• 제목/요약/키워드: Single production forming machine

검색결과 4건 처리시간 0.019초

고속 생산형 필름 진동판 성형기 및 금형 국산화 개발(I) - 단수 생산 진동판 성형기 - (Domestic Development of Vibrational Film Forming Machine and Die and Mold in the High Speed Production(I) - Single production forming machine -)

  • 김정현
    • 한국기계가공학회지
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    • 제11권6호
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    • pp.9-15
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    • 2012
  • Vibrational film has been more employed in ear-phones or small type of speakers along with a wide use of portable multi-media equipments such as MP3 and MP4. However, the current hand work production process of diaphragms is inefficient. In this study, a die-and-mold and a single production forming machine are developed, and they result in a multi-production forming machine. The multi-production forming machine consists primarily of a film feeding unit and an unwinding unit. A vacuum suction device provides the film feeding unit, while the unwinding unit is obtained using an appropriate damper. The advantage of the developed single production forming machine is shown according to a proper voice test.

고속 생산형 필름 진동판 성형기 및 금형 국산화 개발(II) - 다량 생산 진동판 성형기 - (Domestic Development of Vibrational Film Forming Machine and Die in the High Speed Production(II) - Multi-production forming machine -)

  • 김정현
    • 한국기계가공학회지
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    • 제13권1호
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    • pp.52-58
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    • 2014
  • This study consists of two parts. The first discusses the development of a single production forming machine which was reported in earlier papers. The second outlines the development of a multi-production forming machine, which consists primarily of a film feeding unit, an unwinding unit, and a heating block unit. The heating block unit of the multi-production forming machine has 30 members per die. An analysis of the stress deformation and temperature deviation of this machine is carried out using ANSYS Workbench and CFX-11 under the design conditions. According to this analysis, the maximum deflection in the Z-direction is $0.05104{\mu}m$ and the maximum temperature deviation is $0.7^{\circ}C$ when the temperature of the heating block unit is $175^{\circ}C$. It was also found that these values are structurally safe. The advantage of the developed multi-production forming machine is demonstrated to be in its offering of a proper voice test.

2겹 판재 멀티포밍 장치에 관한 연구 (A study of Double Sheet Multi-forming Equipment)

  • 윤재웅;손옥종
    • 한국산학기술학회논문지
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    • 제18권3호
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    • pp.49-55
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    • 2017
  • 대부분의 모터 케이스는 방수기능과동심도, 직각도 품질 등이 우수한딥드로잉 제품을 채택하고 있으며 그외 방수기능이 필요없는 블로우모터, 시트모터 등 실내에 장착되는 모터 케이스는 멀티포밍 제조방법을 채택하고 있다. 딥드로잉 공정은 드로잉 성형, 트리밍 및 피어싱 등 약 12공정을 소화할수 있는 고가의 트랜스퍼 프레스가 필요하다. 하지만 멀티포밍 공법은 멀티포밍기 1대 또는 멀티포밍기 1대와 프레스 1대의 구성으로 이루어지기 때문에 저렴한 투자로도 완제품 생산이 가능하다. 멀티포밍기는 대부분 수입이 되는 고가설비 이고 국내 중소기업이 개발한 굽힘/전단가공 일체형 멀티포밍기는 생산원가를 줄이는데 부족하여 굽힘/전단 분리형 멀티포밍기를 본 연구를 통해 개발하였다. 일체형 멀티포밍기는 비교적 얇고 작은소형 제품을 제한적인 작업 방법으로 사용된다. 크고 소재가 두꺼운 제품은 전단 하중이 높아 단발 크랭크 프레스를 이용하고 블랭킹 후 멀티포밍기로 이동 작업자가 수작업으로 소재를 공급한다. 멀티포밍기에서 벤딩 작업이 이루어지면 다시 프레스로 이송해 치수를 교정한다. 이러한 작업공정 분산은 생산성 저하, 품질이슈 및 과도한 인원 투입으로 원가경쟁력 저하를 가져왔다 이에 굽힘/전단가공 공정분리와 설비자동화를 통하여 안정되고 원가가 절감되는 생산체계를 갖추고자 하는게 본 연구의 목적이다.

진동신호 기계학습을 통한 프레스 금형 상태 인지 (State recognition of fine blanking stamping dies through vibration signal machine learning)

  • 홍석관;정의철;이성희;김옥래;김종덕
    • Design & Manufacturing
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    • 제16권4호
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    • pp.1-6
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    • 2022
  • Fine blanking is a press processing technology that can process most of the product thickness into a smooth surface with a single stroke. In this fine blanking process, shear is an essential step. The punches and dies used in the shear are subjected to impacts of tens to hundreds of gravitational accelerations, depending on the type and thickness of the material. Therefore, among the components of the fine blanking mold (dies), punches and dies are the parts with the shortest lifespan. In the actual production site, various types of tool damage occur such as wear of the tool as well as sudden punch breakage. In this study, machine learning algorithms were used to predict these problems in advance. The dataset used in this paper consisted of the signal of the vibration sensor installed in the tool and the measured burr size (tool wear). Various features were extracted so that artificial intelligence can learn effectively from signals. It was trained with 5 features with excellent distinguishing performance, and the SVM algorithm performance was the best among 33 learning models. As a result of the research, the vibration signal at the time of imminent tool replacement was matched with an accuracy of more than 85%. It is expected that the results of this research will solve problems such as tool damage due to accidental punch breakage at the production site, and increase in maintenance costs due to prediction errors in punch exchange cycles due to wear.