• 대한산업공학회지
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• 제30권2호
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• pp.84-92
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• 2004

A process control procedure is proposed when 100% inspection is performed in a process with excellent capability. Only the incomplete measurement data is assumed to be available, i.e. the specific measurement value of the quality characteristic is not available for each item but it can be determined to be smaller or larger than any prescribed value. In the suggested model, a signal limit is introduced to determine whether the process under study is in control or not. If the quality characteristic of an incoming item exceeds the upper signal or the lower signal limit, the process is determined to be stopped or not by comparing the number of consecutively accepted items with a predetermined threshold number. The procedure is designed based on the type I and II errors. The performance of the model is evaluated by the expected number of items produced under the in-control and out-of-control states until the process is stopped.

• Korea-Australia Rheology Journal
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• 제21권2호
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• pp.83-89
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• 2009

Coating process plays an important role in information technology such as display, battery, chip manufacturing and so on. However, due to complexity of coating material and fast deformation of the coating flow, the process is hard to control and it is difficult to maintain the desired quality of the products. Moreover, it is hard to measure the coating process because of severe processing conditions such as high drying temperature, high deformation coating flow, and sensitivity to the processing variables etc. In this article, the coating process is to be re-illuminated from the rheological perspectives. The practical approach to analyze and quantify the coating process is discussed with respect to coating materials, coating flow and drying process. The ideas on the rheology control of coating materials, pressure and wet thickness control in patch coating process, and stress measurement during drying process will be discussed.

• 품질경영학회지
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• 제34권3호
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• pp.66-72
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• 2006

The process capability indices are widely used to measure the capability of the process to manufacture items within the specified tolerance. Most evaluations on process capability indices focus on point estimates, which may result in unreliable assessments of process performance. The index $C_p$ has been widely used in various industries to assess process performance. In this paper, we propose new testing procedure on assessing $C_p$ index for practitioners to use in determining whether a given process is capable. The provided approach is easy to use and the decision making is more reliable. Whether a process is clearly normal or nonnormal, our bootstrap testing procedure could be applied effectively without the complexity of calculation. A numerical result based on the proposed approach is illustrated.

• 소성∙가공
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• 제11권7호
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• pp.581-588
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• 2002

Among the processes to produce micro lens, the process using press molding is a new technology to simplify the process, but it contains many unknown variables. The press-molding process proposed in this paper was simplified into two step process, the first step is the pressing to design the preform for glass element, the second step is the annealing to reduce the residual stress. It is important to estimate the amount of shrinkage of glass gob and the residual stress during process. It Is difficult to evaluate the process variables as mentioned above through the experiment. The influences due to process variables was evaluated by using FEM parametric analysis. The results in this paper can be applicable to produce micro lens.

• 대한안전경영과학회지
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• 제8권3호
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• pp.11-25
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• 2006

It is necessary to deal with the process capability index carefully because it has been developed with certain assumptions. Companies make a decision on processes through the results obtained by using and treating data extracted from the processes. However if they have incorrect or wrong results, they cannot lead to proper outputs but also bring to loss of the competition in quality. Therefore, this study will show a method to analysis Cp (process capability ; CP) and an idea of mass-production on Pp (process performance ; PP) based on the Sigma Estimate which is one of the uncertainty in the process capability index and makes a lot of error. To apply this method, it is essential to understand and to analyze the processes exactly. Especially, it is required to establish the more accurate process capability index that can quickly and properly respond to changes on processes to recognize the small changes on the process which lies in specification in mass production system that the continual monitoring of quality managers is required.

• 한국세라믹학회지
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• 제52권2호
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• pp.133-136
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• 2015

The outward diffusion of $Na^+$ ions in iron-bearing soda lime silicate glass via oxidation heat treatment before the ion exchange process is artificially induced in order to increase the amount of ions exchanged during the ion exchange process. The effect of the addition process is analyzed through measuring the bending strength, the weight change, and the inter-diffusion coefficient after the ion exchange process. The glass strength is increased when the outward diffusion of $Na^+$ ions via oxidation heat treatment before the ion exchange process is added. For the glass subjected to the additional process, the weight change and diffusion depth increase compared with the glass not subjected to the process. The interdiffusion coefficient is also slightly increased as a result of the additional process.

• International Journal of Quality Innovation
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• 제1권1호
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• pp.89-96
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• 2000

Evolutionary operation (EVOP) is a continuous improvement system which explores a region of process operating conditions by deliberately creating some systematic changes to the process variable levels without jeopardizing the product. It is aimed at securing a satisfactory operating condition in full-scale manufacturing processes, which is generally different from that obtained in laboratory or pilot plant experiments. Information on how to improve the process is generated from a simple experimental design. Traditional EVOP procedures are established on the assumption that the variance of the response variable should be small and stable in the region of the process operation. However, it is often the case that process noises have an influence on the stability of the process. This process instability is due to many factors such as raw materials, ambient temperature, and equipment wear. Therefore, process variables should be optimized continuously not only to meet the target value but also to keep the variance of the response variables as low as possible. We propose a scheme to achieve robust process improvement. As a process performance measure, we adopted the mean square error (MSE) of the replicate response values on a specific operating condition, and used the Kruskal-Wallis test to identify significant differences between the process operating conditions.

• International Journal of Quality Innovation
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• 제8권1호
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• pp.137-153
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• 2007

Failure mode and effects analysis (FMEA) is a preventive technique in reliability management field. The successful implementation of FMEA technique can avoid or reduce the probability of system failure and achieve good product quality. The FMEA technique had applied in vest scopes which include aerospace, automatic, electronic, mechanic and service industry. The marking process is one of the back ends testing process that is the final process in semiconductor process. The marking process failure can cause bad final product quality and return although is not a primary process. So, how to improve the quality of marking process is one of important production job for semiconductor testing factory. This research firstly implements FMEA technique in laser marking process improvement on semiconductor testing factory and finds out which subsystem has priority failure risk. Secondly, a CCD position solution for priority failure risk subsystem is provided and evaluated. According analysis result, FMEA and CCD position implementation solution for laser marking process improvement can increase yield rate and reduce production cost. Implementation method of this research can provide semiconductor testing factory for reference in laser marking process improvement.

• 한국레이저가공학회지
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• 제10권3호
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• pp.15-24
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• 2007

In this paper laser micromachining system design process for commercialization is suggested. The constructed system design process is properly adjusted for laser micromachining area after tailoring engine process of system engineering process such as requirement analysis, functional analysis and allocation, system synthesis and system optimization process. In the current laser machining system design, system components and specifications are determined on the basis of experimental experience which a laser is being used in machining some materials as well as the current machining and research trend. In this paper, however, systematic process is suggested in addition to experimental experience, which the laser and system components and their specifications are decided in the process of definition of functional requirements and engine design variables of system to satisfy the customer's requirements.

• 경영과학
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• 제31권3호
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• pp.27-39
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• 2014

We derive a generalized statistic form of Q control chart, which is especially suitable for short run productions and start-up processes, for the detection of process mean shifts. The generalization means that the derived control chart statistic concurrently uses within lot variability and between lot variability to explain the process variability. The latter variability source is noticeably prevalent in lot type production processes including semiconductor wafer fabrications. We first obtain the generalized Q control chart statistic when both the process mean and process variance are unknown, which represents the case of implementing statistical process control charting for short run productions and start-up processes. Also, we provide the corresponding generalized Q control chart statistics for the rest of three cases of previous Q control chart statistics : (1) both the process mean and process variance are known (2) only the process mean is unknown and (3) only the process variance is unknown.