Optimizing the micro-texture and cutting parameters of ball-end milling cutters to achieve milling stability

Abstract

The machining of titanium alloy can lead to serious tool wear, with this resulting in a lack of milling system stability and chatter. This paper looks at the relationship between milling stability and the use of micro-texturing on cutter surfaces, which aims to reduce tool wear. A stability lobe diagram is first drawn that is based on a dynamic model of the milling of titanium alloy by a ball-end milling cutter. This provides the cutting parameters for a stable domain. A micro-texture distribution model is then established for different cutting depths and the influence of micro-texture parameters on the milling performance of cutting tools according to various cutting parameters studied. The parameters are then optimized using an artificial bee colony algorithm, with the milling force, machined surface quality, and tool wear being adopted as the principal evaluation indicators. This provides a theoretical basis for the future selection of optimal cutting and micro-texture parameters.

Keywords

Micro-texture ball-end milling cutter titanium alloy milling stability parameter optimization chatter

Get full access to this article

View all access options for this article.

References

Lianyao

Lingyan

Pengnan

, et al. Research progress in cutting titanium alloys with micro-textured tools[J]. Aerospace Materials Technology 2020; 50: 8–12.

Tong

. Finite element analysis and parameter optimization for milling of thin-walled Titanium alloy bending parts [D]. Shandong: Shandong Jianzhu University, 2020.

Yangbin

Qingchun

Peihao

, et al. Simulation of cutting titanium alloy with surface micro-textured tools based on ABAQUS[J]. Tool Technology 2017; 51: 39–42.

Changcheng

Runlan

Hua

. Considering the influence of material removal on the milling stability of thin-walled workpieces [J]. Manufacturing Automation 2020; 42: 95–99 + 103.

Yayun

Lili

Jianxin

, et al. Research on the wear mechanism of dry cutting alumina ceramic green body with textured cemented carbide tools on the flank [J]. Aviation Manufacturing Technology 2018; 61: 38–44.

Yun

Jianxin

Hongzhi

, et al. The effect of micro-texture on the surface friction properties of steel rails[J]. Manufacturing Technology and Machine Tool 2019; 11: 17–23.

Shucai

Zhiwei

Yuhua

, et al. Finite element simulation of titanium alloy processing with micro-textured ball-end milling cutter[J]. J Shenyang Univ of Technol 2015; 37: 530–535.

Yongmei

Xiangyang

Guosheng

. Research on the performance of texture cutter Al7075-T6 in high-speed dry cutting [J]. Machine Tool and Hydraulics 2020; 48: 159–163.

Nan

. The effect of strip groove micro-texture on the cutting performance of PCBN tools[J]. Diamond and Abrasive Engineering 2020; 40: 80–85.

10.

Lingli

. Analysis of milling system chatter stability and its influencing factors [D]. Shenyang: Northeastern University, 2015.

11.

Ruiliang

Xubo

Zhiwen

, et al. Experimental study on ultra-precision turning surface morphology of 2024 aluminum alloy[J]. Manufacturing Technology and Machine Tool 2020; 07: 72–75.

12.

Sridhar

Hohn

Long

. A stability algorithm for the general milling process: contribution to machine tool chatter research—7. J. Eng. Ind. 1968; 90: 330–334.

13.

Milton

Elesandro

Luciana

, et al. The role of tool presetting in milling stability uncertainty[J]. Procedia Manuf 2018; 26: 164–172.

14.

Lin

Y-C

K-D

Shih

W-C

, et al. Prediction of surface roughness based on cutting parameters and machining vibration in end milling using regression method and artificial neural network[J]. Appl Sci 2020; 10.

15.

Rohit

Bhagat

. A novel ensemble method based on the SBLMD-ANN-MOPSO approach for predicting milling stability regimes[J]. Meas Sci Technol 2022; 33.

16.

Rohit

Bhagat

. An ensemble approach to maximize metal removal rate for chatter free milling[J]. J Comput Sci 2022: 101567.

17.

Rohit

Bhagat

. Extenuating chatter vibration in milling process using a new ensemble approach[J]. Journal of Vibration Engineering & Technologies 2022: 1–18.

18.

Shucai

. Research on the influence of tool mesoscopic geometric features on the cutting performance of Titanium alloy. Beijing: Science Press, 2017.

19.

Qiu

. Analysis and experimental research on multiple influencing factors of chatter stability in high speed milling[D]. Suzhou: Soochow University, 2018.

20.

Chao

. Dynamic modeling and stability analysis of milling process[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2020.

21.

Budak

Altintas¸

. Analytical prediction of chatter stability in milling—part I：general formulation[J]. J Dyn Syst Meas Contr, 1998, 120：22–30.

22.

Jingshuo

. Research on dynamic modeling and simulation in the process of ball End milling cutter [D]. Shenyang: Northeastern University, 2014.

23.

Lin

, et al. The identification of the cutting force coefficients for ball-end finish milling[J]. The International Journal of Advanced Manufacturing Technology 2019; 102: 4121–4135.

24.

Xin

. Research on precise distribution design and parameter optimization of micro-texture of ball-end milling cutter [D]. Harbin: Harbin University of Science and Technology, 2019.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

2.37 MB