Surface topography is an important factor in evaluating the surface integrity and service performance of milling parts. The dynamic characteristics of the manufacturing system and machining process parameters significantly influence the machining precision and surface quality of the parts, and the vibration control method is applied in high-precision milling to improve the machine quality. In this study, a novel surface topography model based on the dynamic characteristics of the process system, properties of the cutting process, and active vibration control system is theoretically developed and experimentally verified. The dynamic characteristics of the process system consist of the vibration of the machine tool and piezoelectric ceramic clamping system. The dynamic path trajectory influenced by the processing parameters and workpiece-tool parameters belongs to the property of the cutting process, while different algorithms of active vibration control are considered as controller factors. The milling surface topography can be predicted by considering all these factors. A series of experiments were conducted to verify the effectiveness and accuracy of the prediction model, and the results showed a good correlation between the theoretical analysis and the actual milled surfaces.
The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00386-7
Miao-Xian Guo
,
Jin Liu
,
Li-Mei Pan
,
Chong-Jun Wu
,
Xiao-Hui Jiang
,
Wei-Cheng Guo
. An integrated machine-process-controller model to predict milling surface topography considering vibration suppression[J]. Advances in Manufacturing, 2022
, 10(3)
: 443
-458
.
DOI: 10.1007/s40436-021-00386-7
1. Zhu L, Liu C (2020) Recent progress of chatter prediction, detection and suppression in milling. Mech Syst Signal Process 143:106840. https://doi.org/10.1016/j.ymssp.2020.106840
2. Liu H, Song W, Niu Y et al (2021) A generalized cauchy method for remaining useful life prediction of wind turbine gearboxes. Mech Syst Signal Process 153:107471. https://doi.org/10.1016/j.ress.2020.107241
3. Li W, Wang A, Gao X et al (2021) Development of multi-band tuned rail damper for rail vibration control. Appl Acoust 184:108370. https://doi.org/10.1016/j.apacoust.2021.108370
4. Li S, Sui J, Ding F et al (2021) Optimization of milling aluminum alloy 6061-T6 using modified Johnson-Cook model. Simul Model Pract Theory 111:102330. https://doi.org/10.1016/j.simpat.2021. 102330
5. Guo W, Wu C, Ding Z et al (2021) Prediction of surface roughness based on a hybrid feature selection method and long short-term memory network in grinding. Int J Adv Manuf Technol 112:2853-2871
6. Pham T, Nguyen D, Banh T et al (2020) Experimental study on the chip morphology, tool-chip contact length, workpiece vibration, and surface roughness during high-speed face milling of A6061 aluminum alloy. Proc Inst Mech Eng Part B J Eng Manuf 234:610-620
7. Matsumura M, Nozaki K, Yanaka W et al (2020) Optimization of milling condition of composite resin blocks for CAD/CAM to improve surface roughness and flexural strength. Dent Mater J 39:1057-1063
8. Wang T, Wu X, Zhang G et al (2020) Theoretical study on the effects of the axial and radial runout and tool corner radius on surface roughness in slot micromilling process. Int J Adv Manuf Technol 108:1931-1944
9. Liu C, He Y, Wang Y et al (2019) An investigation of surface topography and workpiece temperature in whirling milling machining. Int J Mech Sci 164:105182. https://doi.org/10.1016/j.ijmecsci.2019.105182
10. Lu X, Zhang H, Jia Z et al (2018) Floor surface roughness model considering tool vibration in the process of micro-milling. Int J Adv Manuf Technol 94:4415-4425
11. Mattia T, Paolo A, Michele M (2020) Surface morphology prediction model for milling operations. Int J Adv Manuf Technol 106:3189-3201
12. Wang Z, Wang B, Yuan J (2019) Modeling of surface topography based on cutting vibration in ball-end milling of thin-walled parts. Int J Adv Manuf Technol 101:1837-1854
13. Jing X, Lv R, Song B et al (2021) A novel run-out model based on spatial tool position for micro-milling force prediction. J Manuf Process 68:739-749
14. Costes J, Moreau V (2011) Surface roughness prediction in milling based on tool displacements. J Manuf Process 13:133-140
15. Pereira R, Marcos G (2021) Robust passive control methodology and aeroelastic behavior of composite panels with multimodal shunted piezoceramics in parallel. Compos Struct 262:113348. https://doi.org/10.1016/j.compstruct.2020.113348
16. Hu J, Habib G (2020) Friction-induced vibration suppression via the tuned mass damper:optimal tuning strategy. Lubricants 8:1-16
17. Wang F, Lee C, Zheng R (2020) Benefits of the inerter in vibration suppression of a milling machine. J Franklin Inst 356:7689-7703
18. Bolat FC, Sivrioglu S (2018) Active vibration suppression of elastic blade structure:using a novel magnetorheological layer patch. J Intell Mater Syst Struct 29:3792-3803
19. Sivrioglu S, Bolat FC (2020) Switching linear quadratic Gaussian control of a flexible blade structure containing magnetorheological fluid. Trans Inst Meas Control 42:618-627
20. Sivrioglu S, Bolat FC, Erturk E (2019) Active vibration control of a blade element with uncertainty modeling in PZT actuator force. Journal Vib Control 25:2721-2732
21. Paul S, Morales-Menendez R (2018) Active control of chatter in milling process using intelligent PD/PID control. IEEE Access 6:72698-72713
22. Zhang X, Wang C, Liu J et al (2019) Robust active control based milling chatter suppression with perturbation model via piezoelectric stack actuators. Mech Syst Signal Process 120:808-835
23. Hadi M, Darus I, Tokhi M (2020) Active vibration control of a horizontal flexible plate structure using intelligent proportionalintegral-derivative controller tuned by fuzzy logic and artificial bee colony algorithm. J Low Freq Noise Vib Active Control. https://doi.org/10.1177/1461348419852454
24. Zhang J, Liu C (2019) Chatter stability prediction of ball-end milling considering multi-mode regenerations. Int J Adv Manuf Technol 100:131-142
25. Li C, Li X, Huang S et al (2020) Ultra-precision grinding of Gd3Ga5O12 crystals with graphene oxide coolant:material deformation mechanism and performance evaluation. J Manuf Process 61:417-427
26. Zhu D, Feng X, Xu X et al (2020) Robotic grinding of complex components:a step towards efficient and intelligent machiningchallenges, solutions, and applications. Robot Comput Integr Manuf 65:101908. https://doi.org/10.1016/j.rcim.2019.101908
27. Zhuang K, Fu C, Weng J et al (2021) Cutting edge microgeometries in metal cutting:a review. Int J Adv Manuf Technol 116:2045-2092
28. Cabrera C, Araujo A, Castello D (2017) On the wavelet analysis of cutting forces for chatter identification in milling. Adv Manuf 5:130-142
29. Vasques CMA, Rodrigues JD (2007) Active vibration control of a smart beam through piezoelectric actuation and laser vibrometer sensing:simulation, design and experimental implementation. Smart Mater Struct 16:305-316
30. Jiang H, Long X, Meng G (2008) Study of the correlation between surface generation and cutting vibrations in peripheral milling. J Mater Process Technol 208:229-238
31. Chen C, Albert J (2008) Design and tuning of a fuzzy logic controller for micro-hole electrical discharge machining. J Manuf Process 10:61-73
32. Xiong J, Zhu B, Chen H et al (2020) Peak elimination of cross structures in wire and arc additive manufacturing using closedloop control. J Manuf Process 58:368-376
33. Qu W, Sun J, Qiu Y (2004) Active control of vibration using a fuzzy control method. J Sound Vib 275:917-930
34. Zhang B, Dong W, Li X et al (2020) Design of active-passive composite vibration isolation system of magnetic levitation and spring based on fuzzy PID control. In:2020 Chinese Automation Congress (CAC), 6-8 Nov 2020, Shanghai. https://doi.org/10.1109/CAC51589.2020.9326769
35. Chen G, Li Y, Liu X (2018) Pose-dependent tool tip dynamics prediction using transfer learning. Int J Mach Tools Manuf 137:30-41
36. Yu Y, Lin C, Hu Y (2021) Study on simulation and experiment of non-circular gear surface topography in ball end milling. Int J Adv Manuf Technol 114:1913-1923
37. Kang WT, Derani MN, Ratnam MM (2021) Effect of vibration on surface roughness in finish turning:simulation study. Int J Simul Model 19:595-606
