/view.cgi?ohiou1179951651.
Testo completo
7
Bouterse, Rosita B. "Competition and integration - what goals count? : EEC competition law and goals of industrial, monetary, and cultural policy /". Deventer [u.a.] : Kluwer Law and Taxation Publ, 1994. http://www.gbv.de/dms/spk/sbb/recht/toc/276164091.pdf.
Testo completo
8
沈瑞蓉 e Ray-Long Shen. "應用多品質特性之SPC-EPC 系統於隧道窯爐溫控制之製程管制探討–以陶瓷電容之介電瓷粉製程為例". 碩士, 朝陽科技大學, 1991. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22091CYUT5031044%22.&searchmode=basic.
Testo completo
9
Chung, Lin-Cheng, e 鍾姈君. "A Study on the Integration of SPC And EPC". Thesis, 1998. http://ndltd.ncl.edu.tw/handle/62729054866886291185.
Testo completoAbstract (sommario):
碩士國立清華大學
統計學研究所
86
Statistical process control (SPC) and engineering process control (EPC) are twostrategies for quality improvement.In general, both strategies have reduction of variablity as their objective,although they seek to accomplish this objective in different ways.This article addresses the $EPC$ rules ($MV$ controller) of the process withmore general stochastic disturbances. We also examine the advantage of usingthe $Cuscore$ control as an interface between $EPC$ and $SPC$ techniques.
10
Lu, Shu-Lan, e 盧淑蘭. "A Study of Dealing With theAutocorrelated Data by Integrating EPC and SPC". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/92426406850012769091.
Testo completoAbstract (sommario):
碩士國立雲林科技大學
工業工程與管理研究所碩士班
91
Autocorrelation commonly exists in the process industry. With reduced ARL, autocorrelation leads to increase the false alarm rate of control charts. It is required to deal with autocorrelation, because it can cause significant deterioration of control charting performance. Time series approach is generally used to describe the structure of autocorrelation. AR(1) and AR(2) models are investigated, and disturbances considered are stepped or linear types. Recent researches on process control find that integration of EPC and SPC has good performance. Purpose of this research is to study integrated EPC and SPC method in dealing with autocorrelated data. Three control charts are considered: Shwhart、CUSUM and EWMA chart. From this study,results show that when the level of autocorrelation is low, the result of integrated EPC and SPC is similar to time series model approach. However, when data with high autocorrelation, integrated EPC and SPC is better than time series model approach. CUSUM and EWMA charts are better than Shewhart chart in these situations:(1) in stepped disturbance, low level of autocorrelation and range of process shifts is 0.5 to 2 time standard deviation; (2) in stepped disturbance, high level of autocorrelation and range of process shifts is 1 to 3 time standard deviation; (3)in linear disturbance, the slope of process shifts is between 0.05 and 0.5. Therefore, it is a good choice using CUSUM or EWMA charts for process monitoring under these situations.
11
CHEN, CHENG YUNG, e 陳鄭勇. "Construct Feedback Control System for Autocorrelated Process by Integrating SPC & EPC". Thesis, 2000. http://ndltd.ncl.edu.tw/handle/63733573008396931495.
Testo completoAbstract (sommario):
碩士中原大學
工業工程學系
88
By observing the global industry trend, high technology industries and petrochemical business will be the main stream of Taiwan economy. Thus, how to build an excellent process control system, so as to improve product quality, moreover to enhance the industry’s competitiveness will be the major researches of the future in academic community and relating business. Nowadays, automatic control system has been improving day by day, and the autocorrelated process exists widely in various kinds of industry. The objective of this research is to improve process control capability of SPC, so that it can respond to the process status and to eliminate the uncontrollable variance immediately. In addition to this study combine the concept of EPC to figure out appropriate control strategy. This research is to integrate the advantages of SPC and EPC, also merging with the time series, forward control and feedback control. Construct a feedback control system by aiming at autocorrelated process, including five parts: manufacturing process subsystem, measurement subsystem, intervention detecting and analyzing subsystem, integrated forward control and feedback control subsystem and adjustment subsystem. By the characteristics of SPC which monitor process continuously and the ability of EPC that adjust process properly, we support an effective and efficient control strategy to improve the yielding rate and quality.
12
Hsiao, Feng-Yuan, e 蕭逢元. "Integrating SPC/EPC Model with Soft Computing Methods in MIMO Process Control System". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/58960665281815647978.
Testo completoAbstract (sommario):
博士中原大學
工業工程研究所
95
The main objective of this study aims at Multiple-Input Multiple-Output (MIMO) process mode. Based on the integrated concepts of SPC and EPC, Soft Computing (SC) technique and statistical analysis technique are combined to modularize the relationship between process output and process input, so optimal yield can be derived and process quality can be improved. The study intended to construct a MIMO process control system with soft computing methods for prediction and parameter control. The system included MIMO process sub-system, measurement sub-system, deviation detection and analysis sub-system, artificial neural network process output prediction sub-system, genetic algorithm parameter design optimization search sub-system and parameter adjustment sub-system. The study detailed the internal operation for each sub-system and relationship among each other. Compared to the past study, the greatest difference is that the soft computing in the study is to integrate artificial neural network and genetic algorithm applications, which was more effective than a single artificial neural network system according to evaluation result. Besides correct prediction and diagnosis for the noise due to system deviation, it effectively controls process input and output as well as achieves process optimization.
13
Li, Fu-Zi, e 李柎梓. "Integrating SPC and EPC in Monitoring the Process Mean Shifts—A Neural Network Approach". Thesis, 2002. http://ndltd.ncl.edu.tw/handle/21447719046144528475.
Testo completoAbstract (sommario):
碩士元智大學
工業工程與管理學系
90
Statistical process control (SPC) and engineering process control (EPC) are two strategies for quality improvement. SPC seeks to reduce process variability by detecting and eliminating assignable causes of variation. On the other hand, EPC seeks to minimize variability by reversing the effect of process disturbance through regular adjustments to manipulable process variables. SPC is traditionally applied to processes where successive observations are statistically independent. EPC is usually applied to processes in which successive observations are related over time. The application of EPC relies on the auto-correlated structure. Recently, the integrated SPC/EPC scheme is gaining interests in industries. Superior control can be achieved through the application of integrated SPC/EPC scheme. In this research, two neural networks had been developed to model the combined SPC/EPC scheme. The first neural network was employed to construct the auto-correlated structure to cancel out the disturbance. The second neural network acted as a monitoring tool similar to traditional control charts. The performance of the proposed neural networks was evaluated by the average run lengths (ARL’s) using simulation. The results show that the proposed neural networks outperform traditional approach. Keywords: statistical process control, engineering process control, neural networks, average run lengths
14
Lin, Yi-Ni, e 林宜霓. "The Application of Integrating SPC and EPC to Manufacturing Process Control and Supply Chain Management". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/00309858671840123800.
Testo completoAbstract (sommario):
博士國立臺灣大學
商學研究所
91
For manufacturing processes, in order to obtain the advantages of eliminating both assignable causes and common causes, an effective method to integrate SPC (Statistical Process Control) and EPC (Engineering Process Control) under the continuous process is rousing escalated interests. In our study, it is found that EPC feedback compensation mechanism affects SPC out-of-control detection, and degrades the output quality once suddenly assignable causes are removed. Based on this finding, we propose a novel SPC control scheme that can effectively detect and identify the disturbance types. Further, we introduce a cost-oriented decision rule for removing assignable causes in SPC and EPC integration system. Based on this decision rule, a more accurate selection methodology of SPC charts’ control limits is also proposed. For supply chain management, many significant researches have successfully completed the quantifying the bullwhip effect of the supply chain management. However, the unexpected demand change, as what happens in the beer game scenario, is not taken into consideration. In this research, we quantify the bullwhip effect resulting from the unexpected demand change and also compare the cases with and without demand information sharing. In addition, based on the above findings, we re-evaluate the value of promotion activities when considering the impact of the bullwhip effect. Also, we propose a cost-oriented decision rule to help to decide whether to adopt Run-by-Run control, which integrates SPC control charts, or not. Last, we propose a formula to decide SPC control limits to detect cost-effective unexpected demand change. Basically, this is a paper regarding management science. Again, it is noticeable that such a simple model cannot completely represent the cases in the real world. However, through the discussion and simulation, we hope to provide the managerial intuitive a more scientific basis. Our goal is to make the managerial decision more comprehensive and less risky. Just as the idea from Jay W. Forrester, the great master in the system dynamic field, that the behavior of the complicated system is often counter-intuitive. It evidences the dilemma we demonstrated in the paper.
15
Chen, Cheng-Kun, e 陳正昆. "To Lower the Difference in Production Process by Using the Integrating Neural Network Approach and SPC/EPC – e.g.: Chemical Production Process". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/03191676242436893612.
Testo completoAbstract (sommario):
碩士國立雲林科技大學
工業工程與管理研究所碩士班
94
Abstract This research combines technologies of Statistic Production Control (SPC) and Engineering Process Control (EPC), and by using reversal connecting Neural Network to set up an effective forecast mold, in order to lower occurrence of defect rate. The research takes example by Chemical Production Process, using Hotelling’s T2 control chart to monitor two existing high-relative output variables, combining EPC to feedback control and correct input parameters. The purpose is to lower the differences of output variables and makes the production process more stable. After many times’ correction, an effective reversal connecting Neural Network forecast mold was set up, via this mold forecasting output variables and adjusting correction of input parameters, makes the output variables fall within controllable range, and reduce the difference of output variables. Verifying the set-up of mold: if the occurrence of variation in production process could be lowered by forecasting output variables and correcting the adjustable input parameters, makes the output variables fall within controllable range. Key words: Neural network, control chart, SPC, EPC
16
羅修來. "Application of Integrated SPC and EPC on Multivariate Autocorrelated Process". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/66923156668637893189.
Testo completoAbstract (sommario):
碩士國立交通大學
工業工程與管理系所
94
The control chart is a widely employed statistical process control (SPC) tool for monitoring the process. It can detect the assignable cause effectively. However, if the process has significant autocorrelation, the traditional SPC procedure would cause suspious information. Besides, chance causes also have impact on the processes. When the process is out of control but no assignable cause is found, it can be adjusted by employing engineering process control (EPC). Recently, the integrated SPC and EPC scheme is the gaining interests in industries. Superior control can be achieved through the application of integrated SPC and EPC scheme. This study presents an integrated SPC and EPC procedure for multivariate autocorrelated process. The SPC procedure constructs a control chart called Z-chart to monitor the process. In the EPC procedure, the modeling way of the group method of data handling (GMDH) is employed to construct the EPC model to adjust the process mean to the target value. GMDH is used to predict the characteristic values of a process output and the relationship between input and output, respectively. A case is utilized to demonstrate the effectiveness of the proposed procedure.
17
Huang, Cheng-Yi, e 黃政逸. "An Integrated SPC and EPC Procedure for Multivariate Autocorrelated Process". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/58076994455550647562.
Testo completoAbstract (sommario):
碩士國立交通大學
工業工程與管理系所
93
Statistical process control (SPC) is a widely employed quality control method in industry. SPC is mainly designed for monitoring single quality characteristic. However, as the design of a product / process becomes complex, a process usually has multiple quality characteristics related to it. These characteristics must be monitored by multivariate SPC. When the autocorrelation is present in the process data, the traditional SPC may mislead the results. Hence, the autocorrelated data must be treated to eliminate the autocorrelation effect before employing SPC to detect the assignable causes. Besides, chance causes also have impact on the processes. When the process is out of control but no assignable cause is found, it can be adjusted by employing engineering process control (EPC). However, EPC may result in unnecessary adjustments. This study presents an integrated SPC and EPC procedure for multivariate autocorrelated process. The SPC procedure constructs a predicting model by group method of data handling (GMDH), which can transfer the autocorrelated data into uncorrelated data. Then, the Hotelling T2 and multivariate cumulative sum control charts are constructed to monitor the process. The EPC procedure constructs a controller utilizing GMDH to adjust the multiple quality characteristics to their target values. Finally, this study uses a set of simulated chemical mechanical polishing (CMP) data to verify the effectiveness of the proposed procedure.
18
Huang, Tzu-yuan, e 黃資元. "Economic Design of Integrated SPC and EPC Using Neural Network Approach". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/89985280982419733223.
Testo completoAbstract (sommario):
碩士國立雲林科技大學
工業工程與管理研究所碩士班
93
The purposes of process control are improving quality and reducing cost, it’s an important subject to balance between these factors. In order to reduce variance for improving quality, combining the Statistical Process Control and Engineering Process Control become for expansionary topic. Many researches prove that integrating SPC and EPC is better than using alone. Some researches using neural network approach in this topic, and proving that have good performance, but less than probing into the cost due to EPC adjustment and the problem of over control. This research considers combining bounded adjustment using Taguchi loss function for EPC method with Neural Network controller. The purpose is to avoid the problem of over control to result in a load of cost due to EPC adjustment. Furthermore, in this study to verify this model is suitable for use in different levels of disturbance, different EPC controller and different adjustment cost. By the result could see that the bounded adjustment using Taguchi method in EPC control has good performance in this study, combining this model for Neural Network controller still has good performance and prove the rationality using Neural Network approach in process control.
19
Hsu, Jun-Ching, e 許俊欽. "Integrate SPC and EPC: Using Neural Network To Construct Disturbance Model". Thesis, 2001. http://ndltd.ncl.edu.tw/handle/09446928745227882404.
Testo completoAbstract (sommario):
碩士國立交通大學
工業工程與管理系
89
Statistical Process control (SPC) and Engineering Process Control (EPC) are two different ways to monitor the process. The purpose of SPC is to find assignable causes and remove them. EPC is used to reduce the effect of predictable variation to keep the process output on target. Although SPC and EPC represent two different control techniques, their objective is the same, i.e. to reduce process variation. Therefore, combine SPC and EPC can effectively reduce variability. In the past, researchers usually utilized time series (such as ARIMA) to construct the disturbance model. However, constructing a time series model needs a complex statistical testing process. This research employs neural networks to construct the disturbance model. This disturbance model is used to construct an adjustment equation, thereby EPC and SPC can be combined to find assignable causes. A numerical example is analyzed. Results show that our proposed approach outperforms the traditional ARIMA approach.
20
Wu, Kuo-Chang, e 吳國彰. "Applying Time Delay Neural Network to Construct an Integrated SPC-EPC System". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/57079004466605497948.
Testo completoAbstract (sommario):
碩士義守大學
工業工程與管理學系碩士班
94
Both statistical process control (SPC) and engineering process control (EPC) are the common tools used in quality control and improvement. The control chart is one of the primary techniques of SPC. It is used to monitor the process variations. While the process variation is detected, EPC can be used to reduce the variation by adjusting some process parameters. The research tries to build an integrated SPC-EPC system to obtain better performance on process control. The integrated system consists of four subsystems. They are data generation subsystem, process variation detection subsystem, process output prediction subsystem, and process adjusting subsystem. As the control chart lacks the ability to process the pattern-related information when the data is within the control limit, some studies tried to use the back-propagation network (BPN) to recognize the patterns in control chart. This research applies the time delay neural network (TDNN) with the memory mechanism to recognize the control chart pattern and to achieve reliable recognition accuracy. The results indicate the TDNN has better performance in detecting process variations and predicting process outputs comparing to the BPN. It is expected that the integrated SPC-EPC system can reduce process variations and improve products’ quality.
21
Tsai, Chia-Ping, e 蔡佳蘋. "Using Rolling Grey Model in a SPC-EPC Process Control Feedback-System". Thesis, 2013. http://ndltd.ncl.edu.tw/handle/24477993864063454221.
Testo completoAbstract (sommario):
碩士國立交通大學
工業工程與管理系所
101
In order to enhance the competitiveness of products, manufacturers must reduce process variation to improve the product quality. Statistical Process Control (SPC) is frequently employed to remove the assignable cause and Engineering Process Control (EPC) is also implemented to adjust the process input, so that the process output can be revised quickly to the target value. Many studies recommended that integrated SPC-EPC has better performance than just employing SPC or EPC alone. The main purpose of this study is using the data of the production and metrology tools to develop a framework of SPC- EPC process feedback control system. First, this paper uses the stepwise regression analysis to order the significant input variables according to their importance, then constructing a relationship between the process input and the output. In addition, a rolling gray model is constructed to predict the future process output when the output of the process has a non-random trend. This feedback control system can decide in advance whether it is necessary to start the variable adjustment mechanism to effectively reduce the process variation.
22
Yang, Tsung-Ju, e 楊宗儒. "The Evaluation and Selection of Control Charts for Integrated SPC and EPC". Thesis, 2000. http://ndltd.ncl.edu.tw/handle/91544843712773317560.
Testo completoAbstract (sommario):
碩士大葉大學
工業工程研究所
88
ABSTRACT Statistical Process Control (SPC) and Engineering Process Control (EPC) are two common techniques used in the process control for reducing manufacturing process variation. SPC apply control charts to monitor the process variations, and EPC reduce the process variation through adjusting some manufacturing parameters. In the recent years, some researchers try to integrate SPC and EPC concepts into the process control to obtain more quickly and accuracy information of manufacturing process. Therefore, in this research, we integrate EPC and SPC techniques, and two kinds of process variations, step change and trend change, are studied. Six control charts that most commonly used in SPC are selected and evaluated. Simulation results in various manufacturing parameters are investigated, the results shown that when SPC and EPC are integrated, CUSCORE has the better performance of six control charts. Keyword: Statistical Process Control, Engineering Process Control, CUSCORE
23
Yi-Ni, Lin, e 林宜霓. "DEVELOPING ON-LINE IDENTIFICATION TECHNIQUES WITH THE INTEGRATED USE OF SPC AND EPC". Thesis, 1997. http://ndltd.ncl.edu.tw/handle/74775675682002758708.
Testo completoAbstract (sommario):
碩士輔仁大學
應用統計學研究所
86
Recently, a great deal of research has focused on integrating statistical process control (SPC) and engineering process control (EPC). Most of these studies have concluded that the integrated use of both SPC and EPC is superior in performance to the use of either alone. However, the majority of these studies have assumed that the assignable causes of a disturbance can be identified and removed as soon as the out-of-control signal is triggered by SPC. For practical consideration, this is not a rational assumption since it is a complex procedure, and it often takes a great deal of time to identify and remove the effects of the assignable causes. This study is concerned with the difficulties of identifying the assignable causes of process disturbance when an integrated SPC and EPC control scheme is in use. Consequently, the proposed identification techniques are able to help controllers (or operators) to better supervise the underlying process in an on-line manner. Simulation studies are investigated to demonstrate the fruitful results when employing our proposed approaches.
24
Wu, Po-Yi, e 吳柏毅. "Applying ANN, SVM and ELM to Identify the Types of Disturbances of an SPC/EPC System". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/18816687158887457102.
Testo completoAbstract (sommario):
碩士輔仁大學
統計資訊學系應用統計碩士班
102
Statistical process control (SPC) charts have been widely used in industry more than eighty years. How to effectively detect out-of-control signals in order to identify assignable causes has become a very important issue. The typical SPC chart applications require that the process measurements are independent to each other. However, with the advancement of sensor technology and shorter sampling frequency, the autocorrelation has existed in most practical processes. Once the autocorreled measurements are produced by the process, the type I error would be increased when using typical SPC charts. Both theoretical and empirical findings have suggested that the integration of SPC and EPC can be an effective way to improve the quality of an autocorrelated process. However, because EPC compensates for the effects of underlying disturbances, the disturbance patterns are embedded and hard to be recognized. Effective identification of disturbance patterns is a very important issue for process improvement since disturbance patterns would be associated with certain assignable causes which affect the process. In practical situations, after compensating by EPC, the underlying disturbance patterns could be any mixture types which are totally different from the original patterns. This study proposes the approaches of artificial neural network, support vector machine and extreme learning machine to identify the disturbance patterns of the underlying disturbances. Experimental results revealed that the proposed schemes are able to effectively recognize various disturbance patterns of an SPC/EPC system.
25
Jou, Yi-Chi, e 周奕圻. "Construction of an ANN-SPC-EPC Process Control System for Coating Operations in Dry Film Photoresist Production". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/42530565416573183924.
Testo completoAbstract (sommario):
碩士朝陽科技大學
工業工程與管理系碩士班
93
Dry film photoresist is an important raw material for transforming precise circuit layout design into producing a high quality printed circuit board. The coating operation of the dry film process plays a key role in producing high quality products. Due to soft property of the dry film photoresist, it is concave by using contact instrumentation and may result in measurement errors. Thus, how to control the thickness of photoresist in the coating operation is very important. To solve the process control problem of the coating operation in the dry film process, first, the thickness image of the dry film is captured by a high resolution CCD, and computer vision techniques are applied to thickness measurement of the films. Second, these thickness data are analyzed by MO-VL BP and CG BP models to predict process trends. Third, the proposed model combines SPC-EPC techniques and finite feedback control model. According to SPC monitoring and construction of the disturbance model, then applied EPC feedback adjustment for coating operation. Finally, sensitivity analysis and DOE are conducted to find the best parameter settings of the optimal ANN-SPC-EPC model that can provide predictable, monitoring and feedback control for coating operation. This research implements the proposed model of combining CG BP and MCEWMA-EPC techniques. Experimental results show that the adjustment times of the proposed model can reduce over 50%, accurately forecast the process trends, and have less almost 20% of the process variance when comparing with the traditional EWMA-EPC method. Obviously, the CG-MCEWMA-EPC model provides forecasting process trend function and process improvement scheme.
26
Wang, Yi-Hsieh, e 王怡仙. "Application of Machine Learning Approaches to the Recognition of Control Chart Patterns for an SPC-EPC Process". Thesis, 2015. http://ndltd.ncl.edu.tw/handle/90477567557862319006.
Testo completoAbstract (sommario):
碩士輔仁大學
統計資訊學系應用統計碩士班
103
In recent year, the integration of statistical process control (SPC) and engineering process control (EPC) has widely used in the industrial processes. Although the integration of SPC and EPC has a great benefit to a process, it causes the problem of recognition of control chart patterns (CCP). That is, EPC is able to compensate for the underlying disturbance; however, it may embed the effects of underlying disturbances. As a result, it becomes much more difficult to recognize the CCP. The recognition of CCP is crucial for the process improvement since those patterns are usually associated with some specific assignable causes. Accordingly, the issue of rapid and correct recognition of CCP for a SPC-EPC process is a very promising research topic in the industry. There has been little research conducted on the recognition of CCP for a SPC-EPC process so far. This study is motivated to propose six machine learning approaches to recognize the mixture patterns of process disturbances. Those six approaches include the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), time-delay neural network (TDNN), rough set (RS) and random forest (RF). Experimental results reveal that the proposed TDNN scheme has the best performance to recognize the CCP for an SPC-EPC process.
27
Li, Ai-Ti, e 李艾玓. "Enhancement of the Detection Capability of a SPC/EPC System Using Neural Networks and Multivariate Adaptive Regression Splines". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/14602642122419165868.
Testo completo
28
Chang, Po-Yu, e 張博與. "Applying Two-stage Computational Intelligence Approaches to the Identification of Mixture Types of Control Chart for an SPC/EPC Process". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/cq8fxj.
Testo completoAbstract (sommario):
碩士輔仁大學
統計資訊學系應用統計碩士班
105
The success of integration of statistical process control (SPC) and engineering process control (EPC) has been reported in recent years. However, the SPC control chart pattern (CCP) has become more difficult to be classified due to the fact that the process disturbances were embedded in the SPC-EPC system. Although some studies have focused on the classification tasks for a manufacturing process, they only considered a single or basic disturbance type in a process. There has been very little research addressed on the classification of mixture of single disturbance for an SPC-EPC system. The purpose of this study is to propose an effective two-stage mechanism to deal with the classification of mixture CCPs for an SPC-EPC process. Because of their excellent performance on classification tasks, this study employs four computational intelligence techniques to recognize the mixture patterns of the system. This four techniques include artificial neural network(ANN), support vector machine(SVM), extreme learning machines(ELM), and multivariate adaptive regression splines(MARS). Simulation results reveal that the proposed two-stage model is approaches are able to effectively identify various mixture types of disturbances for an SPC-EPC system.
29
Chang, Ju-Ching, e 張洳菁. "Improving MES and SPC Integration System of FPD FAB Using Web Services". Thesis, 2012. http://ndltd.ncl.edu.tw/handle/37856064592986613486.
Testo completoAbstract (sommario):
碩士國立交通大學
管理學院工業工程與管理學程
100
The Flat Panel Display(FPD) Industy is suffering the firece competition. In order to improve the competence – higher product yield, lower product cost and enhance the production efficiency, the production and quality management becomes extremely important. The FPD production process using complicate physical and chemical process, must have many metrology steps to validate the process quality before and after the process steps, and use the statistic process control(SPC) methodology to monitor the process quality trend, in order to control the stability of the production equipment. This case study is a demonstration to integrate the MES track-out process and process quality control of the SPC system, to improvement the production efficiency and the product process quality control. When the production staff executes the track-out order in the MES, the system response time would impact the production efficiency and process quality control. To improve this, the case study leverages the methodology of the business process reengineering, to find out the main factors which impact the system response time. And evaluate the software technology to redesign the system integration architecture, in order to shorten the response time. This architecture framework could also be helpful for the small-medium size panel makers and the R&D institution to integrate the MES track out step with the SPC quality check step.
30
Han, Cheng Chun, e 鄭淳瀚. "Integration of EPC Network and Wireless Sensor Network for Heterogeneous Network Gateway Design". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/t25r2b.
Testo completoAbstract (sommario):
碩士國立臺東大學
資訊管理學系碩士班
97
The most critical technologies in the third wave of computing called ubiquitous network are RFID and Wireless sensor network. Both of them not only have high research expansion but also wide application popularity. Although we have the high popularity of RFID and WSN, yet the insufficient welcome calculation and effective data framework lead to disintegrated setting and protocol. This thesis aims at the integration of RFID and WSN, which is the combination of features of the instantaneity of WSN and the consistency and convenience of RFID EPC. This groundbreaking combination can create a compound system to provide a convenient environment for users to choose what they need for their works. On the other hand, facing the upcoming of digital family, the internet and its peripheral equipments are more crucial to families and firms. We also plan to adopt OSGi to build an OSGi Gateway in the future. We can execute this process through OSGi Framework in order to accelerate the integration of heterogeneous networks in digital family.
31
Shen, Ray-Long, e 沈瑞蓉. "Applying SPC-EPC Systems of Multi-Quality Characteristics to Temperature Control of Tunnel Kilns - An example of Dielectric Powder Process of Ceramic Capacitors". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/79765768285959308409.
Testo completoAbstract (sommario):
碩士朝陽科技大學
工業工程與管理系碩士班
91
Ceramic dielectric powder is a very important raw material in high technology industry. This kind of ceramic dielectric powder almost has been used in the high technology products, for examples of multiplayer ceramic capacitors, chip resistors, dielectric resonator/filter, patch antenna, substrates for MIC and single layered capacitor for HF etc. In this research, we aim at temperature control problems of tunnel kilns in the ceramic dielectric powder process of ceramic capacitors. The operations of furnace temperature control in calcinations of the tunnel kiln are explored. There are many uncontrollable disturbance factors and process operation problems in the powder process may affect the calcinations operation in the tunnel kiln. To achieve the request for the specification in the ceramic dielectric powder, it is very important to control the furnace temperature in the calcinations operation. In order to solve the temperature control problem in the calcinations operation, including four main stages in this research. At the first stage, we aim at potential factors that may affect the tunnel kiln temperature control to discuss. Planning a design of experiment, then find the significant factors of influence the tunnel kiln temperature control. At the second stage, comparing the models of SPC and finding the best model to control the furnace temperatire in the tunnel kiln. At the third stage, we are proposeded two models to predict the tunnel kiln furnace. A process control system for monitoring and feedback the temperature is developed in the calcinations operation by combining statistical process control and Engineering process control. SPC supplies the monitor process function. EPC be able to process outputs class to target values with continuous adjustment. Finally, constructing the process control model to implement the calcinations operation of the powder process, and using the evaluation index to evaluate the performance. This research proposes a method of combining BPN and EWMA techniques to construct the disturbance model. This disturbance model can be presented by an adjustment equation, thereby EPC and SPC can be combined to find assignable causes. According to EPC feedback adjustment, it can not only accurately feedback the variation of the next time but also effectively predict the model behaviors and reduce variabilities. The need for compact, light, thin, and high capacity electronic equipment and consumer electronic products has greatly increased the importance of the furnace temperature control problems of the ceramic dielectric powder process in tunnel kiln for the capacitor manufacturers. This research contributes a solution to a common problem of the temperature control and offers a computer-aided temperature control and process adjustment system to meet the process and quality control request. In addition, the results of this research can be beneficial to enterprises that have similar process and precise quality control request, such as steel, cement, food, and cell-phone industries.
32
Hsu, Wei-Jing, e 徐偉晉. "Construction of an Autocorrelated Process Feedback and Control Model Based on RBF Network and SPC-EPC System – A Case of MLCC Cutting Operations". Thesis, 2001. http://ndltd.ncl.edu.tw/handle/63660430204756513082.
Testo completoAbstract (sommario):
碩士朝陽科技大學
工業工程與管理系碩士班
89
Since recently developed electronic products are extremely thin and light, the chip-type components have replaced the traditional components. Because of the excellent electronic characters in multi-layer ceramic capacitor(MLCC), it can be applied to the high-density PC board design. To reach the excellent electronic characters, every ceramic capacitor process should be planned and controlled properly, especially the ceramic capacitor cutting operation which has high accurate request. The cutting operation relates to the electronic characters of ceramic capacitor seriously, so the operation has significant influence on the quality and cost of the whole ceramic capacitor process. Current ceramic capacitor cutting operation uses program algorithms and computer vision techniques to obtain the centers of the cutting marks, and judge the cutting positions by artificial works. Because of the forms of cutting marks are always irregular, the ceramic capacitor cutting operations cannot reach high accurate request, furthermore, current operations even have no effective quality control methods and surveillance systems to revise the unusual cutting operations. To improve the above problem, this research will use computer vision technique to choose the more reliable cutting positions, and make the cutting distances to be the major quality characteristic. Then, the bounded adjustment with response surface methodology and the radial basis function network are implemented to control and feedback the cutting inaccuracies in the cutting operations, respectively. Under the measurement of the evaluation indices, it shows that the bounded adjustment and the radial basis function network both have obvious effects on the improvement of the cutting operations. Especially, the radial basis function network method has the most significant effect. The need for compact, light, thin, and high capacity electronic equipment and consumer electronic products has greatly increased the importance of the multi-layer ceramic capacitor cutting operation. This research contributes a solution to a common problem of the ceramic capacitor cutting operation and offers a computer-aided process control system to meet the process and quality control request. In addition, the results of this research can be beneficial to enterprises that have similar process and precise quality control request, such as IC wafer dicing.
33
ŠVEJDOVÁ, Kateřina. "Individuální a skupinová integrace žáků s mentálním postižením v rámci plnění povinné školní docházky". Master's thesis, 2007. http://www.nusl.cz/ntk/nusl-47512.
Testo completoAbstract (sommario):
Problems dealing with individual and group integration of mentally handicapped pupils in terms of fulfilment the compulsory school attendance are very often discussed. In the diploma work I have concentrated on a research of fulfilment the basic conditions of integration and problems that parents and educationalists solve during integration of mentally handicapped pupils into elementary schools. Workers from specially-pedagogical centres for mentally handicapped people, educationalists providing education for integrated pupils and these integrated pupils'parents form together the experimental complex. Following methods and techniques of collecting data were used to reach the aim - the methods of standardized interviews, the methods of inquiry-form researchs and as a complementary method two matter studies. My diploma work could by inspiration for mentally handicapped pupils'parents, common educationalists, special educationalists and authorities of state administration.