• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 1999.10a
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• pp.12-12
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• 1999

온도에 따라 물성치가 변화하는 재료의 열응력 예측은 연속주조공정에 의한 제품 생산에서 중요하다. 연속주조공정에서 금속이 급속히 냉각됨으로 인하여 응력이 크게 발생될 뿐만 아니라 금속 내부에 크랙이 발생될 수 있으며, 이는 최종제품의 품질에 영향을 미칠 수 있다. 따라서 연속주조공정에서 양호한 주조제품을 얻기 위해서는 냉각조건 등과 같은, 주조시 수반되는 여러가지 주조결함의 원인을 제어해 주어야한다. 주조결함에는 주물 주입에 기인하는 결함과 주입 완료 후 응고과정에서 주물의 수축으로 기인하는 결함이 있다. 공기 및 가스의 포집, 개재물의 혼입 등이 전자에 속하며, 응고층 내부의 온도차, 응고수축(solidification shrinkage), 응력변형 등으로 인한 주물변형 및 표면결함 등이 후자에 속한다. 주물의 응고시에 고상화된 영역에서의 온도구배와 시간에 따른 온도변화는 금속내부에서의 열변형으로 인한 열응력을 발생시키고, 이것은 잔류응력이나 크랙 등과 같은 최종제품의 결함의 원인이 될 수 있다.

• Proceedings of the Materials Research Society of Korea Conference
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• 2011.05a
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• pp.55.1-55.1
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• 2011

의료용 고속 에어터빈 핸드피스는 주로 치과에서 치아 절삭 및 치료에 사용하는 도구로써 치의학 분야에서 가장 폭넓게 사용되는 의료장비 중 하나이다. 핸드피스용 소재는 황동 혹은 스테인레스 강이 주로 사용되었으나, 최근 고내식성, 인체친화성이 높은 티타늄을 사용한 가벼운 재질의 핸드피스 제품들이 개발되어 독일 및 일본을 중심으로 출시되고 있다. 티타늄으로 제조되는 부분은 헤드와 바디로써, 본 연구에서는 에어터빈의 케이스에 해당하는 헤드부 제조에 있어서 정밀주조 공정 적용의 타당성을 조사하였다. 티타늄은 특히 용융점이 높고 유동성이 좋지 않을 뿐만 아니라 주형과의 반응성이 높기 때문에 주조 시 제품 내/외 부에 결함발생 빈도가 높다. 이와 같은 주조결함 제어를 위하여 상용 유한요소 프로그램인 ProCASTTM을 사용하여 핸드피스 헤드 제조 시 유동 및 응고해석을 수행하였다. 또한 헤드 부 형상에 따른 주조성을 조사하여 이를 바탕으로 최적 주조방안 도출을 위한 정밀주조 공정해석 연구를 수행하였다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1243-1248
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• 2011

Internal defects are a major concern in the casting process because they have a significant influence on the strength and fatigue life of casting products. In general, they cause stress concentration and can be a starting point of cracks. Therefore, it is important to understand the effects of internal defects on mechanical properties such as fatigue life. In this study, fatigue experiments on tensile specimens with internal defects were performed. The internal defects in the casting product were scanned by an industrial CT scanner, and its shape was simplified by ellipsoidal primitives for the structural and fatigue analysis. The analysis results were compared with experimental results for casting products with internal defects. It was demonstrated that it is possible to consider internal defects of casting products in stress and fatigue analysis. The proposed method provides a tool for the prediction of the fatigue life of casting products and the investigation of the effects of internal defects on mechanical performance.

• Journal of Korea Foundry Society
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• v.12 no.4
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• pp.285-292
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• 1992

• Journal of Korea Foundry Society
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• v.42 no.4
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• pp.209-217
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• 2022

In the present study, in order to prevent the misrun defects that occur during traditional brass casting, a method for selecting the proper casting process conditions is proposed. A learning model was developed and demonstrated to be able to learn the presence or absence of defects according to the casting process conditions and to predict the occurrence of defects depending on the certain process given. Appropriate process conditions were determined by applying the proposed method, and the determined conditions were verified through a comparison of different simulation results with additional conditions. With this method, it is possible to determine the casting process conditions that will prevent defects in the desired sand model. This technology is expected to contribute to realization of smart traditional brass farming workshops.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.12
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• pp.1599-1604
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• 2012

In general, internal defects, such as shrinkage in casting, cause stress concentration and can be a starting point for cracks. Therefore, it is important to understand the effects of internal defects on the mechanical properties including the impact behavior. This study aim is to evaluate the effects of internal casting defects on the impact performance of Al-alloy castings. Both an experimental method and computational analysis were used to achieve the research objective. The internal defects in the casting were scanned using an industrial CT scanner, and their shape was simplified using ellipsoidal primitives for impact analysis. The good agreement between the experimental and computer simulation results verified the reliability of the proposed computational method for the FEA of casting components with internal defects.

• Journal of Korea Foundry Society
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• v.41 no.3
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• pp.241-251
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• 2021

7000-series aluminum alloys are noted for their superior strength compared with other Al alloys, and their billets are generally fabricated by direct-chill (DC) casting. Surface defects in a DC-cast aluminum billet are mainly related to exudation and the meniscus freezing phenomenon, which are influenced by alloy compositions, casting speed, and casting temperature. 7000-series aluminum alloys have a wide freezing range during solidification, which makes it easy for casting defects to occur. In this study, we investigated surface defect evolution in casting billets of Al-8Zn-2Mg-2Cu alloy fabricated by a DC casting process. The billets showed "wavy" or "dotted" surfaces. The wavy surface was formed by meniscus freezing at a lower casting speed (200 mm/min) and temperature (655 ℃). In the wavy surface, refined dendritic cells were observed in a concave region due to the constitutional supercooling caused by meniscus freezing. Meanwhile, at a higher casting temperature (675 ℃), the dotted surface was formed by pore formation. In the dotted surfaces in the billet formed at a high casting speed (230 mm/min), an exudation layer was formed by the high metallostatic head pressure. The dotted region and the smooth region had a refined dendritic morphology and a columnar morphology at the exudation layer, respectively. This is attributed to the formation of gas pores in the dotted region.

• Journal of the Korea Convergence Society
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• v.8 no.3
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• pp.169-176
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• 2017

Casting process includes wax pattern, investment, dewaxing, curing, casting, etc., and each single process is important to achieve a good result. Since 2000, 3D printers have been developed and widely used; as more prefer UV Curavle resin method over wax method, resultant casting defects have become worse. To resolve such problem, preceding research revealed casting defects of existing wax method. In particular, defects of UV Curavle resin method showed difference in investment, dewaxing, deresinating and curing compared to the existing one. Accordingly, results were presented through casting tests; especially, a temperature rising curve only for UV Curavle resin was shown rather than one for the existing method. Lastly, this research classified those not available with direct casting and suggested mold manufacturing. This research is expected to be useful for 3D printer users or those who would conduct direct casting with UV Curavle resin.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1546-1552
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• 2016

In addition, because it uses preheating to dissolve an alloy in general, it is hard to regulate the appropriate melting temperature of the alloy and brewing time and shows the defect of the supplementation thing due to the super-heating. Once the alloy is molten and then most of the casting by attaching a sight glass or non-contact temperature sensor is suitable casting temperature the operator pressing a button to generate a centrifugal force to inject the molten alloy into a crucible in the casting ring. These results, and most of the cast temperature is too high or too low to generate a lot of casting defects do not get into a uniform cast body. In this paper, we developed a dental casting machine for high frequency using a single temperature sensor which can measure the actual temperature of the alloy than the temperature of the external non-contact measurement using a temperature sensor.

• Journal of Korea Foundry Society
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• v.41 no.6
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• pp.521-527
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• 2021

This study presents a process management method for the detection of casting defects during in high-pressure die casting based on machine learning. The model predicts the defects of the next cycle by extracting the features appearing over the previous cycles. For design of the gearbox, the proposed model detects shrinkage defects with data from three cycles in advance with 98.9% accuracy and 96.8% recall rates.