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The International Journal of Advanced Manufacturing Technology

Published in:

03-11-2021 | ORIGINAL ARTICLE

RTCP test axis motion planning for five-axis machine tool dynamic performance using observability optimization based on modified genetic algorithm

Authors: Qicheng Ding, Wei Wang, Jiexiong Ding, Jing Zhang, Chong Hu, Fengmin Lei, Li Du, Liping Wang

Published in: The International Journal of Advanced Manufacturing Technology | Issue 1-2/2022

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Abstract

In the five-axis CNC machining process, the dynamic tracking error due to servo dynamic performance deficiency is a main cause of processing inaccuracy during precise high-speed machining. The rotation tool center point (RTCP) test is commonly used to measure the dynamic performance of five-axis machine tools. The key to the capability of the RTCP test is axis motion planning in the test process. However, the axis motion plans for RTCP tests are usually based on simple motion instruction or engineering experience; the mechanism of the discrepancy between different axis motion plans is unclear. In this study, the axis motion planning process for RTCP dynamic performance tests is analyzed, and a novel axis motion planning method is proposed. The axis motion planning process is directly connected to the mechanism of dynamic tracking error; error observability is used as the index to guide RTCP axis motion planning. A modified genetic algorithm is used to select the sensitive rotary axis position and velocity combos; cubic spline interpolation is used to plan the axis motions based on the sensitive position and velocity combos.

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Neugebauer R, Denkena B, Wegener K (2007) Mechatronic systems for machine tools. CIRP Ann 56(2):657–686CrossRef

Lyu D, Liu Q, Liu H, Zhao W (2020) Dynamic error of CNC machine tools: a state-of-the-art review. Int J Adv Manuf Technol 106(5–6):1869–1891CrossRef

Jia Z-y, Ma J-w, Song D-n, Wang F-j, Liu W (2018) A review of contouring-error reduction method in multi-axis CNC machining. Int J Mach Tools Manuf 125:34–54CrossRef

Andolfatto L, Lavernhe S, Mayer J (2011) Evaluation of servo, geometric and dynamic error sources on five-axis high-speed machine tool. Int J Mach Tools Manuf 51(10–11):787–796CrossRef

Lei W-T, Wang W-C, Fang T-C (2014) Ballbar dynamic tests for rotary axes of five-axis CNC machine tools. Int J Mach Tools Manuf 82:29–41CrossRef

Lin M-T, Wu S-K (2013) Modeling and analysis of servo dynamics errors on measuring paths of five-axis machine tools. Int J Mach Tools Manuf 66:1–14CrossRef

Jiang Z, Ding J, Ding Q, Du L, Wang W (2018) An attempt of error tracing and compensation method of the linkage error of five-axis CNC machine tool. In: ASME 2018 international design engineering technical conferences and computers and information in engineering conference. American Society of Mechanical Engineers Digital Collection

Jia Z, Song D, Ma J, Gao Y (2017) Pre-compensation for continuous-path running trajectory error in high-speed machining of parts with varied curvature features. Chin J Mech Eng 30(1):37–45CrossRef

Zhang D, Chen Y, Chen Y (2016) Iterative pre-compensation scheme of tracking error for contouring error reduction. Int J Adv Manuf Technol 87(9–12):3279–3288CrossRef

10.

Song D-n, Ma J-w, Jia Z-y, Gao Y-y (2017) Estimation and compensation for continuous-path running trajectory error in high-feed-speed machining. Int J Adv Manuf Technol 89(5–8):1495–1508CrossRef

11.

Yang J, Altintas Y (2015) A generalized on-line estimation and control of five-axis contouring errors of CNC machine tools. Int J Mach Tools Manuf 88:9–23CrossRef

12.

Yang J, Zhang H-T, Ding H (2017) Contouring error control of the tool center point function for five-axis machine tools based on model predictive control. Int J Adv Manuf Technol 88(9–12):2909–2919CrossRef

13.

Zhong G, Shao Z, Deng H, Ren J (2017) Precise position synchronous control for multi-axis servo systems. IEEE Trans Industr Electron 64(5):3707–3717CrossRef

14.

Zhang J, Ding J, Li Q, Jiang Z, Ding Q, Du L, Wang W (2019) A new contouring error estimation for the high form accuracy of a multi-axis CNC machine tool. Int J Adv Manuf Technol 101(5–8):1403–1421CrossRef

15.

NAS979 (1969) Uniform cutting test-NAS series. Metal cutting equipment. NAS, USA

16.

Song Z, Cui Y (2011) S-shape detection test piece and a detection method for detecting the precision of the numerical control milling machine. US Patent US8061052B2. https://patents.google.com/patent/US8061052B2/en?oq=US8061052B2

17.

Wang W, Jiang Z, Tao W, Zhuang W (2015) A new test part to identify performance of five-axis machine tool—part I: geometrical and kinematic characteristics of S part. Int J Adv Manuf Technol 79(5–8):729–738CrossRef

18.

Wang W, Jiang Z, Li Q, Tao W (2015) A new test part to identify performance of five-axis machine tool-part II validation of S part. Int J Adv Manuf Technol 79(5–8):739–756CrossRef

19.

He W, Wang L, Guan L (2020) A novel approach to calculating the dynamic error reflected on an S-shaped test piece. Proceedings of the Institution of Mechanical Engineers, Part C: J Mech Eng Sci 0954406220962150

20.

ISO 10791 (2020) Test conditions for machining centers-part 7: accuracy of finished test pieces. https://www.iso.org/standard/73814.html

21.

Bryan J (1982) A simple method for testing measuring machines and machine tools. Part 2: construction details. Precis Eng 4(3):125–138CrossRef

22.

Bryan J (1982) A simple method for testing measuring machines and machine tools Part 1: principles and applications. Precis Eng 4(2):61–69CrossRef

23.

Le Flohic J, Paccot F, Bouton N, Chanal H (2018) Model-based method for feed drive tuning applied to serial machine tool. Int J Adv Manuf Technol 95(1–4):735–745CrossRef

24.

Ding Q, Wang W, Du L, Ding J, Zhang J, Wang L (2020) Dynamic performance test under complicated motion states for five-axis machine tools based on double ballbar. Int J Adv Manuf Technol 111(3):765–783CrossRef

25.

Weikert S (2004) R-test, a new device for accuracy measurements on five axis machine tools. CIRP Ann 53(1):429–432CrossRef

26.

Jywe W, Hsu T-H, Liu C-H (2012) Non-bar, an optical calibration system for five-axis CNC machine tools. Int J Mach Tools Manuf 59:16–23CrossRef

27.

Hong C, Ibaraki S (2013) Non-contact R-test with laser displacement sensors for error calibration of five-axis machine tools. Precis Eng 37(1):159–171CrossRef

28.

Ding J, Zhou Z, Liu F, Du L, Wang W, Jiang Z, Song Z, Jiang J, Huang Z, Deng M (2018) Five-axis machine tool cutter posture and cutter tip position error synchronous detection mechanism. Europe Patent EP3238875B1

29.

Jiang Z, Ding J, Zhang J, Du L, Wang W (2018) Research on error tracing method of five-axis CNC machine tool linkage error. J Braz Soc Mech Sci Eng 40(4):209CrossRef

30.

Ding Q, Ding J, Zhang J, Du L (2020) An attempt to relate dynamic tracking error to occurring situation based on additional rectilinear motion for five-axis machine tools. Adv Mech Eng 12(10):1687814020967573CrossRef

31.

ISO 10791: Test conditions for machining centers-part 6: accuracy of speeds and interpolations (2014).

32.

Bringmann B, Knapp W (2006) Model-based ‘chase-the-ball’ calibration of a 5-axes machining center. CIRP Ann 55(1):531–534CrossRef

33.

Bringmann B, Küng A, Knapp W (2005) A measuring artefact for true 3D machine testing and calibration. CIRP Ann 54(1):471–474CrossRef

34.

Jiang Z, Wang W, Li Q, Zhou Z, Ding J, Deng M (2015) Evaluation of the dynamic performance for five-axis CNC machine tools based on RTCP. Paper presented at the ASME 2015 International Mechanical Engineering Congress and Exposition, Houston, Texas, November 13–19, 2015

35.

Jiang Z, Ding J, Zhang J, Ding Q, Li Q, Du L, Wang W (2019) Research on detection of the linkage performance for five-axis CNC machine tools based on RTCP trajectories combination. Int J Adv Manuf Technol 100(1–4):941–962CrossRef

36.

Ding Q, Wang W, Jiang Z, Zhang J, Du L, Ding J (2019) RTCP detection for five-axis CNC machine tool dynamic performance based on 8-shape trajectory. In: 2019 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, pp 1709–1714

37.

Zhong L, Bi Q, Huang N, Wang Y (2018) Dynamic accuracy evaluation for five-axis machine tools using S trajectory deviation based on R-test measurement. Int J Mach Tools Manuf 125:20–33CrossRef

38.

Ding Q, Wang W, Jiang Z, Zhang J, Du L (2019) Comparison of the generating method and detecting ability of RTCP trajectories for five-axis CNC machine tool. J Mech Eng 55(20):116–127 (in Chinese)

39.

Jiang Z, Ding J, Song Z, Du L, Wang W (2016) Modeling and simulation of surface morphology abnormality of ‘S’ test piece machined by five-axis CNC machine tool. Int J Adv Manuf Technol 85(9):2745–2759CrossRef

40.

Tsai CY, Lin PD (2009) The mathematical models of the basic entities of multi-axis serial orthogonal machine tools using a modified Denavit-Hartenberg notation. Int J Adv Manuf Technol 42(9–10):1016–1024CrossRef

41.

Driels MR, Pathre US (1990) Significance of observation strategy on the design of robot calibration experiments. J Robot Syst 7(2):197–223CrossRef

42.

Yuzhe L, Jun W, Liping W, Jinsong W (2016) Measurement trajectory evaluation and error compensation for kinematic calibration of a 5-axis hybrid machine tool. J Tsinghua Univ 56(10):1047–1054

43.

Nahvi A, Hollerbach JM (1996) The noise amplification index for optimal pose selection in robot calibration. In: Proceedings of IEEE international conference on robotics and automation. IEEE, pp 647–654

44.

Li Q, Wang W, Zhang J, Shen R, Li H, Jiang Z (2019) Measurement method for volumetric error of five-axis machine tool considering measurement point distribution and adaptive identification process. Int J Mach Tools Manuf 147CrossRef

45.

Bhoskar MT, Kulkarni MOK, Kulkarni MNK, Patekar MSL, Kakandikar G, Nandedkar V (2015) Genetic algorithm and its applications to mechanical engineering: a review. Mater Today 2(4–5):2624–2630

46.

Zhang J, Ding J, Sugita N, Kizaki T, Li Q, Ding Q, Wang L (2021) A sample construction method in kinematics characteristics domain to identify the feed drive model. Precis Eng 68:82–96CrossRef

47.

He G, Sang Y, Pang K, Sun G (2018) An improved adaptive sampling strategy for freeform surface inspection on CMM. Int J Adv Manuf Technol 96(1–4):1521–1535CrossRef

48.

Haiderali AE, Madabhushi G (2016) Evaluation of curve fitting techniques in deriving p–y curves for laterally loaded piles. Geotech Geol Eng 34(5):1453–1473CrossRef

49.

Huang W, Mao G, Zhang J (2010) Research of cutter track elimination algorithm for CNC cam grinding based on cubic spline curve fitting and spline interpolation. Key Engineering Materials. Trans Tech Publ, pp 550–554

50.

Lin M-T, Wu S-K (2013) Modeling and improvement of dynamic contour errors for five-axis machine tools under synchronous measuring paths. Int J Mach Tools Manuf 72:58–72CrossRef

51.

Florussen G, Houben K, Spaan H, Spaan-Burke T (2020) Automating accuracy evaluation of 5-axis machine tools. International Journal of Automation Technology 14(3):409–416CrossRef

52.

Liu H, Xue X, Tan G (2010) Backlash error measurement and compensation on the vertical machining center. Engineering 2(6):403CrossRef

53.

Li Z, Wang Y, Wang K (2020) A data-driven method based on deep belief networks for backlash error prediction in machining centers. J Intell Manuf 31(7):1693–1705CrossRef

Title: RTCP test axis motion planning for five-axis machine tool dynamic performance using observability optimization based on modified genetic algorithm
Authors: Qicheng Ding
Wei Wang
Jiexiong Ding
Jing Zhang
Chong Hu
Fengmin Lei
Li Du
Liping Wang
Publication date: 03-11-2021
Publisher: Springer London
Published in: The International Journal of Advanced Manufacturing Technology / Issue 1-2/2022
Print ISSN: 0268-3768
Electronic ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-021-08048-5

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