금형 가공 시 최적 가공조건을 결정하기 위한 공구수명 예측 프로그램 개발
Development of tool-life prediction program to determine the optimal machining conditions in mold machining
0
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
Recently, with the emergence of the 4th industrial revolution, the demand for smart factories and factory automation is increasing. In this study, a tool life prediction program was developed to select optimal machining conditions using CNC milling equipment, which is widely used in flexible production and automation. The equipment used in the experiment was Hwacheon Machine Tool's 5-axis machining equipment, and the tool used was a 17F2R tool. For the machining path, the down-milling cutting method was selected and long-term machining was performed. The analysis standard for side wear on the tool was set at 0.1 to 0.2 mm, and tool life data and wear data were obtained in the cutting experiment. The program was created through the data obtained from the experiment, and a prediction rate of over 90% was secured when comparing the experimental data and the predicted data.
Abstract
Recently, with the emergence of the 4th industrial revolution, the demand for smart factories and factory automation is increasing. In this study, a tool life prediction program was developed to select optimal machining conditions using CNC milling equipment, which is widely used in flexible production and automation. The equipment used in the experiment was Hwacheon Machine Tool's 5-axis machining equipment, and the tool used was a 17F2R tool. For the machining path, the down-milling cutting method was selected and long-term machining was performed. The analysis standard for side wear on the tool was set at 0.1 to 0.2 mm, and tool life data and wear data were obtained in the cutting experiment. The program was created through the data obtained from the experiment, and a prediction rate of over 90% was secured when comparing the experimental data and the predicted data.
참고문헌 (9)
- 정성택, "밀링 공구의 역 공학 설계에서 3D 유한요소 해석을 통한 절삭력 실험의 신뢰성 검증", Design & Manufacturing, 13(2), pp.54~59 (2019), 한국금형공학회[DOI]
- Baek, S. Y., "A Study on Proposal of Flank Wear Criterion by Using a Built-in Current Sensor when Manufacturing the Mold Materials in a Smart Machine Tool", MATEC Web of Conferences, Vol. 207, p. 03007, EDP Sciences, 207, pp.03007~ (2018), EDP Sciences
- 정성택, "금형강의 하향절삭 시 절삭력 예측 및 실험에 대한 연구", 한국생산제조학회지, 27(4), pp.346~350 (2018), 한국생산제조학회[DOI]
- Altintas, Y, "Manufacturing automation", pp.54~62 (2000), Cambridge University Pres
- Lee, S. T., "Prediction and Experiments of cutting forces in end milling", Transactions of the korean society of machine tool Engineers, 31(4), pp.9~15 (2004)
- Khorasani, A. M., "Tool life prediction in face milling machining of 7075 Al by using artificial neural networks (ANN) and Taguchi design of experiment (DOE)", International Journal of Engineering and Technology, 3(1), pp.30~ (2011)
- Bagri, S., "Tool wear and remaining useful life prediction in micro-milling along complex tool paths using neural networks", Journal of Manufacturing Processes, 71, pp.679~698 (2021)
- He, Z., "Research on tool wear prediction based on temperature signals and deep learning", Wear, 478 (2021)
- Lee, J. C., "(for the 21st century) Manufacturing Process", pp.411~418 (2002), Munundang