• Journal of Platform Technology
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• v.9 no.3
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• pp.52-62
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• 2021

Quality control is critical at manufacturing sites and is key to predicting the risk of quality defect before manufacturing. However, the reliability of manual quality control methods is affected by human and physical limitations because manufacturing processes vary across industries. These limitations become particularly obvious in domain areas with numerous manufacturing processes, such as the manufacture of major nuclear equipment. This study proposed a novel method for predicting the risk of quality defects by using natural language processing and machine learning. In this study, production data collected over 6 years at a factory that manufactures main equipment that is installed in nuclear power plants were used. In the preprocessing stage of text data, a mapping method was applied to the word dictionary so that domain knowledge could be appropriately reflected, and a hybrid algorithm, which combined n-gram, Term Frequency-Inverse Document Frequency, and Singular Value Decomposition, was constructed for sentence vectorization. Next, in the experiment to classify the risky processes resulting in poor quality, k-fold cross-validation was applied to categorize cases from Unigram to cumulative Trigram. Furthermore, for achieving objective experimental results, Naive Bayes and Support Vector Machine were used as classification algorithms and the maximum accuracy and F1-score of 0.7685 and 0.8641, respectively, were achieved. Thus, the proposed method is effective. The performance of the proposed method were compared and with votes of field engineers, and the results revealed that the proposed method outperformed field engineers. Thus, the method can be implemented for quality control at manufacturing sites.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.2
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• pp.27-33
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• 2012

A marine propeller is designed for preventing cavitation priority. Cavitation is a phenomenon which is defined as the vibration or noise by dropping the pressure on the high-speed rotation of the propeller. There has to be a enough thrust on the low-speed rotation for preventing cavitation. Thus, it has to be considered in the increasing of the number of blade and the angle of wing to design the propeller. In addition, flow resistance will be increasing by narrowing the width between blades. So high quality surface roughness of the hub to minimize flow resistance is required. Interference problems with tool and neighboring surfaces often take place from this kind of characteristics of the propeller. During 5-Axis machining of these propellers, the excessive local interference avoidance, necessary to avoid interference, leads to inconsistency of cutter posture, low quality of machined surface. Therefore, in order to increase the surface quality, it is necessary to minimize the cutter posture changes and create a continuous tool path while avoiding interference. This study, by using a MC-space algorithm for interference avoidance and a MB-spline algorithm for continuous control, is intended to create a 5-Axis machining tool path with excellent surface quality. Also, an effectiveness is confirmed through a verification manufacturing.

• International Journal of Quality Innovation
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• v.7 no.2
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• pp.141-156
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• 2006

This study describes a novel algorithm for optimizing the quality yield of silicon wafer slicing. 12 inch wafer slicing is the most difficult in terms of semiconductor manufacturing yield. As silicon wafer slicing directly impacts production costs, semiconductor manufacturers are especially concerned with increasing and maintaining the yield, as well as identifying why yields decline. The criteria for establishing the proposed algorithm are derived from a literature review and interviews with a group of experts in semiconductor manufacturing. The modified Delphi method is then adopted to analyze those results. The proposed algorithm also incorporates the analytic hierarchy process (AHP) to determine the weights of evaluation. Additionally, the proposed algorithm can select the evaluation outcomes to identify the worst machine of precision. Finally, results of the exponential weighted moving average (EWMA) control chart demonstrate the feasibility of the proposed AHP-based algorithm in effectively selecting the evaluation outcomes and evaluating the precision of the worst performing machines. So, through collect data (the quality and quantity) to judge the result by AHP, it is the key to help the engineer can find out the manufacturing process yield quickly effectively.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.182-188
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• 2000

High-frequency electric resistance welding (HERW) technique is one of the most productive manufacturing method currently available for pipe and tube production because of its high welding speed. In this process, a heat input is controlled by skilled operators observing color and shape of bead but such a manual control can not provide reliability and stability required for manufacturing pipes of high grade quality because of a variety of bead shapes and noisy environment. In this paper, in an effort to provide reliable quality inspection, we propose a neural network-based method for classification of bead shape. The proposed method utilizes the structure of Kohonen network and is designed to learn the skill of the expert operators and to provide a good solution to classify bead shapes according to their welding conditions. This proposed method is implemented on the real pipe manufacturing process, and a series of experiments are performed to show its effectiveness.

• Journal of Korean Society for Quality Management
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• v.22 no.4
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• pp.79-101
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• 1994

In modern manufacturing, a product consists of many components created by different processes. Variations in the individual component dimensions and in the processes may result in unacceptable final assemblies. Thus, engineers have increased pressure to properly set tolerance specifications for individual components and to control manufacturing processes. When a proper variation stack-up analysis is not performed for all of the components in a functional system, all component parts can be within specifications, but the final assembly may not be functional. Thus, in order to improve the performance of the final assembly, a proper variation stack-up analysis is essential for specifying dimensional tolerances and process control. This research provides a detailed case example of the use of variation stack-up analysis using a Monte Carlo simulation method to improve the defect rate of a complex process, which is the commutator brush track undercut process of an armature assembly of a small motor. Variations in individual component dimensions and process mean shifts cause high defect rate, Since some dimensional characteristics have non-normal distributions and the stack-up function is non-linear, the Monte Carlo simulation method is used.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.3
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• pp.501-514
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• 1997

Manufacturing is fast entering a new age of industrial excellence that is being called "Agile Manufacturing." The goal of Agile Manufacturing is to link customers, suppliers, and the manufacturing system into a super-efficient confederation to produce a variety of products quickly and at a low cost. In order to improve the quality of the study of production input control(PIC) in a job shop manufacturing system by reducing the significant gap between research models and models of actual manufacturing systems, the previous line of research on PICs in a job shop manufacturing system is extended by integrating customers and suppliers with the manufacturing system. Then, a set of measures is developed to evaluate PICs, measures that reflect concerns of customers and suppliers as well as concerns of the manufacturer. Also, a weighted overall measure (with various cases to represent different possible weights of manufacturer's emphasis on the performance measures) is used to synthesize all the performance measures. Then, for each case, various existing PICs are evaluated in combination with various priority dispatching rules(PDRs).

• IE interfaces
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• v.8 no.3
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• pp.249-257
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• 1995

Expert system is an effective approach for quality system to be automated and thus to be an essential integrating mechanism in any move towards CIM(Computer Integrated Manufacturing). A quality control expert system is introduced and its relationship to CIM is illustrated in a case study. Process control expert system developed by Kim and Sin[6] has been improved via ODBC(Open DataBase Connectivity) for efficient information network, graph representation using Windows API for rapid response and some new rules for identification of patterns in control charts.

• Korean Management Science Review
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• v.10 no.2
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• pp.111-120
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• 1993

The most important factors for a product to survive in the market are cost and quality. In recent years, quality proceeds to cost. There are many techniques of use to improve the quality of a product. One of the techniques is applying statistical methods (especially Taguchi method) to real operational conditions for a continuous manufacturing process in P company. There are 91 factors to control in the process. So, we predetermined 7 main effect factors and 6 interactive effect factors by statistical methods and advices of engineers. With these 13 factors, we determined the optimal level of operations for the process.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2054-2060
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• 2008

This paper aims at the performance enhancement of tension controller for the yarn manufacturing process. The tension controller is required to keep the tension constant while the yarn is manufactured by a draw and twist machine, which is essential and critical for good quality production of yarn, steel, paper, etc. This paper proposes a linear model of tension control plant to develop a precise tension control system, which is derived by the close observation of the conventional mathematical model of motor driving and tension control systems. It is shown by experiments that the proposed control system precisely maintains the tension constant within the error bound of 0.05% while the conventional PI controller has about 0.2% error. The control performance of the system has been compared to that of conventional PI control not only for constant speed control but also for transient speed control experiments.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.20-26
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• 2017

Process control is crucial in many industries, especially in semiconductor manufacturing. In such large-volume multistage manufacturing systems, a product has to go through a very large number of processing steps with reentrant) before being completed. This manufacturing system has many machines of different types for processing a high mix of products. Each process step has specific quality standards and most of them have nonlinear dynamics due to physical and/or chemical reactions. Moreover, many of the processing steps suffer from drift or disturbance. To assure high stability and yield, on-line quality monitoring of the wafers is required. In this paper we develop a real-time fault detection system on semiconductor manufacturing process. Proposed system is superior to other incremental fault detection system and shows similar performance compared to batch way.