• Journal of the Korea Safety Management & Science
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• v.21 no.3
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• pp.11-16
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• 2019

The latest issue is the smart factory. In order to implement this smart factory, the most fundamental element is to establish product specifications for factors affecting the product, obtain useful data to analyzed and predicted, and maintain safety. But most manufacturers have many errors. Therefore, the purpose of this study is to verify factors of product through statistical techniques and to study the process control and safety.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.353-354
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• 2006

The automatic inspection system is developed for the formed end part of automotive fuel pipe. The developed system has functions of outer diameter and formed end length measurement by LVDT(linear variable differential transformer) together with burr cleaning of automotive fuel pipe. The measured data are managed and controlled in real time by embedded SPC(statistical process control) program. The system is composed of mechanical part, electronic part and developed software system. These three parts operate automatically by mutual communication with each other. The developed system showed good results in finding inferior goods and efficiency improvement of the fuel pipe production line. It also eliminated the unreliable manual inspection processes and improved the confidence of product quality.

• International Journal of Quality Innovation
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• v.9 no.3
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• pp.71-91
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• 2008

The definition of cycle time is the time from the wafer start to the wafer output. It usually takes one or two months to get the product since customer decides to produce it. The cycle time is a critical factor for customer satisfaction because it represents the response time to the market. Long cycle time reflects the ineffective investment for the capital. The cycle time is very important for foundry because long cycle time will cause customer unsatisfied and the order loss. Consequently, all of the foundries put lots of human source in the cycle time improvement. Usually, we make decisions based on the experience in the cycle time management. We have no mechanism or theory for cycle time management. We do work-in-process (WIP) management based on turn rate and standard WIP (STD WIP) set by experiences. But the experience didn't mean the optimal solution, when the situation changed, the cycle time or the standard WIP will also be changed. The experience will not always be applicable. If we only have the experience and no mechanism, management will not be work out. After interview several foundry fab managers, all of the fab can't reflect the situation. That is, all of them will have an impact period after product mix or utilization varied. In this study, we want to develop a formula for standard WIP and use statistical process control (SPC) concept to set WIP upper/lower limit level. When WIP exceed the limit level, it will trigger action plans to compensate WIP Profile. If WIP Profile balances, we don't need too much WIP. So WIP level could be reduced and cycle time also could be reduced.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.289-299
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• 2000

This study provides an application of datamining approach to CQI(Continuous Quality Improvement) using the discharge summary. First, we found a process variation in hospital infection rate by SPC (Statistical Process Control) technique. Second, importance of factors influencing hospital infection was inferred through the decision tree analysis which is a classification method in data-mining approach. The most important factor was surgery followed by comorbidity and length of operation. Comorbidity was further divided into age and principal diagnosis and the length of operation was further divided into age and chief complaint. 24 rules of hospital infection were generated by the decision tree analysis. Of these, 9 rules with predictive prover greater than 50% were suggested as guidelines for hospital infection control. The optimum range of target group in hospital infection control were Identified through the information gain summary. Association rule, which is another kind of datamining method, was performed to analyze the relationship between principal diagnosis and comorbidity. The confidence score, which measures the decree of association, between urinary tract infection and causal bacillus was the highest, followed by the score between postoperative wound disruption find postoperative wound infection. This study demonstrated how datamining approach could be used to provide information to support prospective surveillance of hospital infection. The datamining technique can also be applied to various areas fur CQI using other hospital databases.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.52-61
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• 2012

The design method for cumulative sum (CUSUM) control charts, which can be robust to autoregressive moving average (ARMA) modeling errors, has not been frequently proposed so far. This is because the CUSUM statistic involves a maximum function, which is intractable in mathematical derivations, and thus any modification on the statistic can not be favorably made. We propose residual-based robust CUSUM control charts for monitoring autocorrelated processes. In order to incorporate the effects of ARMA modeling errors into the design method, we modify parameters (reference value and decision interval) of CUSUM control charts using the approximate expected variance of residuals generated in model uncertainty, rather than directly modify the form of the CUSUM statistic. The expected variance of residuals is derived using a second-order Taylor approximation and the general form is represented using the order of ARMA models with the sample size for ARMA modeling. Based on the Monte carlo simulation, we demonstrate that the proposed method can be effectively used for statistical process control (SPC) charts, which are robust to ARMA modeling errors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.30
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• pp.117-123
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• 1994

To improve quality of an product We have adopted new approaches such as SPC(Statistical Process Control), CIM(compure Integreated Munufactture), QDF(Quality Development Function), etc. In spite of using these approaches, an organization should fail to achive its true goal if that organization didn't developed its cooperative quality culture which is the total of collective and shared learning of quality-related values as the organization have developed its capacity to survive in its external environment and to manage its own internal affairs. In this paper, I analyzed the current quality culture of Korea Telecom and tried to suggest how Korea Telecom develop its service quality based on the quality culture.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.4 s.175
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• pp.811-823
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• 2000

In design and manufacturing airframe structures which are composed of a lot of sub-assemblies and large complex profile shapes it is difficult to reduce so called hardware variations. Accordingly cost increasing factors for manufacturing airframe parts are much more than other machine parts because of the variability of fabricated details and assemlies. To improve cost and quality, accurate assembly methods and DPD techniques are proposed in this paper which are based upon using CAD/CAM techniques, the concept of KC's and the coordinated datum and index throughout the design, tooling, manufacturing and inspection. The proposed methods are applied to produce fuselage frame assemblies and related engineering aspects are described regarding the design of parts and tools in the context of concurrent digital definition. First articles and consequent mass production of frame assemblies shows a great improvement of the process capability ratio from 0.7 by the past processes to 1.0 by the proposed methods in addition to the cost reduction due to the less number of tools, reduced total assembly times and the space compaction needed by massive inventory. The need to achieve better Cpk, however, and future studies to be investigated will be addressed briefly.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.121-128
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• 2021

In order to design a real time big data collection and analysis system of manufacturing data in a smart factory, it is important to establish an appropriate wired/wireless communication system and protocol. This paper introduces the latest communication protocol, OPC-UA (Open Platform Communication Unified Architecture) based client/server function, applied user interface technology to configure a network for real-time data collection through IoT Integration. Then, Database is designed in MES (Manufacturing Execution System) based on the analysis table that reflects the user's requirements among the data extracted from the new cutting process automation process, bush inner diameter indentation measurement system and tool monitoring/inspection system. In summary, big data analysis system introduced in this paper performs SPC (statistical Process Control) analysis and visualization analysis with interface of OPC-UA-based wired/wireless communication. Through AI learning modeling with XGBoost (eXtream Gradient Boosting) and LR (Linear Regression) algorithm, quality and visualization analysis is carried out the storage and connection to the cloud.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.255-266
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• 2022

The quality control of coarse aggregate among aggregates, which are the main ingredients of concrete, is currently carried out by SPC(Statistical Process Control) method through sampling. We construct a smart factory for manufacturing innovation by changing the quality control of coarse aggregates to inspect the coarse aggregates based on this image by acquired images through the camera instead of the current sieve analysis. First, obtained images were preprocessed, and HED(Hollistically-nested Edge Detection) which is the filter learned by deep learning segment each object. After analyzing each aggregate by image processing the segmentation result, fineness modulus and the aggregate shape rate are determined by analyzing result. The quality of aggregate obtained through the video was examined by calculate fineness modulus and aggregate shape rate and the accuracy of the algorithm was more than 90% accurate compared to that of aggregates through the sieve analysis. Furthermore, the aggregate shape rate could not be examined by conventional methods, but the content of this paper also allowed the measurement of the aggregate shape rate. For the aggregate shape rate, it was verified with the length of models, which showed a difference of ±4.5%. In the case of measuring the length of the aggregate, the algorithm result and actual length of the aggregate showed a ±6% difference. Analyzing the actual three-dimensional data in a two-dimensional video made a difference from the actual data, which requires further research.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.27-32
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• 1997

Continual monitoring of abnormal operating conditions i a key issue in maintaining high product quality and safe operation, since the undetected process abnormality may lead to the undesirable operations, finally producing low quality products, or breakdown of equipment. The statistical projection method recently highlighted has the advantage of easily building reference model with the historical measurement data in the statistically in-control state and not requiring any detailed mathematical model or knowledge-base of process. As the complexity of process increases, however, we have more measurement variables and recycle streams. This situation may not only result in the frequent occurrence of process Perturbation, but make it difficult to pinpoint trouble-making causes or at most assignable source unit due to the confusing candidates. Consequently, an ad hoc skill to monitor and diagnose in plat-wide scale is needed. In this paper, we propose a hierarchical plant-wide monitoring methodology based on hierarchical decomposition and principal component analysis for handling the complexity and interactions among process units. This have the effect of preventing special events in a specific sub-block from propagating to other sub-blocks or at least delaying the transfer of undesired state, and so make it possible to quickly detect and diagnose the process malfunctions. To prove the performance of the proposed methodology, we simulate the Tennessee Eastman benchmark process which is operated continuously with 41 measurement variables of five major units. Simulation results have shown that the proposed methodology offers a fast and reliable monitoring and diagnosis for a large scale chemical plant.