• Journal of the Korea Safety Management & Science
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• v.7 no.2
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• pp.141-151
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• 2005

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. In the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with second-order autoregressive disturbance. We compare three control systems; EPC, EPC combined with EWMA. This paper shows through simulation that tlhe performance of the integrated model of EPC and EWMA is more preferable than that of EPC.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.233-241
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• 1999

Control chart is a very extensively used tool in testing whether a process is in a state of statistical control or not. In this paper, we propose a robust EWMA(exponentially weighted moving averages) control chart for variables, which is based on the Huber's M-estimator. The Huber's M-estimator is a well-known robust estimator in sense of distributional robustness. In the proposed chart, the estimation of the process deviation is modified to have a s table level and high power. To compare the performances of the proposed control chart with other charts, some Monte Carlo simulations we performed. The simulation results show that the robust EWMA control chart has good performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.1
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• pp.110-122
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• 1993

When voice is transmitted through packet switching network, there needs a overload control, that is, a control for the congestion which lasts short periods and occurrs in local extents. In this thesis, we analyzed the performance of the statistical packet voice multiplexer using the overload control strategy by bit dropping. We assume that the voice is coded accordng to (4,2) embedded ADPCM and that the voice packet is generated and transmitted according to the procedures in the CCITT recomendation G. 764. For the performance analysis, we must model the superposed packet arrival process to the multiplexer as exactly as possible. It is well known that interarrival times of the packets are highly correlated and for this reason MMPP is more suited for the modelling in the viewpoint of accuracy. Hence the packet arrival process in modeled as MMPP and the matrix geometric method is used for the performance analysis. Performance analysis is similar to the MMPP IG II queueing system. But the overload control makes the service time distribution G dependent on system status or queue length in the multiplexer. Through the performance analysis we derived the probability generating function for the queue length and using this we derived the mean and standard deviation of the queue length and waiting time. The numerical results are verified through the simulation and the results show that the values embedded in the departure times and that in the arbitrary times are almost the same. Results also show bit dropping reduces the mean and the variation of the queue length and those of the waiting time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.58
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• pp.113-118
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• 2000

Estimation of the process deviation is an important problem in statistical process control, especially in the control chart, process capability analysis or measurement system analysis. In this paper we suggest the use of the Gini's mean difference for the estimation of the c, the measure of the process deviation through a lots of simulations in various types of distributions. The Gini's mean difference uses the differences of all possible pairs of data. This point will improve the efficiency of estimation. In various classes of distributions, the Gini's mean difference shows good performance, in sense of bias of estimates or mean squared errors.

• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.32-38
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• 2007

A batch Process has a multi-way data structure that consists of batch-time-variable axis, so the statistical modeling of a batch process is a difficult and challenging issue to the process engineers. In this study, We applied a statistical process control technique to the general batch process data, and implemented a fault-detection and Statistical process control system that was able to detect, identify and diagnose the fault. Semiconductor etch process and semi-batch styrene-butadiene rubber process data are used to case study. Before the modeling, we pre-processed the data using the multi-way unfolding technique to decompose the data structure. Multivariate regression techniques like support vector regression and partial least squares were used to identify the relation between the process variables and process condition. Finally, we constructed the root mean squared error chart and variable contribution chart to diagnose the faults.

• Journal of Korean Society for Quality Management
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• v.26 no.2
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• pp.106-117
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• 1998

The following study has been undertaken to build QIP (Quality Improvement Process) of an inner-layer process in a PCB (Printed Circuit Board) manufacturing plant. The objective of the study is stabilization and optimization of the process through quality improvement. To do that, defective factors in process are gathered by the cause and effect analysis and classified by PFD (Process Flow Diagram), key factors are found out by PFMECA (Process Failure Mode and Effect Criticalty Analisis), DOE(Design of Experiments) is a, pp.ied to those key factors to optimize the process, SPC (Statistical Process Control) chart is used to maintain the optimal conditions of the process and to improve quality continuously, and a quality management system is developed to improve quality mind and quality system for the PCB jmanufacturing plant. Overall, QIP is established to improve quality for the PCB manufacturing plant in the study.

• Journal of the Korea Safety Management & Science
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• v.11 no.2
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• pp.117-126
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• 2009

We develop methods for propagating and analyzing EPCI(Extended Process Capability Index) by using the error type that classifies into accuracy and precision. EPCI developed in this study can be applied to the three combined processes that consist of production, measurement and calibration. Little calibration work discusses while a great deal has been studied about SPC(Statistical Process Contol) and MSA(Measurement System Analysis). EPCI can be decomposed into three indexes such as PPCI(Production Process Capability Index), PPPI(Production Process Performance Index), MPCI(Measurement PCD, and CPCI(Calibration PCI). These indexs based on the type of error classification can be used with various statistical techniques and principles such as SPC control charts, ANOVA(Analysis of Variance), MSA Gage R&R, Additivity-of-Variance, and RSSM(Root Sum of Square Method). As the method proposed is simple, any engineer in charge of SPC. MSA and calibration can use efficientily in industries. Numerical examples are presentsed. We recommed that the indexes can be used in conjunction with evaluation criteria.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.875-880
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• 2015

Traditional semiconductor process control has been performed through statistical process control techniques in a constant process-recipe conditions. However, the complexity of the interior of the etching apparatus plasma physics, quantitative modeling of process conditions due to the many difficult features constraints apply simple SISO control scheme. The introduction of the Advanced Process Control (APC) as a way to overcome the limits has been using the APC process control methodology run-to-run, wafer-to-wafer, or the yield of the semiconductor manufacturing process to the real-time process control, performance, it is possible to improve production. In addition, it is possible to establish a hierarchical structure of the process control made by the process control unit and associated algorithms and etching apparatus, the process unit, the overall process. In this study, the research focused on the methodology and monitoring improvements in performance needed to consider the process management of future developments in the semiconductor manufacturing process in accordance with the age of the APC analysis in real applications of the semiconductor manufacturing process and process fault diagnosis and control techniques in progress.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.98-105
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• 1999

As the IC(Integrated Circuit) has been densified and complicated, it is required to thorough process control to improve yield. Experts, for this purpose, focused on the process analysis automation, which is came from the strict data management in semiconductor manufacturing. In this paper, we presents the process analysis system that can analyze causes, for a output after processes. Also, the plasma etching process that highly affects yield among semiconductor process is modeled to predict a output before the process. To approach this problem, we use adaptively trained neural networks that exhibit superior accuracy over statistical techniques. And in comparison with methods in other paper, a method that history of trend for input data is considered is shown to offer advantage in both learning and prediction capability. This research regards CD(Critical Dimension) that is considerable in high integrated circuit as output variable of the prediction model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.130-139
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• 2016

The purpose of this paper is to analyze the problems and the sources of defective products and draw improvement plans in a small plastic boat manufacturing process using TOC (Theory Of Constraints) and statistical analysis. TOC is a methodology to present a scheme for optimization of production process by finding the CCR (Capacity Constraints Resource) in the organization or the all production process through the concentration improvement activity. In this paper, we found and reformed constraints and bottlenecks in plastic boat manufacturing process in the target company for less defect ratio and production cost by applying DBR (Drum, Buffer, Rope) scheduling. And we set the threshold values for the critical process variables using statistical analysis. The result can be summarized as follows. First, CCRs in inventory control, material mix, and oven setting were found and solutions were suggested by applying DBR method. Second, the logical thinking process was utilized to find core conflict factors and draw solutions. Third, to specify the solution plan, experiment data were statistically analyzed. Data were collected from the daily journal addressing the details of 96 products such as temperature, humidity, duration and temperature of heating process, rotation speed, duration time of cooling, and the temperature of removal process. Basic statistics and logistic regression analysis were conducted with the defection as the dependent variable. Finally, critical values for major processes were proposed based on the analysis. This paper has a practical importance in contribution to the quality level of the target company through theoretical approach, TOC, and statistical analysis. However, limited number of data might depreciate the significance of the analysis and therefore it will be interesting further research direction to specify the significant manufacturing conditions across different products and processes.