• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.1
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• pp.7-16
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• 2003

At present, company has to produce a product that consumer like with a competitive price, a good quality, and a fitting time to supply. Process control and quality control are very important to supply with a product uniformly and inexpensively. Process control is given much weight in the quality control in manufacturing system. Statistical process controls(SPC) that are used in process generally have major impact on manufacturing, product design activities, and process development potentially. Control charts in statistical process control method can be interpreted the data from quality characteristics in production process and discriminated between chance variation and assignable variation in process. In addition, control chart can be used to monitor the process output and detect when changes in the inputs are required to bring the process back to an in-control state. The models that relate the influential inputs to process outputs help determine the nature and magnitude of the adjustments required. In this paper, the characteristic of product quality is monitored by control chart during the machining process and construction of quality control cycle is considered to divide into two types in this case that different assignable causes lead to shifts having different magnitudes. Then we are intended to find a process shift magnitude which has economical quality cost policy and are considered to quality cost functions to find a process shift magnitude. Those costs are categorized into the well-known categories of prevention, appraisal, and internal failure and external failure. This paper ends with numerical examples that demonstrate the usefulness of the model.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.435-446
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• 2000

Statistical process control(SPC) and engineering process control(EPC) are two strategies for quality improvement that have been developed independently. EPC seeks to minimize variability by adjusting compensatory variables in order to make the process level close to the target, while SPC seeks to reduce variability by monitoring and eliminating causes of variation. One purpose of this paper is to propose the IMA(0,1,1) model as the in-control process model. For the out-of-control process model we consider two cases; one is the case with a step shift in the level, and the other is the case with a change in the nonstationarity. Another purpose is to suggest the use of an integrated process control procedure with adjustment and monitoring, which can consider the proposed process model effectively. An integrated control procedure will improve the process control activity significantly for cases of the proposed model, when compared to the procedure of using either EPC or SPC, since EPC will keep the process close to the target and SPC will eliminate special causes.

• Industrial Engineering and Management Systems
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• v.12 no.2
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• pp.112-117
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• 2013

The $\overline{x}$ control chart for the process mean and either the R or s control chart for the process dispersion have been used together to monitor the manufacturing processes. However, it has been pointed out that this procedure is flawed by a fault that makes it difficult to capture the behavior of process condition visually by considering the relationship between the shift in the process mean and the change in the process dispersion because the respective characteristics are monitored by an individual control chart in parallel. Then, the ($\overline{x}$, s) control chart has been proposed to enable the process managers to monitor the changes in the process mean, process dispersion, or both. On the one hand, identifying which process parameters are responsible for out-of-control condition of process is one of the important issues in the process management. It is especially important in the ($\overline{x}$, s) control chart where some parameters are monitored at a single plane. The previous literature has proposed the multiple decision method based on the statistical hypothesis tests to identify the parameters responsible for out-of-control condition. In this paper, we propose how to identify parameters responsible for out-of-control condition using the information criterion. Then, the effectiveness of proposed method is shown through some numerical experiments.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.403-407
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• 2005

This paper proposes a looperless tension control algorithm which is robust to disturbance and tensional variation in rolling process using SVR(Support Vector Regression). Hot rolling process which is global technology to coil steel after continuous finishing process for welded bars followed by roughing mill process, becomes hot issue. Finishing mill process not only makes it possible to produce ultra thin steel strip(0.8 mm) but enhance the quality of terminals of coil, which increases the productivity due to faster process. Constant tension control between stands in hot rolling process is essential to enhance the quality of steel. Sensorless tension control is under research by some advanced companies to replace the conventional tension control method because in continuous finishing mill process, it is impossible to install the looper used in conventional control system. Simulation results show the effectiveness of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.1.2-6
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• 1994

A state or the arts in Japanese chemical process control is reviewed based on experience in applying advanced process control schemes to several industrial chemical processes. The applications validate model predictive control (MPC), the most popular advanced control scheme in the process control community, as, indeed, a powerful and practical control algorithm. However, at the same time, it is elucidated that MPC can solve only the control algorithm part of the problem and one needs chemical and systems engineering aspects to solve the entire problem. By illustrating several industrial process control problems, the need for chemical engineering aspects as well as the future direction for process control are addressed, especially in light or current attitudes toward product quality.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.134-136
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• 2007

Sequential Function Chart(SFC) is very easy to grasp the sequential flow of control logic and has the compatability for a maintenance, In process control, when an error is occurred, to execute other routine by force, we control the process by remote control. In this paper, we proposed the method of select sequence control by remote control in process control designed by Sequential Function Chart(SFC).

• Proceedings of the Safety Management and Science Conference
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• 2011.11a
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• pp.535-549
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• 2011

Control chart is most widely used in SPC(Statistical Process Control), Recently it is a critical issue that the standard control chart is not suitable to non-normal process with very small percent defective. Especially, this problem causes serious errors in the reliability procurement, such as semiconductor, high-precision machining and chemical process etc. Procuring process control technique for non-normal process with very small percent defective and perturbation is becoming urgent. Control chart technique in non-normal distribution become very important issue. In this paper, we investigate on research trend of control charts under non-normal distribution with very small percent defective and perturbation, and propose some variable-transformation methods applicable to CUSUM control charts in non-normal process.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.116-122
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• 2016

Recently, the production cycle in manufacturing process has been getting shorter and different types of product have been produced in the same process line. In this case, the control chart using coefficient of variation would be applicable to the process. The theory that random variables are located in the three times distance of the deviation from mean value is applicable to the control chart that monitor the process in the manufacturing line, when the data of process are changed by the type of normal distribution. It is possible to apply to the control chart of coefficient of variation too. ${\bar{x}}$, s estimates that taken in the coefficient of variation have just used all of the data, but the upper control limit, center line and lower control limit have been settled by the effect of abnormal values, so this control chart could be in trouble of detection ability of the assignable value. The purpose of this study was to present the robust control chart than coefficient of variation control chart in the normal process. To perform this research, the location parameter, ${\bar{x_{\alpha}}}$, $s_{\alpha}$ were used. The robust control chart was named Tim-CV control chart. The result of simulation were summarized as follows; First, P values, the probability to get away from control limit, in Trim-CV control chart were larger than CV control chart in the normal process. Second, ARL values, average run length, in Trim-CV control chart were smaller than CV control chart in the normal process. Particularly, the difference of performance of two control charts was so sure when the change of the process was getting to bigger. Therefore, the Trim-CV control chart proposed in this paper would be more efficient tool than CV control chart in small quantity batch production.

• Proceedings of the Safety Management and Science Conference
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• 2013.04a
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• pp.547-556
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• 2013

Control chart is most widely used in SPC(Statistical Process Control), Recently it is a critical issue that the standard control chart is not suitable to non-normal process with very small percent defective. Especially, this problem causes serious errors in the reliability procurement, such as semiconductor, high-precision machining and chemical process etc. Procuring process control technique for non-normal process with very small percent defective and perturbation is becoming urgent. Control chart technique in non-normal distribution become very important issue. In this paper, we investigate on research trend of control charts under non-normal distribution.