Skip to main content
Log in

An integrated machine-process-controller model to predict milling surface topography considering vibration suppression

  • Published:
Advances in Manufacturing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  11. Mattia T, Paolo A, Michele M (2020) Surface morphology prediction model for milling operations. Int J Adv Manuf Technol 106:3189–3201

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  14. Costes J, Moreau V (2011) Surface roughness prediction in milling based on tool displacements. J Manuf Process 13:133–140

    Article  Google Scholar 

  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

    Article  Google Scholar 

  16. Hu J, Habib G (2020) Friction-induced vibration suppression via the tuned mass damper: optimal tuning strategy. Lubricants 8:1–16

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  MathSciNet  Google Scholar 

  21. Paul S, Morales-Menendez R (2018) Active control of chatter in milling process using intelligent PD/PID control. IEEE Access 6:72698–72713

    Article  Google Scholar 

  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

    Article  Google Scholar 

  23. Hadi M, Darus I, Tokhi M (2020) Active vibration control of a horizontal flexible plate structure using intelligent proportional-integral-derivative controller tuned by fuzzy logic and artificial bee colony algorithm. J Low Freq Noise Vib Active Control. https://doi.org/10.1177/1461348419852454

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  26. Zhu D, Feng X, Xu X et al (2020) Robotic grinding of complex components: a step towards efficient and intelligent machining-challenges, solutions, and applications. Robot Comput Integr Manuf 65:101908. https://doi.org/10.1016/j.rcim.2019.101908

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  32. Xiong J, Zhu B, Chen H et al (2020) Peak elimination of cross structures in wire and arc additive manufacturing using closed-loop control. J Manuf Process 58:368–376

    Article  Google Scholar 

  33. Qu W, Sun J, Qiu Y (2004) Active control of vibration using a fuzzy control method. J Sound Vib 275:917–930

    Article  MathSciNet  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

Download references

Acknowledgments

This project was supported by the National Natural Science Foundation of China (Grant No. 51905347).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei-Cheng Guo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, MX., Liu, J., Pan, LM. et al. An integrated machine-process-controller model to predict milling surface topography considering vibration suppression. Adv. Manuf. 10, 443–458 (2022). https://doi.org/10.1007/s40436-021-00386-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40436-021-00386-7

Keywords

Navigation