• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.115-125
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• 2011

Manufacturing companies usually manage the process to achieve high quality using various types of control chart in statistical process control. When an assignable cause occurs in a process, the data in the control chart changes with different patterns by the specific causes. It is important in process control to classify the CCP (Control Chart Pattern) recognition for fast decision making. In former research, gathered data from process used to apply as raw data, leads to degrade the performance of recognizer and to decrease the learning speed. Therefore, feature based recognizer, employing feature extraction method, has been studied to enhance the classification accuracy and to reduce the dimension of data. We propose the method to extract features that take the distances between CCP data and reference vector generated from BDK (Bi-Directional Kohonen Network). We utilize those features as the input vectors in ANN (Artificial Neural Network) and compare with raw data applied ANN to evaluate the performance.

• 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 Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.203-212
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• 2018

Quality requirements of manufactured products or parts are given in the form of specification limits on the quality characteristics of individual units. If a product is to meet the customer's fitness for use criteria, it should be produced by a process which is stable or repeatable. In other words, it must be capable of operating with little variability around the target value or nominal value of the product's quality characteristic. In order to maintain and improve product quality, we need to apply statistical process control techniques such as histogram, check sheet, Pareto chart, cause and effect diagram, or control charts. Among those techniques, the most important one is control charting. The cumulative sum (CUSUM) control charts have been used in statistical process control (SPC) in industries for monitoring process shifts and supporting online measurement. The objective of this research is to apply Taguchi's quality loss function concept to cost based CUSUM control chart design. In this study, a modified quality loss function was developed to reflect quality loss situation where general quadratic loss curve is not appropriate. This research also provided a methodology for the design of CUSUM charts using Taguchi quality loss function concept based on the minimum cost per hour criterion. The new model differs from previous models in that the model assumes that quality loss is incurred even in the incontrol period. This model was compared with other cost based CUSUM models by Wu and Goel, According to numerical sensitivity analysis, the proposed model results in longer average run length in in-control period compared to the other two models.

• Industrial Engineering and Management Systems
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• v.12 no.4
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• pp.380-388
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• 2013

Large variation in electrocardiogram (ECG) waveforms continues to present challenges in defining R-wave locations in ECG signals. This research presents a procedure to extract the R-wave locations by forward-backward (FB) algorithm and classify the arrhythmic beat conditions by using RR intervals. The FB algorithm shows forward and backward searching rules from QRS onset and eliminates lower-amplitude signals near the baseline using a statistical process control concept. The proposed algorithm was trained the optimal parameters by using MIT-BIH arrhythmia database (MITDB), and it was verified by actual Holter ECG signals from a local hospital. The signals are classified into normal (N) and three arrhythmia beat types including premature ventricular contraction (PVC), ventricular flutter/fibrillation (VF), and second-degree heart block (BII) beat. This work produces 98.54% accuracy in the detection of R-wave location; 98.68% for N beats; 91.17% for PVC beats; and 87.2% for VF beats in the collected Holter ECG signals, and the results are better than what are reported in literature.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.1
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• pp.94-101
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• 2009

Complex Products may present more than one type of defects and these defects are not always of equal severity. These defects are classified according to their seriousness and effect on product quality and performance. Demerit systems are very effective systems to monitoring the different types of defects. So, classical demerit control chart used to monitor counts of several different types of defects simultaneously in complex products. S.M. Na et al.(2003) proposed the Demerit-CUSUM for the improvement of the demerit control chart performance and Nembhard, D. A. et al.(2001) and G.Y Cho et al.(2004) developed a Demerit control chart using the EWMA technique and evaluated the performance of the control chart. In this paper, we present an effective method for process control using the Demerit-CUSUM with fast initial response. Moreover, we evaluate exact performance of the Demerit-CUSUM control chart with fast initial response, Demerit-CUSUM and Demerit-EWMA according to changing sample size or parameters.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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• pp.9-17
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• 2010

Complex products may present more than one type of defects and these defects are not always of equal severity. These defects are classified according to their seriousness and effect on product quality and performance. So, demerit systems are very effective systems to monitor the different types of defects. Recently, Kang et al.(2009) proposed the revised Demerit-CUSUM for the evaluation of the Demerit-CUSUM control chart performance exactly. In this paper, we present an advanced Demerit control chart using the double EWMA technique. The double EWMA technique is very efficient and strong method for process control where defects and nonconformities occur with various defect types. Moreover, we compare exact performance of Demerit-CUSUM, Demerit-EWMA and Demerit-DEWMA control chart according to changing sample size or mean shifts magnitude. By the result, we confirm that the performance of Demerit-DEWMA control chart is more than the performance of the Demerit-CUSUM and Demerit-EWMA control chart.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.4
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• pp.114-120
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• 2008

The control chart is widely used statistical process control(SPC) tool that searches for assignable cause of variation and detects any change of process. Generally, ${\bar{X}}-R$ control chart and ${\bar{X}}-S$ are most frequently used. When the production run is short and process parameter changes frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. The CV control chart is an effective tool to control the mean and variability of process simultaneously. The CV control chart, however, is not sensitive at small shift in the magnitude of CV. In this paper, we propose an CV-EWMA (exponentially weighted moving average) control chart which is effective in detecting a small shift of CV. Since the CV-EWMA control chart scheme can be viewed as a weighted average of all past and current CV values, it is very sensitive to small change of mean and variability of the process. We suggest the values of design parameters and show the results of the performance study of CV-EWMA control chart by the use of average run length (ARL). When we compared the performance of CV-EWMA control chart with that of the CV control chart, we found that the CV-EWMA control chart gives longer in-control ARL and much shorter out-of-control ARL.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.37-45
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• 2016

Control chart is representative tools of statistical process control (SPC). It is a graph that plotting the characteristic values from the process. It has two steps (or Phase). First step is a procedure for finding a process parameters. It is called Phase I. This step is to find the process parameters by using data obtained from in-controlled process. It is a step that the standard value was not determined. Another step is monitoring process by already known process parameters from Phase I. It is called Phase II. These control chart is the process quality characteristic value for management, which is plotted dot whether the existence within the control limit or not. But, this is not given information about the economic loss that occurs when a product characteristic value does not match the target value. In order to meet the customer needs, company not only consider stability of the process variation but also produce the product that is meet the target value. Taguchi's quadratic loss function is include information about economic loss that occurred by the mismatch the target value. However, Taguchi's quadratic loss function is very simple quadratic curve. It is difficult to realistically reflect the increased amount of loss that due to a deviation from the target value. Also, it can be well explained by only on condition that the normal process. Spiring proposed an alternative loss function that called reflected normal loss function (RNLF). In this paper, we design a new control chart for overcome these disadvantage by using the Spiring's RNLF. And we demonstrate effectiveness of new control chart by comparing its average run length (ARL) with ${\bar{x}}-R$ control chart and expected loss control chart (ELCC).

• 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 of Industrial and Systems Engineering
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• v.36 no.4
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• pp.109-115
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• 2013

Process Capability Index (PCI) is useful Statistical Process Control (SPC) tool that is measure of process diagnostic and assessment tools widely use in industrial field. It has advantage of easy to calculate and easy to use in the field. $C_p$ and $C_{pk}$ are traditional PCIs. These are only considers of process variation. These are not given information about the characteristic value does not match the target value of the process. Studies of this process capability index by many scholars actively for supplement of its disadvantage. These studies to evaluate the capability of situation of various field has presented a new process capability index. $C_{pm}$ is considers both the process variation and the process deviation from target value. And $C_{pm}{^+}$ is considers economic loss for the process deviation from target value. In this paper development of new process capability index that is Taguchi's quadratic loss function by applying the expected loss. And check the correlation between existing traditional process capability index ($C_{pk}$) and new one. Finally, we propose the criteria for classification about developed process capability index.