• Journal of Korean Society for Quality Management
• /
• v.35 no.2
• /
• pp.106-112
• /
• 2007

In statistical process control, it is assumed that the process data are independent. However, most of chemical processes such as semi-conduct processes do not satisfy the assumption because of presence of autocorrelation between process data. It causes abnormal out of control signal in the process control and misleading estimation in process capability. In this study, we adopted Shore's method to solve the problem and propose an optimal subgroup size to estimate the variance correctly for AR(1) processes. Especially, we focus on finding an actual subgroup size for small samples based on simulation study.

• Journal of the Korea Society for Simulation
• /
• v.3 no.1
• /
• pp.167-178
• /
• 1994

For the identification of the optimal operating conditions of phosphoric acid plant, an intelligent simulation was performed based on the dissolution reaction of phosphate rock. A phosphoric acid plant consists of three main processes : ball-mill grinding process, rock reaction process and slurry filteration process. The grinding and filteration processes are relatively simple processes and most of the simulation works are on the reaction process. The practical operation data of phosphoric acid plant at Namhae Chemical Corp. were utilized in the simulation. The operation of the phosphoric acid plant is highly dependent on the heuristics of operators and so the expert system technology was employed. The operation of phosphoric acid plant varies with the origin of phosphate rock. Results of the simulation showed the optimal values of major process variables and optimal operating conditions. The knowledgebase for the expert system was constructed based on the interview with the experienced plant operators.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.12
• /
• pp.33-37
• /
• 2002

A short shot is a molded part that is incomplete because insufficient material was injected into the mold. Remedial actions to control the process conditions can be taken by the injection molding experts based on their knowledge and experience. However, it is very difficult for the non-experts to avoid short shot by finding the proper process conditions such as mold temperature, melt temperature and filling time. In this paper, an intelligent generator of the optimal process conditions based upon fuzzy logic algorithm is proposed so that trial and error can be minimized and the non-experts as well as the experts can also find the optimal process conditions.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2001.04a
• /
• pp.402-405
• /
• 2001

A short shot is a molded part that is incomplete because insufficient material was injected into the mold. Remedial actions to control the process conditions can be taken by the injection molding experts based on their knowledge and experience. However, it is very difficult for the non-experts to avoid short shot by finding the proper process conditions such as mold temperature, melt temperature and filling time. In this paper, an intelligent generator of the optimal process conditions based upon fuzzy logic algorithm is proposed so that trial and error can be minimized and the non-experts as well as the experts can also find the optimal process conditions.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1991.10a
• /
• pp.147-159
• /
• 1991

Stochastic convexity(concvity) of a stochastic process is a very useful concept for various stochastic optimization problems. In this study we first establish stochastic convexity of a certain class of Markov additive processes through the probabilistic construction based on the sample path approach. A Markov additive process is obtained by integrating a functional of the underlying Markov process with respect to time, and its stochastic convexity can be utilized to provide efficient methods for optimal design or for optimal operation schedule of a wide range of stochastic systems. We also clarify the conditions for stochatic monotonicity of the Markov process, which is required for stochatic convexity of the Markov additive process. This result shows that stochastic convexity can be used for the analysis of probabilistic models based on birth and death processes, which have very wide application area. Finally we demonstrate the validity and usefulness of the theoretical results by developing efficient methods for the optimal replacement scheduling based on the stochastic convexity property.

• Journal of Korean Society for Quality Management
• /
• v.28 no.2
• /
• pp.1-16
• /
• 2000

This paper considers the problem of determining the optimal process mean and screening specification limits of a surrogate variable associated with product quality under two-stage screening procedure. In two-stage screening, the surrogate variable is inspected first to decide whether an item should be accepted, rejected or additional observations should be taken. If additional observations are required, the performance variable of interest is then observed to classify the undecided items. Assuming that the performance variable and the surrogate variable are jointly normally distributed, the optimal process mean and the screening limits are obtained by maximizing the expected profit which includes selling price, production, reprocessing, inspection and penalty costs. A numerical example is presented and numerical studies are performed to compare the proposed two-stage screening procedure with single-stage screening procedures.

• Transactions of Materials Processing
• /
• v.9 no.6
• /
• pp.644-652
• /
• 2000

Tube hydroforming is recently drawing attention of automotive industries due to its several advantages over conventional methods. It can produce wide range of products such as subframes, engine cradles, and exhaust manifolds with cheaper production cost by reducing overall number of processes. h successful tube hydroforming depends on the reasonable combination of the internal pressure and axial load at the tube ends. This paper deals with the optimal process design of hydroforming process using the genetic algorithm and neural network. An optimization technique is used in order to minimize the tube thickness variation by determining the optimal loading path in the tube expansion forming and the tube T-shape forming process.

• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.2
• /
• pp.66-73
• /
• 1997

In many optimal methods for the structural design, the structural analysis is performed with the given design parameters. Then the design sensitivity is calculated based on its structural anaysis results. There-after, the design parameters are changed iteratively. But genetic algorithm is a optimal searching technique which is not depend on design sensitivity. This method uses for many design para- meter groups which are generated by a designer. The generated design parameter groups are become initial population, and then the fitness of the all design parameters are calculated. According to the fitness of each parameter, the design parameters are optimized through the calculation of reproduction process, degradation and interchange, and mutation. Those are the basic operation of the genetic algorithm. The changing process of population is called a generation. The basic calculation process of genetic algorithm is repeatly accepted to every generation. Then the fitness value of the element of a generation becomes maximum. Therefore, the design parameters converge to the optimal. In this study, the optimal design pro- cess of a machine tool structure for static loading is presented to determine the optimal base supporting points and structure thickness using a genetic algorithm.

• Transactions of Materials Processing
• /
• v.14 no.9 s.81
• /
• pp.798-803
• /
• 2005

In this paper, the six sigma scheme together with the rigid-viscoplastic finite element method is employed to obtain the optimal metal flow lines of a hot forging according to the six sigma processes, i.e., five steps such as define, measure, analyze, improve and control. Each step is investigated in detail to meet customer's requirements through improvement of product quality. A forging simulator is used for analysis of the metal flow lines of the hot forging, manufactured by a hot press forging machine, under various conditions of major factors determined at each step. The analyzed results are examined in order to reveal the effects of major factors on the metal flow lines and the formed shapes. The effects are then used to find an optimal process and the optimal process with die is devised and tested. The comparison between the required metal flow lines and the experiments shows that the approach is effective for optimal process design in hot forging considering metal flow lines.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.27 no.8
• /
• pp.501-504
• /
• 2014

This paper was developed and described core-process to implement low on resistance which was the most important characteristics of SJ (super junction) MOSFET. Firstly, using process-simulation, SJ MOSFET optimal structure was set and developed its process flow chart by repeated simulation. Following process flow, gate level process was performed. And source and drain level process was similar to genral planar MOSFET, so the process was the same as the general planar MOSFET. And then to develop deep trench process which was main process of the whole process, after finishing photo mask process, we developed deep trench process. We expected that developed process was necessary to develop SJ MOSFET for automobile semiconductor.