• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.244-248
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• 2013

In this paper, we describe the smart metal casting processing management system, in which we applied case-based reasoning on the window environment. Metal casting is one of the most common casting processes. The metal casting processing is complex and variable depends on a kind of metal casting products. Especially, the metal casting industry has a feature which produces small quantities but produces a lot of different types of metal casting products. And we developed the smart metal casting processing management system which could show the processing route according to the product cases intelligently using the result of case-based reasoning. The experimental result shows that our metal casting processing management system schemes achieves more productivity than manual management schemes.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.1-7
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• 2019

In the modern industrial period, the introduction of mass production was most important progress in civilization. Die-casting process is one of main methods for mass production in the modern industry. The aluminum die-casting in the mold filling process is very complicated where flow momentum is the high velocity of the liquid metal. Actually, it is almost impossible in complex parts exactly to figure the mold filling performance out with the experimental knowledge. The aluminum die-castings are important processes in the automotive industry to produce the lightweight automobile bodies. Due to this condition, the simulation is going to be more critical role in the design procedure. Simulation can give the best solution of a casting system and also enhance the casting quality. The cost and time savings of the casting layout design are the most advantage of Computer Aided Engineering (CAE). Generally, the relations of casting conditions such as injection system, gate system, and cooling system should be considered when designing the casting layout. Due to the various relative matters of the above conditions, product defects such as defect extent and location are significantly difference. In this research by using the simulation software (AnyCasting), CAE simulation was conducted with three layout designs to find out the best alternative for the casting layout design of an automotive Oil Pan_BJ3E. In order to apply the simulation results into the production die-casting mold, they were analyzed and compared carefully. Internal porosities which are caused by air entrapments during the filling process were predicted and also the results of three models were compared with the modifications of the gate system and overflows. Internal porosities which are occurred during the solidification process are predicted with the solidification analysis. And also the results of the modified gate system are compared.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.35-40
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• 2017

A most important progress in civilization was the introduction of mass production. One of main methods for mass production is die-casting molds. Due to the high velocity of the liquid metal, aluminum die-casting is so complex where flow momentum is critical matter in the mold filling process. Actually in complex parts, it is almost impossible to calculate the exact mold filling performance with using experimental knowledge. To manufacture the lightweight automobile bodies, aluminum die-castings play a definitive role in the automotive part industry. Due to this condition in the design procedure, the simulation is becoming more important. Simulation can make a casting system optimal and also elevate the casting quality with less experiment. The most advantage of using simulation programs is the time and cost saving of the casting layout design. For a die casting mold, generally, the casting layout design should be considered based on the relation among injection system, casting condition, gate system, and cooling system. Also, the extent or the location of product defects was differentiated according to the various relations of the above conditions. In this research, in order to optimize the casting layout design of an automotive Oil Pan_BR2E, Computer Aided Engineering (CAE) simulation was performed with three layout designs by using the simulation software (AnyCasting). The simulation results were analyzed and compared carefully in order to apply them into the production die-casting mold. During the filling process with three models, internal porosities caused by air entrapments were predicted and also compared with the modification of the gate system and overflows. With the solidification analysis, internal porosities occurring during the solidification process were predicted and also compared with the modified gate system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.715-725
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• 1994

The objective of this research is to develop a cost model for integrated CAD system. A computerized system realizing this model then is used to aid designers. The first area is to propose a conceptual framework of a multilevel cost model. The methodology of constructing the model is discussed. Then suggestion of an object oriented programming technique for implementing the model is presented. Complicate estimation procedure can be systematically handled by this technique. Interval analysis to deal with the uncertainty of information and decision during design process is used. An experimentation algorithm for calculating the cost distribution is proposed to overcome the shortcoming of interval analysis. Major focus of this research is on net shape manufacturing processes including die casting, injection molding, and metal forming.

• Journal of Environmental Health Sciences
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• v.44 no.1
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• pp.55-62
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• 2018

Objectives: This study set out to measure the heavy metal concentrations in waste water produced in the casting pickling process at dental technical laboratories and examine the actual state of its treatment. Methods:The investigator measured the concentrations of each heavy metal at 55 dental technical laboratories using an inductively coupled plasma optical emission system. Results: The annual usage of electrolytes was under 10 L in 50 (90.9%), and was 10L or more in five (9.1%) laboratories. Among the laboratories, 15 (27.3%) commissioned the treatment of waste,12 (21.8%) treated the waste with general sewage,and 28 (50.9%) treated the waste in aseptic tank. The arithmetic $mean{\pm}standard$ deviation and the geometric mean of chrome(Cr) were $75.3{\pm}50.9$ and 58.3 mg/L; those of cobalt (Co) were $112.3{\pm}106.7$ and 66.1 mg/L; those of nickel (Ni) were $62.9{\pm}83.5$ and 8.9 mg/L; those of molybdenum (Mo) were $17.1{\pm}13.4$ and 12.0 mg/L; those of iron (Fe) were $31.5{\pm}44.1$ and 6.2 mg/L; those of lead (Pb) were $0.3{\pm}0.3$ and 0.3 mg/L; those of beryllium (Be) were $3.6{\pm}3.6$ and 2.0 mg/L. The hydrogen ion concentration was under pH 2 across all the samples. Conclusions: The findings show that the dental technical laboratories were not doing well with the separation, storage, collection, and treatment of the electrolytes they discarded, and that most of the electrolytes were introduced through the general sewage or aseptic tank. The causes of this include alack of perception among the practitioners at dental technical laboratories and contracted companies avoiding collection for economic reasons. There is a need for education to improve the perceptions of waste water treatment among the practitioners at dental technical laboratories. Environment-related departments should be stricter with legal applications in the central and local governments. It is also required to provide proper management of commissioned treatment.

• Journal of Korea Foundry Society
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• v.41 no.6
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• pp.550-557
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• 2021

With the advent of the fourth industrial revolution, the interest in the internet of things (IoT) in manufacturing is growing, even at foundries. There are several types of process data that can be automatically collected at a foundry, but considerable amounts of process data are still managed based on handwriting for reasons such as the limited functions of outdated production facilities and process design based on operator know-how. In particular, despite recognizing the importance of converting process data into big data, many companies have difficulty adopting these steps willingly due to the burden of system construction costs. In this study, the field applicability of IoT-based devices was examined by manufacturing devices and applying them directly to the site of a centrifugal foundry. For the centrifugal casting process, the temperature and humidity of the working site, the molten metal temperature, and mold rotation speed were selected as process parameters to be collected. The sensors were selected in consideration of the detailed product specifications and cost required for each process parameter, and the circuit was configured using a NodeMCU board capable of wireless communication for IoT-based devices. After designing the circuit, PCB boards were prepared for each parameter, and each device was installed on site considering the working environment. After the on-site installation process, it was confirmed that the level of satisfaction with the safety of the workers and the efficiency of process management increased. Also, it is expected that it will be possible to link process data and quality data in the future, if process parameters are continuously collected. The IoT-based device designed in this study has adequate reliability at a low cast, meaning that the application of this technique can be considered as a cornerstone of data collecting at foundries.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.312-320
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• 2023

Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.