Skip to main content
SpringerLink
Log in

Feedrate optimization method based on machining allowance optimization and constant power constraint

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

Abstract

Aiming at the problem of low machining efficiency caused by the constant feedrate set by craftsmen in CNC machining, a feedrate optimization method by effectively combining the allowance optimization and the constant power constraint is developed in this paper. Firstly, the paper presents a machining allowance optimization method and corresponding hierarchical solution strategy to calculate machining allowance distribution. Then, a milling power prediction model is established on the basis of the milling force model and its accuracy is verified by the power model verification experiments. Furthermore, a feedrate optimization method with the spindle constant power constraint and a principle of secondary fine optimization are proposed. Finally, the allowance optimization of the complex part is carried out to obtain the positioning parameters and the optimal machining allowance distribution of the blank. Based on the machining allowance optimization results, the effectiveness of the feedrate optimization method is verified by the complex part machining optimization experiments. The experimental results show that the real-time power is maintained within the range of [−5.88%, 7.31%] of given target power value, reaching constant power constraint. It significantly shortens the processing time, improves the efficiency by 26.88%, and reduces the cost under the premise of protecting the machine tool and cutter. In this paper, the integrated optimization process from the determination of the blank quality to the processing parameter optimization is systematically completed.

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
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data availability

Not applicable.

Code availability

Not applicable.

References

  1. Zhou W, Zhu X, Wang J, Ran Y (2018) A new error prediction method for machining process based on a combined model. Math Probl Eng:1–8. https://doi.org/10.1155/2018/3703861

  2. Rai JK, Xirouchakis P (2008) Finite element method based machining simulation environment for analyzing part errors induced during milling of thin-walled components. Int J Mach Tool Manu 48:629–643. https://doi.org/10.1016/j.ijmachtools.2007.11.004

    Article  Google Scholar 

  3. Shen B, Huang GQ, Mak KL, Wang XC (2003) A best-fitting algorithm for optimal location of large-scale blanks with free-form surfaces. J Mater Process Technol 139(1-3):310–314. https://doi.org/10.1016/s0924-0136(03)00241-3

    Article  Google Scholar 

  4. Zhang Y, Zhang DH, Wu BH (2015) An approach for machining allowance optimization of complex parts with integrated structure. J Comput Des Eng 2(4):248–252. https://doi.org/10.1016/j.jcde.2015.06.007

    Article  Google Scholar 

  5. Wu X, Dai W (2016) Research on machining allowance distribution optimization based on processing defect Risk. Procedia CIRP 56:508–511. https://doi.org/10.1016/j.procir.2016.10.099

    Article  Google Scholar 

  6. Bae SH, Ko K, Bo HK, Byoung KC (2003) Automatic feedrate adjustment for pocket machining. Comput Aided Des 35(5):495–500. https://doi.org/10.1016/S0010-4485(01)00195-6

    Article  Google Scholar 

  7. Zhang C (2009) Off-Line feedrate optimization based on simulation of cutting forces. Key Eng Mater 407-408:408–411. https://doi.org/10.4028/www.scientific.net/KEM.407-408.408

    Article  Google Scholar 

  8. Park HS, Qi BW, Dang DV, Park DY (2018) Development of smart machining system for optimizing feedrates to minimize machining time. J Comput Des Eng 5(3):299–304. https://doi.org/10.1016/j.jcde.2017.12.004

    Article  Google Scholar 

  9. Beudaert X, Lavernhe S, Tournier C (2012) Feedrate interpolation with axis jerk constraints on 5-axis NURBS and G1 tool path. Int J Mach Tool Manu 57:73–82. https://doi.org/10.1016/j.ijmachtools.2012.02.005

    Article  Google Scholar 

  10. Erkorkmaz K, Layegh SE, Lazoglu I, Erdim H (2013) Feedrate optimization for freeform milling considering constraints from the feed drive system and process mechanics. CIRP Ann Manuf Technol 62(1):395–398. https://doi.org/10.1016/j.cirp.2013.03.084

    Article  Google Scholar 

  11. Beudaert X, Pechard PY, Tournier C (2011) 5-Axis tool path smoothing based on drive constraints. Int J Mach Tool Manu 51(12):958–965. https://doi.org/10.1016/j.ijmachtools.2011.08.014

    Article  Google Scholar 

  12. Sun Y, Zhao Y, Xu JT, Guo DM (2014) The feedrate scheduling of parametric interpolator with geometry, process and drive constraints for multi-axis CNC machine tools. Int J Mach Tool Manu 85:49–57. https://doi.org/10.1016/j.ijmachtools.2014.05.001

    Article  Google Scholar 

  13. Bharathi A, Dong J (2016) Feedrate optimization for smooth minimum-time trajectory generation with higher order constraints. Int J Adv Manuf Technol 82(5-8):1029–1040. https://doi.org/10.1007/s00170-015-7447-x

    Article  Google Scholar 

  14. Jixiang Y, Deniz A, Yusuf A (2018) A feedrate scheduling algorithm to constrain tool tip position and tool orientation errors of five-axis CNC machining under cutting load disturbances. CIRP J Manuf Sci Tec 23:78–90. https://doi.org/10.1016/j.cirpj.2018.08.005

    Article  Google Scholar 

  15. Baek DK, Ko TJ, Kim HS (2001) Optimization of feedrate in a face milling operation using a surface roughness model. Int J Mach Tool Manu 41(3):451–462. https://doi.org/10.1016/S0890-6955(00)00039-0

    Article  Google Scholar 

  16. Baek DK, Ko TJ, Yang SH (2012) Fast and precision NURBS interpolator for CNC systems. Int J Precis Eng Manuf 13(6):955–961. https://doi.org/10.1007/s12541-012-0124-1

    Article  Google Scholar 

  17. Zhang ZX, Luo M, Zhang DH, Wu BH (2018) A force-measuring-based approach for feed rate optimization considering the stochasticity of machining allowance. Int J Adv Manuf Technol 97(5-8):2545–2556. https://doi.org/10.1007/s00170-018-2127-2

    Article  Google Scholar 

  18. Xiong Z, Wang MY, Li Z (2004) A near-optimal probing strategy for workpiece localization. IEEE Trans Robot 20(4):668–676. https://doi.org/10.1109/TRO.2004.829474

    Article  Google Scholar 

  19. Sun YW, Xu JT, Guo DM, Jia ZY (2009) A unified localization approach for machining allowance optimization of complex curved surfaces. Precis Eng 33(4):516–523. https://doi.org/10.1016/j.precisioneng.2009.02.003

    Article  Google Scholar 

  20. Sun YW, Wang XM, Guo DM, Liu J (2009) Machining localization and quality evaluation of parts with sculptured surfaces using SQP method. Int J Adv Manuf Technol 42(11):1131–1139. https://doi.org/10.1007/s00170-008-1673-4

    Article  Google Scholar 

  21. Li P, Wang RS, Wang YX, Tao WY (2020) Evaluation of the ICP algorithm in 3D point cloud registration. IEEE Access 8:68030–68048

    Article  Google Scholar 

  22. Shi XJ, Liu T, Han X (2020) Improved iterative closest point (ICP) 3D point cloud registration algorithm based on point cloud filtering and adaptive fireworks for coarse registration. Int J Remote Sens 41(8):3197–3220. https://doi.org/10.1080/01431161.2019.1701211

    Article  Google Scholar 

  23. Safaeian HN, Miri M, Rashki M (2016) An enhanced simulation-based design method coupled with meta-heuristic search algorithm for accurate reliability-based design optimization. Eng Comput 32(3):477–495. https://doi.org/10.1007/s00366-015-0427-9

    Article  Google Scholar 

  24. Sierra MR, Coello CA (2005) Improving PSO-based multi-objective optimization using crowding, mutation and 6-dominance. Lect Notes Comput Sci 3410:505–519

    Article  Google Scholar 

  25. Da Y, Ge XR (2005) An improved PSO-based ANN with simulated annealing technique. Neurocomputing 63:527–533. https://doi.org/10.1016/j.neucom.2004.07.002

    Article  Google Scholar 

  26. Altintas Y (2012) Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design. Ind Robot 31(1):B84. https://doi.org/10.1115/1.1399383

    Article  Google Scholar 

  27. Liu N, Zhang YF, Lu WF (2015) A hybrid approach to energy consumption modelling based on cutting power: a milling case. J Clean Prod 104(1):264–272

    Article  Google Scholar 

Download references

Funding

This study is supported by the National Key R&D Program of China (No. 2020YFB1710400) and Natural Science Basic Research Plan in Shaanxi Province of China (No. 2021JM-054).

Author information

Authors and Affiliations

  1. Key Laboratory of High Performance Manufacturing for Aero Engine (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi’an, 710072, China

    Baohai Wu, Yang Zhang, Guangxin Liu & Ying Zhang

  2. Engineering Research Center of Advanced Manufacturing Technology for Aero Engine (Northwestern Polytechnical University), Ministry of Education, Xi’an, 710072, China

    Baohai Wu, Yang Zhang, Guangxin Liu & Ying Zhang

Authors
  1. Baohai Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guangxin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Baohai Wu: supervision, conceptualization, methodology, resources, funding acquisition.

Yang Zhang: methodology, experimentation, data curation, writing-original draft.

Guangxin Liu: supervision, methodology.

Ying Zhang: supervision, reviewing, and editing.

All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Ying Zhang.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Applicable.

Conflict of interest

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, B., Zhang, Y., Liu, G. et al. Feedrate optimization method based on machining allowance optimization and constant power constraint. Int J Adv Manuf Technol 115, 3345–3360 (2021). https://doi.org/10.1007/s00170-021-07381-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-07381-z

Keywords

Search

Navigation