• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.4
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• pp.116-123
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• 2005

The traditional process capability indices Cp, Cpk, Cpm, $Cpm^+$ have been used to characterize process performance on the basis of univariate quality characteristics. Cp, Cpk consider the process variation, Cpm considers both the process variation and the process deviation from target and Cpm+ considers economic loss for the process deviation from target. In manufacturing industry, there is growing interest in quantitative measures of process variation under multivariate duality characteristics. The multivariate process capability index incorporates both the process variation and the process deviation from target or considers expected loss caused by the process deviation from target. This paper proposes multivariate capability index based on the expected loss derived from multivariate normal distribution.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.381-399
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• 2004

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for process quality improvement. SPC re-duces process variability by detecting and eliminating special causes of process variation, while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been need for an integrated process control (IPC) procedure which combines the two strategies. This paper considers a scheme that simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an IMA(1,1) model with a step shift. The EPC part of the scheme adjusts the process, while the SPC part of the scheme detects the occurrence of a special cause. For adjusting the process repeated adjustment is applied according to the predicted deviation from target. For detecting special causes the exponentially weighted moving average control chart is applied to the observed deviations. It was assumed that the adjustment under the presence of a special cause may increase the process variability or change the system gain. Reasonable choices of parameters for the IPC procedure are considered in the context of the mean squared deviation as well as the average run length.

• Journal of the Chungcheong Mathematical Society
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• v.13 no.2
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• pp.19-27
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• 2001

In this paper we study another kind of the large deviation property, i.e. moderate deviation type for random amount of random measures on $R^d$ about a Poisson point process and a Poisson center cluster random measure.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.4
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• pp.182-189
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• 2007

Process Capability indices(PCIs) have been widely used in manufacturing industries to provide a quantitative measure of process performance. PCIs have been developed to represent process capability more exactly. The traditional process capability indices Cp, Cpk, Cpm, $Cpm^+$ have been used to characterize process performance on the basis of univariate quality characteristics. Cp, Cpk consider the process variation, Cpm considers both the process variation and the process deviation from target and $Cpm^+$ considers economic loss for the process deviation from target In the previous studies, only one designated location on each part is measured. System process capability index even though in single process, multiple measurement locations on each part are required to calculate the reliable process capability. In manufacturing industry, there is growing interest in quantitative measures of process variation under multivariate quality characteristics. The multivariate process capability index incorporates both the process variation and the process deviation from target or considers expected loss caused by the process deviation from target. In this paper, we compare various process capability indices and propose the application method of PCIs.

• Journal of Korea Multimedia Society
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• v.17 no.7
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• pp.858-865
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• 2014

In the case of system like MES, various sensors collect the data in real time and save it as a big data to monitor the process. However, if there is big data mining in distributed computing system, whole processing process can be improved. In this paper, system to analyze the cause of operation deviation was built using the big data which has been collected from deasphalting process at the two different plants. By applying multivariate statistical analysis to the big data which has been collected through MES(Manufacturing Execution System), main cause of operation deviation was analyzed. We present the example of analyzing the operation deviation of deasphalting process using the big data which collected from MES by using multivariate statistics analysis method. As a result of regression analysis of the forward stepwise method, regression equation has been found which can explain 52% increase of performance compare to existing model. Through this suggested method, the existing petrochemical process can be replaced which is manual analysis method and has the risk of being subjective according to the tester. The new method can provide the objective analysis method based on numbers and statistic.

• Communications of the Korean Mathematical Society
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• v.15 no.2
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• pp.379-390
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• 2000

In this paper the limit theorems on lag increments of a Wiener process due to Chen, Kong and Lin [1] are developed to the case of a Gaussian process via estimating upper bounds of large deviation probabilities on suprema of the Gaussian process.

• Transactions of Materials Processing
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• v.15 no.4 s.85
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• pp.295-303
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• 2006

Optimization is carried out to determine process parameters which reduce the amount of springback and improve shape accuracy of a deep drawn product in sheet metal forming process. The study uses the amount of stress deviation along the thickness direction in the deep drawn product as an indicator of springback instead of springback simulation. The scheme incorporates with an explicit elasto-plastic finite element method for calculation of the final shape and the stress deviation The optimization method adopts the response surface method in order to seek for the optimum condition of process parameters such as the blank holding force and the draw-bead force. The present scheme is applied to design of the variable blank holding force in an U-draw bending process and the application is further extend ε d to the design of draw-bead force in a front side member formed with advanced high strength steel (AHSS) sheets of DP60. Results show that design of process parameter is well performed to decrease the stress deviation through the thickness and to reduce the amount of springback. The present analysis provides a guideline in a design stage for controlling the springback based on the finite element simulation of the complicated parts.

• Journal of Korean Society for Quality Management
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• v.29 no.3
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• pp.79-85
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• 2001

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 process deviation when we design the control limits in construction of the control charts. The efficiency of the Gini's mean difference was well explained in Nam, Lee and Jung(2000). In this paper we propose an $\overline{X}$ control chart which use the control limits based on the Gini's mean difference. In various classes of distributions, the proposed control chart shows food performance.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.200-205
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• 1996

Recently, in order to achieve high flexibility of manufacture, monitoring and control strategies cf a new type have been developed. Since the generation of built-up edge on the cutting tool damages the surface finish of the workpiece, the monitoring system of built-up edge is an important process monitoring. In this study, the analyzing methods of cutting force signal to detect the built-up edge during cutting process are described. The cutting force signals are analyzed using the mean, standard deviation and mean to standard deviation of this cutting signals. We can obtain the guide to detect the built-up edge during turning process.