• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1138-1143
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• 2015

Effects of fabrication process variations on impedance of microelectrodes integrated on a neural probe were examined through equivalent circuit modeling and SPICE simulation. Process variation and the corresponding range were estimated based on experimental data. The modeling results illustrate that the process variation induced by metal etching process was the dominant factor in impedance variation. We also demonstrate that the effect of process variation is frequency dependent. Another process variation that was examined in this work was the thickness variation induced by deposition process. The modeling results indicate that the effect of thickness variation on impedance is negligible. This work provides a means to predict the variations in impedance values of microelectrodes on neural probe due to different process variations.

• International Journal of Quality Innovation
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• v.1 no.1
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• pp.97-105
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• 2000

In this paper, a two-section method for measuring is introduced and the variation sources of measurement process are analysed. Measuring is a special process in general process. Various variation source must be firstly decomposed so that the statistical distribution law of measuring process can be established, and then implement monitoring control of the measuring process. A special method to obtain the measuring variation is discussed, and a monitoring control technique for measuring process is studied based statistical distribution. Towards the end, we briefly introduce software design for the analysis and control of a measurement process.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.285-294
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• 2002

The control chart is widely used tool for monitoring of the process. This paper deals with an interpretation of the process capability index(PCI) and process performance index(PPI) when the control chart is used for monitoring of the process. The main difference between the PCI and PPI is how we did estimate the process deviation. The PCI uses the within process variation and the PPI uses the total process variation, which is conceptually defined as sum of between process variation and within process variation. Easy interpretations of the PCI and PPI for field engineers are described. And some new approaches to interpretation of the process capability and performance index may be useful in early stabilization of new process.

• Journal of Korean Society for Quality Management
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• v.38 no.2
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• pp.171-179
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• 2010

Pearn et al.(2002) supposed the $C_{pp}$ multiple process performance analysis chart. This chart displays multiple processes with the process variation and process departure on one single chart. But, this chart can not display the distribution of the process variation and process departure and is inappropriate for processes with non-normal distributions. With bootstrapping method, we can display the distribution of the process variation and process departure on the $C_{pp}$ multiple process performance analysis chart.

• 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.

• Proceedings of the Safety Management and Science Conference
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• 2011.11a
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• pp.551-556
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• 2011

This research presents an implementation strategy of Process Capability Index (PCI) according to the types of process characteristics. The types of process feature are classified as four perspectives of variation range, time period, error position, and process stage. The paper examines short-term or long-term PCI, within or between variation, position of precision or accuracy, and inclusion of measurement or calibration stage. Moreover, the study proposes normality test of unilateral PCI.

• Korean Journal of Construction Engineering and Management
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• v.20 no.1
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• pp.105-113
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• 2019

A construction project is a complex process consisting of interdependent tasks. If one task has variation, other tasks will be affected additionally and it is difficult to reach consensus on task variation due to the characteristics of the construction site that is going on quickly and complicatedly. Therefore, this study identified the root cause of task variation in construction process. Adjacent matrix was created with the tasks corresponding to major causes of variation, and the network analysis was performed. The results of social network analysis in the construction process can stabilize the work flow in the complex process and improve the reliability of the process plan and the project performance.

• 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.

• IE interfaces
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• v.18 no.4
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• pp.444-453
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• 2005

The purpose of the Gage R&R study is to determine whether a measurement system is adequate for monitoring a process. If the measurement system variation is small relative to the process variation, then the measurement system is deemed "adequate". The sources of variation associated with the measurement system are compared using an analysis of variance (ANOVA) model, in general. A typical ANOVA model used in a standard Gage R&R study is the two-factor random effect model. Then, the ANOVA partitions the total variation into three categories: repeatability, reproducibility, part variation. However, if the process variation possesses the between group variation, within group variation, and within part variation, these variations can cause the measurement system evaluation to provide misleading results. That is, in the standard Gage R&R study these variations affect the estimate of repeatability, reproducibility, or both. This paper presents a four-factor nested factorial ANOVA model which explicitly considers these variations for the Gage R&R study. The variance component estimators are derived by setting the EMS equations equal to the corresponding mean square from the ANOVA table and solving. And the proposed model is compared with the standard Gage R&R model.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.553-571
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• 2019

Geometric variation including welding distortion accumulates as many parts are joined together, ultimately affecting the final product. This variation is then subjected to correction, which requires considerable effort, time, and cost. This variation can be categorized as in-plane/out-of-plane variation. To date, studies on variation simulation have largely focused on the out-of-plane variation, however the variation generated in the in-plane direction requires more time and efforts to correct afterwards. This research aims to construct a variation simulation model considering both the in-plane and out-of-plane variations. A geometric analysis was performed to derive an equation that reflects the coupling effect of the out-of-plane variation on the in-plane variation. The proposed model is validated with case study analysis and the results shows that good fidelity in predicting and diagnosing the in-plane variation during the block assembly process considering welding distortion.