• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.4
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• pp.80-86
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• 2021

Plastics are widely used in mechanical and other fields due to their light weight, design flexibility, and molding processability. In processing plastics, defective products are mixed and reprocessed to improve production efficiency and reduce costs. In this study, an experiment was conducted to confirm the coefficient of thermal expansion of HDPE during this reprocessing. The coefficient of thermal expansion was measured at different measurement directions and heating rates. As a result, we observed that the coefficient of thermal expansion in the direction perpendicular to the injection direction is greater than that in the horizontal direction.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.123-128
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• 2018

When the injection molding system molds some plastic products, defective product rate will be increased if plastic materials have some excessive moisture content. Therefore, it is very important to control the dehumidification and drying of plastic material. Since the moisture content of the plastic material may change from time to time depending on the material and the molding process, it is necessary to observe the change in real time and maintain a constant moisture content. To solve these problems, I proposed a smart factory system model for plastic molding in this paper. In addition, I designed the interface module to be installed in the dehumidifying dryer which is the core of this process. In addition to this, performance tests were conducted to check the effectiveness and the results were verified as valid.

• The Korean Journal of Air & Space Law and Policy
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• v.20 no.2
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• pp.59-158
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• 2005

When a military aircraft suffers damages due to the defects in its design, manufacturing or notification, all of which are generally understood as products liability defects, the obvious compensation is sought as it would in other consumer good case. However, there exist clear yet unappreciated difference between general consumer goods and military aircraft, as far as products liability law is concerned - some sort of recovery should be obtained even when there exist only defects, not damages, to the aircraft because of the implication of defective parts is much grave than what can be expected in a consumer goods case. While certain anticipatory measures do exist in manual or at negotiation stages for the safety of military aircraft, such measures are ineffective, if not ambiguous, in recovery effort in the post-accident stage In another word, the standardized military procurement contract manuals and boilerplate forms do not appreciate the unique and dangerous military nature of military aircraft. There are many unique legal issues which can arise when trying to prevent defective aircraft or parts, or to recover compensations for accident due to such defects. At two-level, the government should establish legal system (or countermeasures if you'd like) for purchasing safer military aircraft. First, one should be able to work with legal ground and policy that allows selecting and purchasing safer goods - the purpose of such contract is not litigious, but rather in acquiring what are most reliable. Second, in case the defects do arise and lead to damages, solid legal principles and instructions should be established for effectively pursuing appropriate company, (usually a aerospace industry giant with much experience) for products liability - the purpose of such pursuit is inevitable for a public official, since he or she is no private business man with much flexibilities, even to the point of waiving such compensatory right for future business purposes. This article tries to identify problems in methods of procuring military aircraft or parts - after reviewing on how the military can improve on legal and policy grounds for procuring what will be the focus of future military strength, it will offer some of the ways to effectively handling and resolving a liability issues.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.130-139
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• 2016

The purpose of this paper is to analyze the problems and the sources of defective products and draw improvement plans in a small plastic boat manufacturing process using TOC (Theory Of Constraints) and statistical analysis. TOC is a methodology to present a scheme for optimization of production process by finding the CCR (Capacity Constraints Resource) in the organization or the all production process through the concentration improvement activity. In this paper, we found and reformed constraints and bottlenecks in plastic boat manufacturing process in the target company for less defect ratio and production cost by applying DBR (Drum, Buffer, Rope) scheduling. And we set the threshold values for the critical process variables using statistical analysis. The result can be summarized as follows. First, CCRs in inventory control, material mix, and oven setting were found and solutions were suggested by applying DBR method. Second, the logical thinking process was utilized to find core conflict factors and draw solutions. Third, to specify the solution plan, experiment data were statistically analyzed. Data were collected from the daily journal addressing the details of 96 products such as temperature, humidity, duration and temperature of heating process, rotation speed, duration time of cooling, and the temperature of removal process. Basic statistics and logistic regression analysis were conducted with the defection as the dependent variable. Finally, critical values for major processes were proposed based on the analysis. This paper has a practical importance in contribution to the quality level of the target company through theoretical approach, TOC, and statistical analysis. However, limited number of data might depreciate the significance of the analysis and therefore it will be interesting further research direction to specify the significant manufacturing conditions across different products and processes.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.129-136
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• 2017

Among the information technology and automation that have rapidly developed in the manufacturing industries recently, tens of thousands of quality variables are estimated and categorized in database every day. The former existing statistical methods, or variable selection and interpretation by experts, place limits on proper judgment. Accordingly, various data mining methods, including decision tree analysis, have been developed in recent years. Cart and C5.0 are representative algorithms for decision tree analysis, but these algorithms have limits in defining the tolerance of continuous explanatory variables. Also, target variables are restricted by the information that indicates only the quality of the products like the rate of defective products. Therefore it is essential to develop an algorithm that improves upon Cart and C5.0 and allows access to new quality information such as loss cost. In this study, a new algorithm was developed not only to find the major variables which minimize the target variable, loss cost, but also to overcome the limits of Cart and C5.0. The new algorithm is one that defines tolerance of variables systematically by adopting 3 categories of the continuous explanatory variables. The characteristics of larger-the-better was presumed in the environment of programming R to compare the performance among the new algorithm and existing ones, and 10 simulations were performed with 1,000 data sets for each variable. The performance of the new algorithm was verified through a mean test of loss cost. As a result of the verification show, the new algorithm found that the tolerance of continuous explanatory variables lowered loss cost more than existing ones in the larger is better characteristics. In a conclusion, the new algorithm could be used to find the tolerance of continuous explanatory variables to minimize the loss in the process taking into account the loss cost of the products.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.27-37
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• 2017

As the global uncertainty of manufacturing has increased and the quality problem has become global, the recall has become a fatal risk that determines the durability of the company. In addition, as the convergence of PSS (product-service system) product becomes common due to the development of IT convergence technology, if the function of any part of hardware or software does not operate normally, there will be a problem in the entire function of PSS product. In order to manage the quality of such PSS products in a stable manner, a new approaches is needed to analyze and manage the hardware and software parts at the same time. However, the Fishbone diagram, FTA, and FMEA, which are widely used to interpret the current quality problem, are not suitable for analyzing the quality problem by considering the hardware and software at the same time. In this paper, a quality risk assessment model combining FTA and FMEA based on defect rate to be assessed daily on site to manage quality and fishbone diagram used in group activity to solve defective problem. The proposed FTA-FMEA based risk assessment model considers the system structure characteristics of the defect factors in terms of the relationship between hardware and software, and further recognizes and manages them as risk. In order to evaluate the proposed model, we applied the functions of ITS (intelligent transportation system). It is expected that the proposed model will be more effective in assessing quality risks of PSS products because it evaluates the structural characteristics of products and causes of defects considering hardware and software together.

• Journal of Korean Society for Quality Management
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• v.49 no.3
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• pp.421-431
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• 2021

Purpose: For improving outgoing quality, this study presents a novel sampling framework based on predictive analytics. Methods: The proposed framework is composed of three steps. The first step is the variable selection. The knowledge-based and data-driven approaches are employed to select important variables. The second step is the model learning. In this step, we consider the supervised classification methods, the anomaly detection methods, and the rule-based methods. The applying model is the third step. This step includes the all processes to be enabled on real-time prediction. Each prediction model classifies a product as a target sample or random sample. Thereafter intensive quality inspections are executed on the specified target samples. Results: The inspection data of three Samsung products (mobile, TV, refrigerator) are used to check functional defects in the product by utilizing the proposed method. The results demonstrate that using target sampling is more effective and efficient than random sampling. Conclusion: The results of this paper show that the proposed method can efficiently detect products that have the possibilities of user's defect in the lot. Additionally our study can guide practitioners on how to easily detect defective products using stratified sampling

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.531-538
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• 2017

In the case of hydraulic pressure type fuse, we accept or reject certain product lots by considering the number of defective products in the operating pressure test. Generally, this procedure, known as 'The inspection by attributes', has been most commonly used in the field of quality assurance of products. However, the method of inspection by attributes suffers because it tests more samples than inspection by variables. Even though the quality of the products has remained stable in the process condition, the same number of samples is required for every lot, which wastes time and money. This paper suggests that the lot acceptance procedure is changed from inspection by attributes to inspection by variables. We can calculate the statistical tolerance percent of defectives and compare this to the Acceptable Quality Level (AQL) in order to save money and time. It is also easier to monitor and control the quality of products by using the process capability index and x-bar charts. In conclusion, the procedure delivers mutual benefit to both the customer and the producer by securing high quality products and reference data.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.345-350
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• 2014

The most important decision of process planning for the manufacturing system is the machine selection problem to minimize machining costs. Each machine has its own different machining performance indicating a different fraction of scrap, making the cost of scrap generated by machining is different for each machine. Therefore, when we decide on machine selection, we must consider the machining cost and the cost of scrap generated. This paper describes the statistical model for the fraction of scrap generated by machining and the machine selection algorithm considering the total cost including the machining cost and the cost of scrap generated.

• IE interfaces
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• v.25 no.1
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• pp.40-51
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• 2012

Today, many manufacturing companies realize that collaboration is crucial for their survival. Especially, in the perspective of quality, the importance of collaboration is emphasized because economic loss increases exponentially while defective parts go through the process in supply chain. However, the manufacturing companies are facing two main difficulties in implementing collaborative relationships with their suppliers. First, it is difficult for the suppliers to produce reliable products due to their obsolete facilities. The problem gets worse for second- or third-tire vendors. Second, the companies experience the lack of universally understandable set of terminology and effective methodologies for knowledge representation. Ontology is one of the best approaches to expressing and processing a domain knowledge. In this paper, we propose the manufacturing ontology-based quality prediction framework to represent and share the knowledge of industrial environment and to predict product quality in manufacturing processes. In addition, we develop the ontology-based quality prediction system based on the proposed framework. We carried out a series of experiments for an injection molding process at an automotive part supplier. The experimental results demonstrated that the proposed framework and system can be successfully applicable in manufacturing industry.