• Proceedings of the Korean Society for Quality Management Conference
• /
• 2004.04a
• /
• pp.425-429
• /
• 2004

Process adjustment Is a complimentary tool to process monitoring in process control. Process adjustment directs on maintaining a process output close to a target value by manipulating another controllable variable, by which significant process improvement can be achieved. Therefore, this approach can be applied to the 'Improve' stage of Six Sigma strategy. Though the optimal control rule minimizes process variability in general, it may not properly function when special causes occur in underlying process, resulting in off-target bias and increased variability in the adjusted output process, possibly for long periods. In this paper, we consider a responsive feedback control system and the minimum mean square error control rule. The bias in the adjusted output process is investigated in a general framework, especially focussing on stationary underlying process and the special cause of level shift type. Illustrative examples are employed to illustrate the issues discussed.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.2
• /
• pp.285-295
• /
• 2004

Statistical process control(SPC) and engineering process control(EPC) are two complementary strategies for quality improvement. An integrated process control(IPC) can use EPC to reduce the effect of predictable quality variations and SPC to monitor the process for detection of special causes. In this paper we assume an IMA(1,1) model as a disturbance process and an occurrence of a level shift in the process, and we consider the economic performance for applying an EWMA chart to monitor MMSE-controlled processes. The numerical results suggest that the IPC scheme in an IMA(1,1) disturbance model does not give additional advantages in the economic aspect.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.22 no.52
• /
• pp.347-354
• /
• 1999

Statistical Process Control(SPC) which uses control charts is widely used to inspect and improve manufacturing process as a effective method. A parametric method is the most common in statistical process control. Shewhart chart was made under the assumption that measurements are independent and normal distribution. In practice, this assumption is often excluded, for example, in case of (equation omitted) chart, when the subgroup sample is small or correlation, it happens that measured data have bias or rejection of the normality test. A bootstrap method can be used in such a situation, which is calculated by resampling procedure without pre-distribution assumption. In this study, applying bootstrap percentile method to (equation omitted) chart, it is compared and evaluated standard process control limit with bootstrap percentile control limit. Also, under the normal and non-normal distributions, where parameter is 0.5, using computer simulation, it is compared standard parametric with bootstrap method which is used to decide process control limits in process quality.

• Journal of Korean Society for Quality Management
• /
• v.33 no.1
• /
• pp.22-31
• /
• 2005

With the common objective to improve process productivity and product quality, statistical process control (SPC) and engineering process control (EPC) have been widely used in the discrete-parts industry and the process industry, respectively. The major focus of SPC is on process monitoring, while that of EPC is on process adjustment. The emergence of the hybrid industry necessitates a synergistic combination of the two methods for an effective process control. This paper investigates the existing studies on SPC, EPC, and the integration of the two methods. This paper also presents future research issues in this field.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.8
• /
• pp.709-712
• /
• 2015

This paper will introduce the development case study about virtual-reality-based operation training system for steel making process. Steel making process consist iron making process to create liquid steel, pig iron, by reduction process, steel making process to make molten steel by refining, continuous casting process to make slab, and rolling process to make final product like coil, plate. This steel making process deals with liquid and solid products, so facilities of steel making process are very various and complicated. In addition, according to various customer requirements, the recycle of facilities and recipes changing have been fast. So the training for skilled operators is very important point. In this paper, we develop steel making training system based virtual reality for training skilled operator. This system consists of virtual machine, virtual HMI, and virtual control panel. And for fitting the characteristics of each process and increasing the education effectiveness, we develop dynamic methods like the method of dynamic education system configuration, initial facilities setup operation education system, and etc.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.22 no.50
• /
• pp.55-63
• /
• 1999

Past studies on economic control charts for controlling process means assumed that the measures of a quality characteristic do not have measurement error. In practice, however, this assumption is frequently violated. In this paper, the economic design models of three control charts(Xbar control chart, Xbar control chart with warning limits, and CUSUM control chart) for controlling process means are developed on the assumption that the measures can have measurement error. The effects of measurement error on the process control cost and design parameters of three economic control charts are examined. According to the experiments done in this study, when measurement error exists, the economic CUSUM control chart has lower process control cost in comparison with two other control charts. When measurement error becomes larger, both the sample size and the sampling interval increase while the control limits decrease.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.6
• /
• pp.64-71
• /
• 1995

The objective of this paper is to realize an analysis system that is capable of controlling the quality of an entire cutting process by including a 3 coordinate measuring machine in the process line. Fuzzy reasoning networks based on fuzzy associative memories has been intro- duced in the measuring process, the control limits for the control process have been obtained, and the efficiency and reliability of the system have been determined by examining the simu- lated reasoning control values.

• Korean Journal of Chemical Engineering
• /
• v.35 no.12
• /
• pp.2336-2347
• /
• 2018

The azeotrope of methyl acetate methanol and water was isolated using extractive distillation with water as entrainer. The pressure-swing extractive distillation (PSED) process and vapor side-stream distillation column (VSDC) with the rectifier process were designed to separate the methyl acetate, methanol and water mixture. It was revealed that the VSDC with the rectifier process had a reduction in energy consumption than the PSED process. Four control schemes of the two process were investigated: Double temperature control scheme (CS1), $Q_R/F$ feedforward control of reboiler duty scheme for PESD (CS2), $Q_R/F$ feedback control scheme for VSDC (CS3), the feedback control scheme of sensitive plate temperature of side-drawing distillation column to dominate the compressor shaft speed (CS4). Feed flow and composition disturbance were used to evaluate the dynamic performance. As a result, CS4 is a preferable choice for separation of methyl acetate-methanol-water mixture. A control scheme combining the operating parameters of dynamic equipment with the control indicators of static equipment was proposed in this paper. It means using the sensitive plate temperature of side-drawing column to control the compressor shaft speed. This is a new control scheme for extractive distillation.

• Journal of Korean Institute of Industrial Engineers
• /
• v.32 no.3
• /
• pp.210-218
• /
• 2006

Traditional statistical process control (SPC) applied to discrete part industry in the form of control charts can look for and eliminate assignable causes by process monitoring. On the other hand, engineering process control (EPC) applied to the process industry in the form of feedback control can maintain the process output on the target by continual adjustment of input variable. This study presents controlling and monitoring rules adopted by variable sampling interval (VSI) to change sampling intervals in a predetermined fashion on the predicted process levels under integrated EPC and SPC systems. Twelve rules classified by EPC schemes(MMSE, constrained PI, bounded or deadband adjustment policy) and type of sampling interval combined with EWMA chart of SPC are proposed under IMA (1,1) disturbance model and zero-order (responsive) dynamic system. Properties of twelve control rules under three patterns of process change (sudden shift, drift and random shift) are evaluated and discussed through simulation and control rules for integrated VSI EPC and SPC systems are recommended.

• Journal of the Korea Safety Management & Science
• /
• v.13 no.4
• /
• pp.225-235
• /
• 2011

Autoregressed Controller, which have trend algorithm, seeks to minimize variability by transferring the output variable to the related process input variable, while multivariate process control techniques seek to reduce variability by detecting and eliminating assignable causes of variation. In the case of process control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We also investigate algorithm with relevant Shewhart chart, Theoretical control charts, precontrol and process capability. To help the people who want to make the theoretical system, we compare the main techniques in "a study on the relation between multivariate process control techniques and trend algorithms".