Skip to main content
Top

18-10-2024

Allowance distribution and parameters optimization for high-performance machining of low rigidity parts in multistage machining processes

Authors: Hao Sun, Sheng-Qiang Zhao, Fang-Yu Peng, Rong Yan, Xiao-Wei Tang

Published in: Advances in Manufacturing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

There are a large number of low rigidity parts in the aerospace field, and how to achieve high-performance manufacturing in their multistage machining processes has received increasing attention. Optimizing the distribution of machining allowance and machining parameters is one of the most convenient ways to improve the machining performance of these parts. In this paper, firstly, considering the machining accuracy and machining efficiency comprehensively, the error efficiency cooperation coefficient of low rigidity parts during machining is established. Based on the semi-parametric regression theory and measured data, the machining error transfer factor within the cooperation coefficient is calibrated. Secondly, the machining optimization strategy based on the Bayesian framework is proposed, and the optimization of multiple machining parameters is realized with the goal of minimizing the error efficiency cooperation coefficient. Finally, the optimization software of machining processes of low rigidity parts for engineering application is developed. In the verification experiments of blade parts, the error efficiency cooperation coefficient is reduced to 0.032 1 after optimization, and the average improvement of machining errors of all measured points is \(14.31\,{\upmu {\text{m}}}\). Besides, the above method is applied to low rigidity shaft parts, and the effectiveness of the proposed method is further verified.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
3.
go back to reference Li X, Gong YD, Ding MX et al (2023) Research on prediction and compensation strategy of milling deformation error of aitanium alloy integral blisk blade. Int J Adv Manuf Technol 127:1–19CrossRef Li X, Gong YD, Ding MX et al (2023) Research on prediction and compensation strategy of milling deformation error of aitanium alloy integral blisk blade. Int J Adv Manuf Technol 127:1–19CrossRef
4.
go back to reference Zhang ZZ, Cai YL, Xi XL et al (2023) Non-uniform machining allowance planning method of thin-walled parts based on the workpiece deformation constraint. Int J Adv Manuf Technol 124(7):2185–2198CrossRef Zhang ZZ, Cai YL, Xi XL et al (2023) Non-uniform machining allowance planning method of thin-walled parts based on the workpiece deformation constraint. Int J Adv Manuf Technol 124(7):2185–2198CrossRef
5.
go back to reference Sun H, Zhao SQ, Peng FY et al (2022) In-situ prediction of machining errors of thin-walled parts: an engineering knowledge based sparse Bayesian learning approach. J Intell Manuf 35:387–411CrossRef Sun H, Zhao SQ, Peng FY et al (2022) In-situ prediction of machining errors of thin-walled parts: an engineering knowledge based sparse Bayesian learning approach. J Intell Manuf 35:387–411CrossRef
6.
go back to reference Zhang T, Li BB, Zhao SQ et al (2022) A knowledge-embedded end-to-end intelligent reasoning method for processing quality of shaft parts. In: Liu H, Yin ZP, Liu LQ (eds) Intelligent robotics and applications. The 15th international conference, ICIRA 2022, Harbin, China, 1–3 August, 2022, Proceedings, Part IV, Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_39CrossRef Zhang T, Li BB, Zhao SQ et al (2022) A knowledge-embedded end-to-end intelligent reasoning method for processing quality of shaft parts. In: Liu H, Yin ZP, Liu LQ (eds) Intelligent robotics and applications. The 15th international conference, ICIRA 2022, Harbin, China, 1–3 August, 2022, Proceedings, Part IV, Springer, Cham. https://​doi.​org/​10.​1007/​978-3-031-13841-6_​39CrossRef
7.
go back to reference Lacalle LNLD, Lamikiz A, Sánchez JA et al (2007) Toolpath selection based on the minimum deflection cutting forces in the programming of complex surfaces milling. Int J Mach Tools Manuf 47(2):388–400CrossRef Lacalle LNLD, Lamikiz A, Sánchez JA et al (2007) Toolpath selection based on the minimum deflection cutting forces in the programming of complex surfaces milling. Int J Mach Tools Manuf 47(2):388–400CrossRef
8.
go back to reference Geng L, Liu PL, Liu K (2015) Optimization of cutter posture based on cutting force prediction for five-axis machining with ball-end cutters. Int J Adv Manuf Technol 78(5/8):1289–1303CrossRef Geng L, Liu PL, Liu K (2015) Optimization of cutter posture based on cutting force prediction for five-axis machining with ball-end cutters. Int J Adv Manuf Technol 78(5/8):1289–1303CrossRef
9.
go back to reference Ma JW, Song DN, Jia ZY et al (2018) Tool-path planning with constraint of cutting force fluctuation for curved surface machining. Precis Eng J Int Soc Precis Eng Nanotechnol 51:614–624 Ma JW, Song DN, Jia ZY et al (2018) Tool-path planning with constraint of cutting force fluctuation for curved surface machining. Precis Eng J Int Soc Precis Eng Nanotechnol 51:614–624
10.
go back to reference Wang L, Yuan X, Si H et al (2019) Feedrate scheduling method for constant peak cutting force in five-axis flank milling process. Chin J Aeronaut 33(7):2055–2069CrossRef Wang L, Yuan X, Si H et al (2019) Feedrate scheduling method for constant peak cutting force in five-axis flank milling process. Chin J Aeronaut 33(7):2055–2069CrossRef
11.
go back to reference Sivasakthivel PS, Sudhakaran R (2013) Optimization of machining parameters on temperature rise in end milling of Al 6063 using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 67:2313–2323CrossRef Sivasakthivel PS, Sudhakaran R (2013) Optimization of machining parameters on temperature rise in end milling of Al 6063 using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 67:2313–2323CrossRef
12.
go back to reference Cakiroglu R, Acr A (2013) Optimization of cutting parameters on drill bit temperature in drilling by Taguchi method. Measurement 46(9):3525–3531CrossRef Cakiroglu R, Acr A (2013) Optimization of cutting parameters on drill bit temperature in drilling by Taguchi method. Measurement 46(9):3525–3531CrossRef
13.
go back to reference Wei B, Tan G, Yin N et al (2016) Research on inverse problems of heat flux and simulation of transient temperature field in high-speed milling. Int J Adv Manuf Technol 84(9/12):2067–2078CrossRef Wei B, Tan G, Yin N et al (2016) Research on inverse problems of heat flux and simulation of transient temperature field in high-speed milling. Int J Adv Manuf Technol 84(9/12):2067–2078CrossRef
14.
go back to reference Mirkoohi E, Bocchini P, Liang SY (2019) Analytical temperature predictive modeling and non-linear optimization in machining. Int J Adv Manuf Technol 102:1557–1566CrossRef Mirkoohi E, Bocchini P, Liang SY (2019) Analytical temperature predictive modeling and non-linear optimization in machining. Int J Adv Manuf Technol 102:1557–1566CrossRef
15.
go back to reference Hu PC, Kai T (2011) Improving the dynamics of five-axis machining through optimization of workpiece setup and tool orientations. Comput Aided Des 43(12):1693–1706CrossRef Hu PC, Kai T (2011) Improving the dynamics of five-axis machining through optimization of workpiece setup and tool orientations. Comput Aided Des 43(12):1693–1706CrossRef
17.
go back to reference Mokhtari A, Jalili MM, Mazidi A (2021) Optimization of different parameters related to milling tools to maximize the allowable cutting depth for chatter-free machining. Proc Inst Mech Eng Part B J Eng Manuf 235(1/2):230–241CrossRef Mokhtari A, Jalili MM, Mazidi A (2021) Optimization of different parameters related to milling tools to maximize the allowable cutting depth for chatter-free machining. Proc Inst Mech Eng Part B J Eng Manuf 235(1/2):230–241CrossRef
20.
go back to reference Duan XY, Peng FY, Zhu KP et al (2019) Tool orientation optimization considering cutter deflection error caused by cutting force for multi-axis sculptured surface milling. Int J Adv Manuf Technol 103(5/8):1925–1934CrossRef Duan XY, Peng FY, Zhu KP et al (2019) Tool orientation optimization considering cutter deflection error caused by cutting force for multi-axis sculptured surface milling. Int J Adv Manuf Technol 103(5/8):1925–1934CrossRef
21.
go back to reference Silva L, Yoshioka H, Shinno H et al (2019) Tool orientation angle optimization for a multi-axis robotic milling system. Int J Autom Technol 13(5):574–582CrossRef Silva L, Yoshioka H, Shinno H et al (2019) Tool orientation angle optimization for a multi-axis robotic milling system. Int J Autom Technol 13(5):574–582CrossRef
22.
go back to reference Xiao QB, Wan M, Zhang WH et al (2022) Tool orientation optimization for the five-axis CNC machining to constrain the contour errors without interference. J Manuf Process 76:46–56CrossRef Xiao QB, Wan M, Zhang WH et al (2022) Tool orientation optimization for the five-axis CNC machining to constrain the contour errors without interference. J Manuf Process 76:46–56CrossRef
23.
go back to reference Koike Y, Matsubara A, Yamaji I (2013) Design method of material removal process for minimizing workpiece displacement at cutting point. CIRP Ann Manuf Technol 62(1):419–422CrossRef Koike Y, Matsubara A, Yamaji I (2013) Design method of material removal process for minimizing workpiece displacement at cutting point. CIRP Ann Manuf Technol 62(1):419–422CrossRef
25.
go back to reference Li ZP, Peng FY, Yan R et al (2021) Configuration optimization through redundancy angle and tool posture by force induced error index in robot ball-end milling. Procedia CIRP 101:150–153CrossRef Li ZP, Peng FY, Yan R et al (2021) Configuration optimization through redundancy angle and tool posture by force induced error index in robot ball-end milling. Procedia CIRP 101:150–153CrossRef
26.
go back to reference Li XY, Li L, Yang YF et al (2022) Machining deformation of single-sided component based on finishing allowance optimization. Chin J Aeronaut 33(9):2434–2444CrossRef Li XY, Li L, Yang YF et al (2022) Machining deformation of single-sided component based on finishing allowance optimization. Chin J Aeronaut 33(9):2434–2444CrossRef
28.
go back to reference Lan T (2010) Fuzzy deduction material removal rate optimization for computer numerical control turning. Am J Appl Sci 7(7):1026–1031CrossRef Lan T (2010) Fuzzy deduction material removal rate optimization for computer numerical control turning. Am J Appl Sci 7(7):1026–1031CrossRef
29.
go back to reference Das MK, Kumar K, Barman TK et al (2012) Optimization of material removal rate in EDM using Taguchi method. Adv Mater Res 626:270–274CrossRef Das MK, Kumar K, Barman TK et al (2012) Optimization of material removal rate in EDM using Taguchi method. Adv Mater Res 626:270–274CrossRef
30.
go back to reference Mukherjee S, Kamal A, Kumar K (2014) Optimization of material removal rate during turning of SAE 1020 material in CNC lathe using Taguchi technique. Proc Eng 97:29–35CrossRef Mukherjee S, Kamal A, Kumar K (2014) Optimization of material removal rate during turning of SAE 1020 material in CNC lathe using Taguchi technique. Proc Eng 97:29–35CrossRef
32.
go back to reference Balogun VA, Edem IF, Adekunle AA et al (2016) Specific energy based evaluation of machining efficiency. J Clean Prod 116:187–197CrossRef Balogun VA, Edem IF, Adekunle AA et al (2016) Specific energy based evaluation of machining efficiency. J Clean Prod 116:187–197CrossRef
33.
go back to reference Xu K, Luo M, Tang K (2016) Machine based energy-saving tool path generation for five-axis end milling of freeform surfaces. J Clean Prod 139:1207–1223CrossRef Xu K, Luo M, Tang K (2016) Machine based energy-saving tool path generation for five-axis end milling of freeform surfaces. J Clean Prod 139:1207–1223CrossRef
34.
go back to reference Zhang C, Jiang P, Zhang L et al (2017) Energy-aware integration of process planning and scheduling of advanced machining workshop. Proc Inst Mech Eng Part B J Eng Manuf 231(11):2040–2055CrossRef Zhang C, Jiang P, Zhang L et al (2017) Energy-aware integration of process planning and scheduling of advanced machining workshop. Proc Inst Mech Eng Part B J Eng Manuf 231(11):2040–2055CrossRef
35.
go back to reference Shin SJ, Woo J, Rachuri S (2017) Energy efficiency of milling machining: component modeling and online optimization of cutting parameters. J Clean Prod 161:12–29CrossRef Shin SJ, Woo J, Rachuri S (2017) Energy efficiency of milling machining: component modeling and online optimization of cutting parameters. J Clean Prod 161:12–29CrossRef
36.
go back to reference Xu K, Tang K (2014) Five-axis tool path and feed rate optimization based on the cutting force-area quotient potential field. Int J Adv Manuf Technol 75(9/12):1661–1679CrossRef Xu K, Tang K (2014) Five-axis tool path and feed rate optimization based on the cutting force-area quotient potential field. Int J Adv Manuf Technol 75(9/12):1661–1679CrossRef
37.
go back to reference Li C, Chen X, Tang Y et al (2017) Selection of optimum parameters in multi-pass face milling for maximum energy efficiency and minimum production cost. J Clean Prod 140:1805–1818CrossRef Li C, Chen X, Tang Y et al (2017) Selection of optimum parameters in multi-pass face milling for maximum energy efficiency and minimum production cost. J Clean Prod 140:1805–1818CrossRef
38.
go back to reference Cui XB, Guo JX (2018) Identification of the optimum cutting parameters in intermittent hard turning with specific cutting energy, damage equivalent stress, and surface roughness considered. Int J Adv Manuf Technol 96:4281–4293CrossRef Cui XB, Guo JX (2018) Identification of the optimum cutting parameters in intermittent hard turning with specific cutting energy, damage equivalent stress, and surface roughness considered. Int J Adv Manuf Technol 96:4281–4293CrossRef
39.
go back to reference Zhu ZR, Peng FY, Tang XW et al (2019) Specific cutting energy index (SCEI)-based process signature for high-performance milling of hardened steel. Int J Adv Manuf Technol 103:1–13CrossRef Zhu ZR, Peng FY, Tang XW et al (2019) Specific cutting energy index (SCEI)-based process signature for high-performance milling of hardened steel. Int J Adv Manuf Technol 103:1–13CrossRef
40.
go back to reference Chen C, Peng FY, Yan R et al (2019) Stiffness performance index based posture and feed orientation optimization in robotic milling process. Robot Comput Integr Manuf 55:29–40CrossRef Chen C, Peng FY, Yan R et al (2019) Stiffness performance index based posture and feed orientation optimization in robotic milling process. Robot Comput Integr Manuf 55:29–40CrossRef
41.
go back to reference Zhu ZR, Peng FY, Yan R et al (2020) Influence mechanism of machining angles on force induced error and their selection in five axis bullnose end milling. Chin J Aeronaut 33(12):3447–3459CrossRef Zhu ZR, Peng FY, Yan R et al (2020) Influence mechanism of machining angles on force induced error and their selection in five axis bullnose end milling. Chin J Aeronaut 33(12):3447–3459CrossRef
44.
go back to reference Sun YW, Xu JT, Guo DM et al (2009) A unified localization approach for machining allowance optimization of complex curved surfaces. Precis Eng 33(4):516–523CrossRef Sun YW, Xu JT, Guo DM et al (2009) A unified localization approach for machining allowance optimization of complex curved surfaces. Precis Eng 33(4):516–523CrossRef
45.
go back to reference Zhang Y, Zhang DH, Wu BH (2015) An approach for machining allowance optimization of complex parts with integrated structure. J Comput Des Eng 2:248–252 Zhang Y, Zhang DH, Wu BH (2015) An approach for machining allowance optimization of complex parts with integrated structure. J Comput Des Eng 2:248–252
46.
go back to reference Wu XN, Dai W (2016) Research on machining allowance distribution optimization based on processing defect risk. Procedia CIRP 56:508–511CrossRef Wu XN, Dai W (2016) Research on machining allowance distribution optimization based on processing defect risk. Procedia CIRP 56:508–511CrossRef
47.
go back to reference Chen YZ, Chen WF, Liang RJ et al (2017) Machining allowance optimal distribution of thin-walled structure based on deformation control. Appl Mech Mater 868:158–165CrossRef Chen YZ, Chen WF, Liang RJ et al (2017) Machining allowance optimal distribution of thin-walled structure based on deformation control. Appl Mech Mater 868:158–165CrossRef
49.
go back to reference Wu BH, Zhang Y, Liu GX et al (2021) Feedrate optimization method based on machining allowance optimization and constant power constraint. Int J Adv Manuf Technol 115(9/10):3345–3360CrossRef Wu BH, Zhang Y, Liu GX et al (2021) Feedrate optimization method based on machining allowance optimization and constant power constraint. Int J Adv Manuf Technol 115(9/10):3345–3360CrossRef
50.
go back to reference Xin HM, Dong MM, Xian C et al (2023) Optimization method for rough-finish milling allowance based on depth control of milling affected layer. Int J Adv Manuf Technol 126(5/6):2083–2095CrossRef Xin HM, Dong MM, Xian C et al (2023) Optimization method for rough-finish milling allowance based on depth control of milling affected layer. Int J Adv Manuf Technol 126(5/6):2083–2095CrossRef
52.
go back to reference Sun H, Peng FY, Zhao SQ et al (2022) Uncertainty calibration and quantification of surrogate model for estimating the machining distortion of thin-walled parts. Int J Adv Manuf Technol 120(1):719–741CrossRef Sun H, Peng FY, Zhao SQ et al (2022) Uncertainty calibration and quantification of surrogate model for estimating the machining distortion of thin-walled parts. Int J Adv Manuf Technol 120(1):719–741CrossRef
53.
go back to reference Zhu ZR, Peng FY, Yan R et al (2018) High efficiency simulation of five-axis cutting force based on the symbolically solvable cutting contact boundary model. Int J Adv Manuf Technol 94(5/8):2435–2455CrossRef Zhu ZR, Peng FY, Yan R et al (2018) High efficiency simulation of five-axis cutting force based on the symbolically solvable cutting contact boundary model. Int J Adv Manuf Technol 94(5/8):2435–2455CrossRef
54.
go back to reference Jin JH, Shi JJ (1999) State space modeling of sheet metal assembly for dimensional control. J Manuf Sci Eng Trans ASME 121(4):756–762CrossRef Jin JH, Shi JJ (1999) State space modeling of sheet metal assembly for dimensional control. J Manuf Sci Eng Trans ASME 121(4):756–762CrossRef
55.
go back to reference Zhou SY, Huang Q, Shi JJ (2003) State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors. IEEE Trans Robot Autom 19(2):296–309CrossRef Zhou SY, Huang Q, Shi JJ (2003) State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors. IEEE Trans Robot Autom 19(2):296–309CrossRef
56.
go back to reference Zhang L, Zhang ZS, Zhou YF et al (2013) Stream of variation modeling and analysis for manufacturing processes based on a semi-parametric regression model. Chin J Mech Eng 49(15):180–185CrossRef Zhang L, Zhang ZS, Zhou YF et al (2013) Stream of variation modeling and analysis for manufacturing processes based on a semi-parametric regression model. Chin J Mech Eng 49(15):180–185CrossRef
57.
go back to reference Sun H, Zhao SQ, Zhang T et al (2022) Analysis and inference of stream of dimensional errors in multistage machining process based on an improved semi-parametric model. In: 2022 IEEE/ASME international conference on advanced intelligent mechatronics (AIM), 11–15 July, Sapporo, Hokkaido, Japan Sun H, Zhao SQ, Zhang T et al (2022) Analysis and inference of stream of dimensional errors in multistage machining process based on an improved semi-parametric model. In: 2022 IEEE/ASME international conference on advanced intelligent mechatronics (AIM), 11–15 July, Sapporo, Hokkaido, Japan
61.
go back to reference Patil JJ, Wan TC, Gong S et al (2023) Bayesian-optimization-assisted laser reduction of poly(acrylonitrile) for electrochemical application. ACS Nano 17(5):4999–5013CrossRef Patil JJ, Wan TC, Gong S et al (2023) Bayesian-optimization-assisted laser reduction of poly(acrylonitrile) for electrochemical application. ACS Nano 17(5):4999–5013CrossRef
62.
go back to reference Rasmussen CE (2003) Gaussian processes in machine learning. In: Advanced lectures on machine learning, ML Summer Schools, Canberra, Australia, 2–14 Feb 2003, pp 63–71 Rasmussen CE (2003) Gaussian processes in machine learning. In: Advanced lectures on machine learning, ML Summer Schools, Canberra, Australia, 2–14 Feb 2003, pp 63–71
63.
go back to reference Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Global Optim 13(4):455–492MathSciNetCrossRef Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Global Optim 13(4):455–492MathSciNetCrossRef
Metadata
Title
Allowance distribution and parameters optimization for high-performance machining of low rigidity parts in multistage machining processes
Authors
Hao Sun
Sheng-Qiang Zhao
Fang-Yu Peng
Rong Yan
Xiao-Wei Tang
Publication date
18-10-2024
Publisher
Shanghai University
Published in
Advances in Manufacturing
Print ISSN: 2095-3127
Electronic ISSN: 2195-3597
DOI
https://doi.org/10.1007/s40436-024-00520-1

Premium Partners