Skip to main content
SpringerLink
Log in

A study on multi-factor geometry-physical modeling and simulation in machine tool cutting processes

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Geometric-physical modeling and simulation of tool machining processes is an effective realization for manufacturing prediction and verification. By integrating the scheme of CNC code analysis, process planning and optimization, cutting mechanism model, and other related aspects, micro cutting details were implemented to be simulated in advance, detected and monitored in the process, and analyzed afterwards, to achieve the purpose of “Verification IS Production.” Pursuant to this purpose, this paper proposed a research framework of micro geometric modeling and physical simulation for machine tool cutting. On the basis of continuous improvements in 3D modules for cutting geometry simulation, the physical simulation research and verification was carried out with several typical scenes, in which the mappings between real occasions and simulation system were established. With the cutting physical models, this paper deeply investigated the simulation calculation and correction for various factors affecting the cutting performance and indicators and finally verifies, analyzes, and optimizes them through actual machining environments. The purpose of this paper is to explore a feasible and novel way for richer scenes and further research through the multi-element modeling of several comprehensive cutting cases and in-depth micro geometry and physics investigation.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

Not applicable.

Code availability

Not applicable.

References

  1. Liang F, Xie Z, Xia A, Zhou M (2020) Aeroelastic simulation of the first 1.5-stage aeroengine fan at rotating stall. Chinese J Aeronaut 33(2):529–549

    Article  Google Scholar 

  2. Karunakaran K, Shringi R (2008) A solid model-based off-line adaptive controller for feed rate scheduling for milling process. J Mater Process Tech 204(1–3):384–396

    Article  Google Scholar 

  3. Ma H, Liu W, Zhou X, Niu Q, Kong C (2020) An effective and automatic approach for parameters optimization of complex end milling process based on virtual machining. J Intell Manuf 31:967–984

    Article  Google Scholar 

  4. El-Mounayri H, Deng H (2010) A generic and innovative approach for integrated simulation and optimisation of end milling using solid modeling and neural network. Int J Comput Integ M 23(1):40–60

    Article  Google Scholar 

  5. Chaube A, Benyoucef L, Tiwari M (2010) An adapted NSGA-2 algorithm based dynamic process plan generation for a reconfigurable manufacturing system. J Intell Manuf 23(4):1141–1155

    Article  Google Scholar 

  6. Touzout F, Benyoucef L (2018) Multi-objective sustainable process plan generation in a reconfigurable manufacturing environment: exact and adapted evolutionary approaches. Int J Prod Res 57(8):2531–2547

    Article  Google Scholar 

  7. Dai X, Wei Z, Quan Q (2016) Modeling and simulation of bow wave effect in probe and drogue aerial refueling. Chinese J Aeronaut 29(2):448–461

    Article  Google Scholar 

  8. Spence A, Altintas Y (1994) A solid modeller based milling process simulation and planning system. J Manuf Sci E-T Asme 116(1):61–69

    Google Scholar 

  9. Mounayri H, Spence A, Elbestawi M (1998) Milling process simulation - a generic solid modeller based paradigm. J Manuf Sci E-T Asme 120(2):213–221

    Article  Google Scholar 

  10. Miao Y (2016) Research on NC machining simulation based on STL model and its key technologies. Dissertation, Zhejiang University, pp 45–47

  11. Yoshimi T, Masafumi S, Abe Y, Orita R, Sata T (1989) Development of a personal CAD/CAM system for mold manufacture based on solid modeling techniques. CIRP Ann 38(1):429–432

    Article  Google Scholar 

  12. Joy J, Feng H (2017) Frame-sliced voxel representation: an accurate and memory-efficient modeling method for workpiece geometry in machining simulation. Comput Aided Design 88(7):1–13

    Article  Google Scholar 

  13. Yau H, Tsou L (2009) Efficient NC simulation for multi-axis solid machining with a universal APT cutter. J Inf Sci Eng 9(2):134–143

    Google Scholar 

  14. Sullivan A, Erdim H, Perry R, Frisken S (2012) High accuracy NC milling simulation using composite adaptively sampled distance fields. Comput Aided Design 44(6):522–536

    Article  Google Scholar 

  15. Arrazola PJ, Özel T, Umbrello D (2013) Recent advances in modeling of metal machining processes. CIRP Ann 62(2):695–718

    Article  Google Scholar 

  16. Budak E, Altintas Y, Armarego E (1996) Prediction of milling force coefficients from orthogonal cutting data. J Manuf Sci E-T Asme 118(2):216–224

    Article  Google Scholar 

  17. Boz Y, Erdim H, Lazoglu I (2015) A comparison of solid model and three-orthogonal dexelfield methods for cutter-workpiece engagement calculations in three- and five-axis virtual milling. Int J Adv Manuf Tech 81(5–8):811–823

    Article  Google Scholar 

  18. Ferry W, Yip-Hoi D (2008) Cutter-workpiece engagement calculations by parallel slicing for five-axis flank milling of jet engine impellers. J Manuf Sci E-T Asme 130(5):51–63

    Article  Google Scholar 

  19. Palanisamy P, Rajendran I, Shanmugasundaram S (2006) Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations. Int J Adv Manuf Tech 32(7–8):644–655

    Google Scholar 

  20. Lu K, Jing M, Zhang X, Dong G (2013) An effective optimization algorithm for multipass turning of flexible workpieces. J Intell Manuf 26(4):831–840

    Article  Google Scholar 

  21. Alajmi M, Alfares F, Alfares M (2017) Selection of optimal conditions in the surface grinding process using the quantum based optimisation method. J Intell Manuf 30(3):1469–1481

    Article  Google Scholar 

  22. Çiçek A, Kıvak T, Ekici E (2013) Optimization of drilling parameters using Taguchi technique and response surface methodology (RSM) in drilling of AISI 304 steel with cryogenically treated HSS drills. J Intell Manuf 26(2):295–305

    Article  Google Scholar 

  23. Kurt M, Bagci E (2011) Feedrate optimisation/scheduling on sculptured surface machining: a comprehensive review, applications and future directions. Int J Adv Manuf Tech 55(9–12):1037–1067

    Article  Google Scholar 

  24. Yusup N, Zain A, Hashim S (2012) Evolutionary techniques in optimizing machining parameters: review and recent applications (2007–2011). Expert Syst Appl 39(10):9909–9927

    Article  Google Scholar 

  25. Tandon V, El-Mounayri H, Kishawy H (2002) NC end milling optimization using evolutionary computation. Int J Mach Tool Manu 42(5):595–605

    Article  Google Scholar 

  26. Bharathi RS, Baskar N (2012) Application of particle swarm optimization technique for achieving desired milled surface roughness in minimum machining time. Expert Syst Appl 39(5):5982–5989

    Article  Google Scholar 

  27. Yusup N, Sarkheyli A, Zain A (2013) Estimation of optimal machining control parameters using artificial bee colony. J Intell Manuf 25(6):1463–1472

    Article  Google Scholar 

  28. Li L, Liu F, Chen B, Li C (2013) Multi-objective optimization of cutting parameters in sculptured parts machining based on neural network. J Intell Manuf 26(5):891–898

    Article  Google Scholar 

  29. Altintas Y (2012) Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design. Cambridge University Press

    Book  Google Scholar 

Download references

Funding

This research was supported by the National Key R&D Projects No. Y2019-VII-0018.

Author information

Authors and Affiliations

  1. National Engineering Research Center of Die & Mold CAD, Shanghai Jiao Tong University, Shanghai, People’s Republic of China, 200030

    Wei Liu, Hengyuan Ma, Xionghui Zhou & Qiang Niu

Authors
  1. Wei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hengyuan Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xionghui Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiang Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Liu.

Ethics declarations

Ethics approval

There are no ethical problems as this research belongs to the field of engineering technology and does not involve any human body-related data.

Consent to participate

All authors consent to participate.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, W., Ma, H., Zhou, X. et al. A study on multi-factor geometry-physical modeling and simulation in machine tool cutting processes. Int J Adv Manuf Technol 129, 2491–2505 (2023). https://doi.org/10.1007/s00170-023-12490-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-023-12490-y

Keywords

Search

Navigation