A Novel Method of Stability Judgment and Milling Parameter Optimization Based on Cotes Integration Method and Neural Network

QIN Guohua;LOU Weida;LIN Feng;XU Yong

CN Submission Journal Articles Figures Search
Adv. Search

  • Sponsored by: China Association for Science and Technology (CAST)

    Editor-In-Chief: Xu Yida

    ISSN 1000-1093

    　

  • Hosted By: China Ordnance Society

    Published By: Acta Armamentarii

    CN 11-2176/TJ

Acta Armamentarii, a Chinese monthly journal, was first published in 1979. The journal is supervised by China Association for Science and Technology, sponsored by China Ordnance Society，and edited and published by Editorial Board of Acta Armamentarii. The Journal is included in all important domestic databases，and also collected by Engineering Index (EI, USA), Chemical Abstracts (CA, USA), Science Abstracts (SA, UK）, Abstract Journal (AJ, Russia), and Current Bibliography on Science and Technology(CBST).Acta Armamentarii has been selected into the journal Of Ordnance Science and Technology (Weapons Industry) Field Of High Quality Scientific and Technological Periodical Classification Catalogue (2022 edition), ranking T1, and the journal of Automotive Engineering Field of High Quality Scientific and technological Periodical classification Catalogue, ranking T2.
Acta Armamentarii ›› 2024, Vol. 45 ›› Issue (2) : 516-526. DOI: 10.12382/bgxb.2022.0673
Paper

A Novel Method of Stability Judgment and Milling Parameter Optimization Based on Cotes Integration Method and Neural Network

  • QIN Guohua1, 2*, LOU Weida2, LIN Feng1 , XU Yong1
Author information +
History +

Abstract

Milling chatter caused by regeneration effect is one of the main factors restricting machining efficiency and workpiece quality. The accurate and efficient identification of milling chatter stability is crucial to suppress the chatter and improve the production efficiency. Therefore, a predication method for milling chatter stability is investigated using the Cotes integration method (CIM) on basis of the milling vibration differential equation. The 2D stability lobe diagram (SLD) is obtained by CIM, which is compared with 1st semi-discretization method (SDM), 1st semi-discretization method (FDM) and 2nd FDM. The results show that CIM is of better convergence. Considering the effect of radial depth of cut on SLD, an iterative calculation method of 3D SLD is established. After equivalently discretizing 3D SLD surfaces as a set of nodes, a neural network prediction model between axial depth of cut and spindle speed and radial depth of cut is constructed by randomly using 90% of the nodes as the training set and 10% of the nodes as the validation set. The predicted results show that the predicted error of neural network is no more than 6%. Finally, a multi-objective optimization model of stable milling is suggested with the objective of the material removal rate and tool life in addition to the corresponding MOEA/D. It provides basic theory and technical support for obtaining the optimal milling parameters with high efficiency and low cost.

Key words

millingoperation / chatter / cotesintegration / neuralnetwork / geneticalgorithm

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations
QIN Guohua, LOU Weida, LIN Feng, XU Yong. A Novel Method of Stability Judgment and Milling Parameter Optimization Based on Cotes Integration Method and Neural Network. Acta Armamentarii. 2024, 45(2): 516-526 https://doi.org/10.12382/bgxb.2022.0673

References

［1］ALTINTA?瘙塁Y, BUDAK E. Analytical prediction of stability lobes in milling［J］. CIRP Annals-Manufacturing Technology, 1995, 44(1):357-362.
［2］INSPERGERT, STEPAN G. Updated semi-discretization method for periodic delay-differential equations with discrete delay［J］. International Journal for Numerical Methods in Engineering, 2004, 61: 117-141.
［3］INSPERGERT, STEPAN G, TURI J. On the higher-order semi-discretizations for periodic delayed systems［J］. Journal of Sound and Vibration, 2008, 313(1): 334-341.
［4］DINGY, ZHU L M, ZHANG X J, et al. A full-discretization method for prediction of milling stability［J］. International Journal of Machine Tools and Manufacture, 2010, 50(5): 502-509.
［5］CHEND, ZHANG X J, DING H. A novel Chebyshev-Gauss pseudospectral method for accurate milling stability prediction［J］. The International Journal of Advanced Manufacturing Technology, 2021, 117(9): 2867-2881.
［6］WUY, YOU Y P, LIU A M. et al. A correction method for milling stability analysis based on local truncation error［J］. The International Journal of Advanced Manufacturing Technology, 2021, 115: 2873-2887.
［7］ZHAND N, JIANG S L, LI S K, et al. A hybrid multi-step method based on 1/3 and 3/8 Simpson formulas for milling stability prediction［J］. The International Journal of Advanced Manufacturing Technology, 2022, 120(1): 265-277.
［8］WANM, DANG X B, ZHANG W H, et al. Optimization and improvement of stable processing condition by attaching additional masses for milling of thin-walled workpiece［J］. Mechanical Systems and Signal Processing, 2018, 103: 196-215.
［9］YUANH, WAN M, YANG Y, et al. A tunable passive damper for suppressing chatters in thin-wall milling by considering the varying modal parameters of the workpiece［J］. The International Journal of Advanced Manufacturing Technology, 2019, 104(9): 4605-4616.
［10］秦国华, 娄维达, 吴竹溪, 等. 基于过程阻尼和结构模态耦合的铣削稳定性分析与实验验证［J］. 中国科学:技术科学, 2020, 50(9): 1211-1225.
QIN G H, LOU W D, WU Z X, et al. Milling stability analysis and validation based on the coupling of process damping and structural mode［J］. SCIENTIA SINICA Technologica, 2020, 50(9):1211-1225 (in Chinese)
［11］张洁, 刘成颖, 郑烽, 等. 基于铣削动力学的刀具强迫振动抑制研究［J］. 机械工程学报, 2018, 54(17): 94-99.
ZHANG J, LIU C Y, ZHENG F, et. al. Research on suppression of the forced vibration of the cutter based on the milling dynamics［J］. Journal of Mechanical Engineering, 2018, 54(17): 94-99. (in Chinese)
［12］GUOJ J, LEE K M, YU M, et al. Regenerative effects of orthogonal chip dimensions on turning stability of thin-wall workpiece-tool coupled dynamics［J］. IEEE/ASME Transactions on Mechatronics, 2022, 27(5):3601-3612.
［13］曾莎莎, 彭卫平, 雷金. 基于混合算法的薄壁件铣削加工工艺参数优化［J］. 中国机械工程, 2017, 28(7): 842-845, 851.
ZENG S S, PENG W P, LEI J. Optimization of milling process parameters based on hybrid algorithm for thin-walled workpieces［J］. China Mechanical Engineering, 2017, 28(7): 842-845, 851. (in Chinese)
［14］RINGGAARDK, MOHAMMADI Y, MERRILD C, et al. Optimization of material removal rate in milling of thin-walled structures using penalty cost function［J］. International Journal of Machine Tools and Manufacture, 2019, 145: 103430.
［15］THANGARASUS K, SHANKAR S, DEVENDRAN K, et al. Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network［J］. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2020, 234(1): 329-342.
［16］郭强, 李红霞, 李玉晓. 基于改进粒子群算法的数控加工参数优化研究［J］. 机械制造与自动化, 2021, 50(3): 132-135.
GUO Q, LI H X, LI Y X. Research on optimization of NC machining parameters based on improved particle swarm optimization［J］. Machine Building & Automation, 2021, 50(3):132-135. (in Chinese)
［17］刘献礼, 孙庆贞, 岳彩旭, 等.基于数据挖掘技术的钛合金铣削工艺参数优化［J］. 计算机集成制造系统, 2022, 28(8): 2440-2448.
LIU X L, SUN Q Z, YUE C X, et. al. Optimization of milling process parameters of titanium alloy based on data mining technology［J］. Computer Integrated Manufacturing Systems, 2022, 28(8): 2440-2448. (in Chinese)
［18］曹宏瑞, 陈雪峰, 何正嘉. 主轴-切削交互过程建模与高速铣削参数优化［J］. 机械工程学报, 2013, 49(5): 161-166.
CAO H R, CHEN X F, HE Z J. Modeling of spindle-process interaction and cutting parameters optimization in high-speed milling［J］. Journal of Mechanical Engineering, 2013, 49(5): 161-166. (in Chinese)
［19］胡瑞飞, 殷鸣, 刘雁, 等. 切削稳定性约束下的铣削参数优化技术研究［J］. 机械工程学报, 2017, 53(5): 190-198.
HU R F, YIN M, LIU Y, et al. Optimization of milling parameters under constrain of process stability［J］. Journal of Mechanical Engineering, 2017, 53(5): 190-198. (in Chinese)
［20］DINGY, ZHU L M, ZHANG X J, et al. Second-order full-discretization method for milling stability prediction［J］. International Journal of Machine Tools and Manufacture, 2010, 50(5): 926-932.
［21］PALUZO-HIDALGOE, GONZALEZ-DIAZ R, GUTIRREZ-NARANJO M A. Two-hidden-layer feed-forward networks are universal approximators: a constructive approach［J］. Neural Networks, 2020, 131: 29-36.
［22］秦国华, 高杰, 叶海潮, 等. 基于融合指标和神经网络的刀具磨损预测［J］. 兵工学报, 2021, 42(9): 2013-2023.
QIN G H, GAO J, YE H C, et al. Tool wear prediction based on fusion evaluation index and neural network［J］. Acta Armamentarii, 2021, 42(9): 2013-2023. (in Chinese)
［23］ZHANG Q F, LI H. MOEA/D: a multiobjective evolutionary algorithm based on decomposittion［J］. IEEE Transactions on Evolutionary Computation, 2007, 11(6): 712-731.
br>br>
第45卷第2期2024年2月
兵工学报ACTA ARMAMENTARII
Vol.45No.2Feb.2024

7

Accesses

0

Citation

Detail
Sections
Recommended

/

〈 〉