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알루미늄 합금(Al7075-T651)의 얇은 벽 밀링가공시 가공특성 및 상태감시에 관한 연구 / 구준영 인기도
발행사항
부산 : 부산대학교 대학원, 2016.2
청구기호
TD 621.8 -16-246
형태사항
ix, 146 p. ; 26 cm
자료실
전자자료
제어번호
KDMT1201603299
주기사항
학위논문(박사) -- 부산대학교 대학원, 기계공학부, 2016.2. 지도교수: 김정석
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목차

Nomenclature 5

제1장 서론 12

1.1. 연구배경 12

1.2. 연구동향 15

1.3. 연구목적 및 내용 20

제2장 알루미늄 합금의 얇은 벽 밀링가공시 가공특성분석 22

2.1. 알루미늄 합금의 얇은 벽 밀링가공시 가공변형특성 24

2.1.1. 실험장치 및 방법 24

2.1.2. 실험결과 및 고찰 27

2.2. 알루미늄 합금의 얇은 벽 밀링가공시 진동특성 39

2.2.1. 실험장치 구성 39

2.2.2. 실험방법 40

2.2.3. 실험결과 및 고찰 43

제3장 알루미늄 합금의 밀링가공에서 채터진동특성 59

3.1. 실험장치 및 방법 62

3.2. 실험결과 및 고찰 65

3.2.1. 가속도와 음향신호특성 65

3.2.2. 가공표면특성 98

3.3. 채터안정선도 103

3.3.1. 채터안정도 모델 - Zero-order chatter stability model 103

3.3.2. 주파수응답분석 및 절삭력계수 108

3.3.3. 채터안정선도 비교분석 113

제4장 알루미늄 합금의 얇은 벽 밀링가공시 채터진동감지를 위한 실시간 채터진동 감시시스템 116

4.1. 절삭신호의 패턴화 118

4.2. 인공신경망을 활용한 채터진동감지 123

4.2.1. 역전파 인공신경망 123

4.2.2. 채터진동 패턴인식 알고리즘 128

4.3. 퍼지추론기법을 활용한 채터진동감지 133

4.4. 실시간 채터진동 감시시스템 139

제5장 결론 143

References 146

Abstract 155

Table 2.1. Material properties of Al7075-T651 25

Table 2.2. Chemical composition of Al7075-T651 25

Table 2.3. Experimental conditions (machining deformation experiments) 26

Table 2.4. Experimental conditions (fundamental experiments) 41

Table 2.5. Experimental conditions (thin-wall machining experiments) 42

Table 3.1. Experimental conditions (chatter vibration experiments) 63

Table 3.2. Chatter limit of axial depth of cut (cutting signal) 75

Table 3.3. Frequency ranges 88

Table 3.4. Chatter limit of axial depth of cut (surface condition) 100

Table 3.5. Experimental conditions (cutting force experiment) 109

Table 3.6. Modal parameters of the machining system 110

Table 3.7. Cutting force coefficient 112

Table 4.1. Fuzzy rules 138

Fig. 1.1. Industrial application of aluminum alloy 13

Fig. 2.1. Experimental setup (machining deformation experiments) 25

Fig. 2.2. Changes in acceleration RMS 28

Fig. 2.3. Changes in AE RMS 29

Fig. 2.4. Machined surface condition 31

Fig. 2.5. Machined surface and cutting signals according to machining area 32

Fig. 2.6. Waterfall graph according to machining area 33

Fig. 2.7. 3D surface graph 34

Fig. 2.8. Deformation of horizontal direction 36

Fig. 2.9. Deformation of vertical direction 37

Fig. 2.10. Experimental setup (vibration characteristic experiments) 39

Fig. 2.11. Experimental method (fundamental experiments) 40

Fig. 2.12. Experimental method (thin-wall machining experiments) 42

Fig. 2.13. Changes in acceleration RMS according to axial depth of cut 44

Fig. 2.14. Changes in acceleration RMS according to spindle speed 44

Fig. 2.15. Changes in SPL according to axial depth of cut 45

Fig. 2.16. Changes in SPL according to spindle speed 45

Fig. 2.17. FFT graph according to axial depth of cut 47

Fig. 2.18. FFT graph according to spindle speed 48

Fig. 2.19. Machined surface according to axial depth of cut 50

Fig. 2.20. Machined surface according to spindle speed 50

Fig. 2.21. Machining stability according to ADOC and spindle speed 51

Fig. 2.22. Changes in acceleration RMS according to machining area 52

Fig. 2.23. Changes in SPL according to machining area 53

Fig. 2.24. FFT graph according to machining area and experiment 55

Fig. 2.25. Machined surface conditions... 57

Fig. 3.1. Change in chip thickness by phase shift of tooth period 60

Fig. 3.2. Experimental setup (chatter vibration experiments) 62

Fig. 3.3. Experimental method (chatter vibration experiments) 63

Fig. 3.4. Scheme of cutting signal processing 64

Fig. 3.5. Changes in cutting signal according to spindle speed... 68

Fig. 3.6. Changes in cutting signal according to spindle speed... 71

Fig. 3.7. Changes in acceleration RMS and SPL 74

Fig. 3.8. Classification of stable region and unstable region 75

Fig. 3.9. Curve of chatter limit of axial depth of cut (cutting signal) 76

Fig. 3.10. Scheme of discrete wavelet transform 77

Fig. 3.11. Changes in peak-count 79

Fig. 3.12. Peak-count at limit axial depth of cut according to spindle speed 80

Fig. 3.13. Changes in FFT graph of acceleration signal 84

Fig. 3.14. Changes in FFT graph of acoustic signal 87

Fig. 3.15. Changes in band energy - band 1 92

Fig. 3.16. Changes in band energy - band 2 96

Fig. 3.17. Band energy at chatter limit axial depth of cut... 97

Fig. 3.18. Surface conditions according to axial depth of cut 100

Fig. 3.19. Curve of chatter limit of axial depth of cut (surface condition) 101

Fig. 3.20. Comprehensive analysis of machining stability 102

Fig. 3.21. Experimental setup (FRF analysis and cutting force experiments) 108

Fig. 3.22. FRF analysis - x direction(XX) 110

Fig. 3.23. FRF analysis - y direction(YY) 110

Fig. 3.24. Changes in cutting forces according to feed per tooth 111

Fig. 3.25. Chatter stability lobe 113

Fig. 3.26. Comparison analysis of chatter stability 114

Fig. 4.1. Scheme of wavelet packet transform - level 3 118

Fig. 4.2. Vector normalization 120

Fig. 4.3. Input and output pattern data 121

Fig. 4.4. Neuron 123

Fig. 4.5. Scheme of multi layer neural networks 124

Fig. 4.6. Scheme of back-propagation neural networks 125

Fig. 4.7. Scheme of back-propagation neural networks with 3-hidden layer 128

Fig. 4.8. Procedure of back-propagation neural networks learning 129

Fig. 4.9. Learning curve of BP-ANN 130

Fig. 4.10. Evaluation of BP-ANN for machining stability diagnosis 132

Fig. 4.11. Acceleration RMS and SPL at limit axial depth of cut 133

Fig. 4.12. Scheme of Mamdani-style fuzzy inference 135

Fig. 4.13. Fuzzy set 137

Fig. 4.14. Algorithm for real-time machining condition monitoring 139

Fig. 4.15. Program for real-time machining condition monitoring 140

Fig. 4.16. Verification of real-time machining condition monitoring program 141

Fig. 4.17. Scheme of real-time machining condition monitoring system 142

초록보기 더보기

 Recently, a lot of researches for lightweight materials and parts have been conducted to improve fuel efficiency in industry of transportation equipment such as aerospace and automotive. Especially, in order to reduce carbon-oxide gases and to improve fuel efficiency light-weighted metal parts have been applied to transportation equipment.

Aluminum alloys (Al-alloys) having high specific strength are effective for structural parts. So, Al-alloys have been used for structural frame parts related to safety of aircraft and automobiles. Also Al-alloys are applied to portable device demanding strength such as laptop computers and mobile phones for weight reduction. Especially Al-alloys are applied to parts having a thin-wall structure. A thin-wall structure is easy to be deformed by cutting forces, vibrations, and heat.

Chatter vibration generated by inappropriate cutting conditions makes surface integrity worse in thin-wall milling process of Al-alloys. Thus it is important to conduct continuous monitoring for diagnosis and avoidance of chatter vibration to ensure a machining quality in thin-wall milling process of Al-alloys.

The objective of this study is to analyze machining characteristics and to make a machining condition monitoring system to detect chatter vibration in thin-wall milling process of Al-alloy(Al7075-T651). For this purpose, thin-wall milling experiments are conducted to analyze the characteristics of machining process. Machining deformation and vibration characteristics of thin-wall milling process are identified by machined surface analysis and time-domain and frequency-domain analysis of cutting signals acquired by accelerometer, microphone, and AE sensor.

In addition, milling experiments are conducted to figure out detailed characteristics of chatter vibration which cause deterioration of surface integrity in thin-wall milling process of Al-alloy. Changes in cutting signals and surface conditions according to spindle speed and axial depth of cut are analyzed. Signal processing is conducted for extracting cutting signal characteristics when chatter vibration occurs. The limits of depth of cut that chatter vibration generates figures out by analysis of acceleration RMS, SPL, peak-count by DWT, and surface conditions. Also features of FFT graphs and frequency bands having high peak and high density when chatter vibration occurs are found out by FFT analysis.

Finally the monitoring algorithm applying artificial neural network(ANN) learned by the cutting signal patterns and fuzzy inference method using acceleration RMS and SPL is created to conduct the monitoring for diagnosis of chatter vibration in real-time. Patterning of cutting signals is conducted by using wavelet packet transform and vector normalizing to use for input pattern of pattern recognition algorithm by ANN. This suggested monitoring scheme can be applied to machining process effectively for detection of abnormal machining conditions such as chatter vibration.

참고문헌 (47건) : 자료제공( 네이버학술정보 )더보기

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 “알루미늄 응용기술의 이해와 활용,” 한국철강신문, 2005. 미소장
2 “Evaluation of Fracture Toughness and Mechanical Properties of Aluminu Alloy 7075, T6 with Nickel Coating,” Procedia Engineering,... 미소장
3 “Failure of AA6061 and 2099 aluminum alloys under dynamic shock loading,” Engineering Failure Analysis, Vol. 35, pp. 302-314, 2013. 미소장
4 Analysis on high-speed face-milling of 7075-T6 aluminum using carbide and diamond cutters 네이버 미소장
5 Investigation of milling cutting forces and cutting coefficient for aluminum 6060-T6 네이버 미소장
6 Artificial Neural Networks for Surface Roughness Prediction when Face Milling Al 7075-T7351 네이버 미소장
7 “Study on Residual Stresses in Milling Aluminium Alloy 7050-T7451,”Advanced Design and Manufacture to Gain a Competitive Edge: New... 미소장
8 Influence of milling strategy on the surface roughness in ball end milling of the aluminum alloy Al7075-T6 네이버 미소장
9 Optimization of end mill tool geometry parameters for Al7075-T6 machining operations based on vibration amplitude by response surface methodology 네이버 미소장
10 “Evaluation of cutter orientations in high-speed ball end milling of cantilever-shaped thin plate,” Journal of Materials Processing... 미소장
11 Surface roughness variation of thin wall milling, related to modal interactions 네이버 미소장
12 Three-dimensional finite element modeling of rough to finish down-cut milling of an aluminum alloy 네이버 미소장
13 Surface Roughness Evaluation in Dry-Cutting of Magnesium Alloy by Air Pressure Coolant 네이버 미소장
14 “ The Effects of Cutting Speed and Feed Rate on Bue-Bul Formation, Cutting Forces and Surface Roughness When Machining... 미소장
15 Machining induced residual stress in structural aluminum parts 네이버 미소장
16 4,4-Diphenyl-7-perhydrothiapyrano[3,4-c]pyrrolone, a new series of substance P receptors antagonists 네이버 미소장
17 Measurement failure robustness in H 2/H ∞ reliable controller synthesis 네이버 미소장
18 Measurement failure robustness in H 2/H ∞ reliable controller synthesis 네이버 미소장
19 Efficient Simulation Programs for Chatter in Milling 네이버 미소장
20 RCPM—A new method for robust chatter prediction in milling 네이버 미소장
21 “Analysis of Linear and Nonlinear Chatter in Milling,” Annals of CIRP, Vol. 39, pp. 459-462, 1990. 미소장
22 “Chatter stability of milling in frequency and discrete time domain,” CIRP Journal of Manufacturing Science and Technology, Vol. 1,... 미소장
23 Tracing and Visualizing Variation of Chatter for In-Process Identification of Preferred Spindle Speeds 네이버 미소장
24 Detection of chatter vibration in end milling applying disturbance observer 네이버 미소장
25 Development of an Intelligent High-Speed Machining Center 네이버 미소장
26 “Development of an intelligent multisensor chatter detection system in milling,” Mechanical Systems and Signal Processing, Vol. 23,... 미소장
27 A Hybrid Approach of ANN and HMM for Cutting Chatter Monitoring 네이버 미소장
28 Matweb n.d. . 미소장
29 “Advanced Machining Processes of Metallic Materials: Theory, Modelling and Applications,” Elsevier, pp. 115-125, 2008. 미소장
30 Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design 네이버 미소장
31 p,p′-DDE bioaccumulation in female sea lions of the California Channel Islands 네이버 미소장
32 A network flow model of group technology 네이버 미소장
33 “Prediction of regenerative chatter by modelling and analysis of high-speed milling,” International Journal of Machine Tools and... 미소장
34 Robust prediction of chatter stability in milling based on the analytical chatter stability 네이버 미소장
35 “Wavelets-Theory and Applications for Manufacturing,” Springer, pp. 17-48, 2011. 미소장
36 “Incipient Fault Detection in Bearings Through the use of WPT Energy and Neural Networks,” Advances in Condition Monitoring of... 미소장
37 “Machining Dynamics-Frequency Response to Improved productivity,” Springer, pp. 99-171, 2009. 미소장
38 “Modal Testing: Theory, Practice, and Application, 2nd edition,” Taylor and Francis, 2000. 미소장
39 Identification of transfer function by inverse analysis of self-excited chatter vibration in milling operations 네이버 미소장
40 Analysis and compensation of mass loading effect of accelerometers on tool point FRF measurements for chatter stability predictions 네이버 미소장
41 Tool condition monitoring (TCM) using neural networks 네이버 미소장
42 A review of machining monitoring systems based on artificial intelligence process models 네이버 미소장
43 Chatter detection in milling machines by neural network classification and feature selection 네이버 미소장
44 Prediction of cutting tool wear, surface roughness and vibration of work piece in boring of AISI 316 steel with artificial neural network 네이버 미소장
45 A review of flank wear prediction methods for tool condition monitoring in a turning process 네이버 미소장
46 Real Time Chatter Vibration Control System in High Speed Milling 네이버 미소장
47 “Artificial Intelligence, 2nd edition,” Addison-Wesley, 2005. 미소장
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