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Research Articles

Study on milling behavior of TiAlN coated tool with variable distribution density micro-texture

ORCID Icon, ORCID Icon & ORCID Icon
Pages 74-97 | Published online: 03 Jan 2024
 

Abstract

Considering that after milling titanium alloy with a ball-end milling cutter, the wear degree of different areas of the rake face is different, which indicates that the tool-chip contact conditions in this area are different, and then the micro-texture action mode is different. To improve the effect of micro-texture, this paper establishes a mathematical distribution model of variable distribution density micro-texture based on the two-zone method, builds a variable distribution density micro-texture ball-end milling titanium alloy test platform, studies the influence of variable density micro-texture parameters on tool milling behavior, and optimizes the parameters based on the improved particle swarm optimization algorithm. The results show that the micro-texture with variable distribution density has a positive effect on the milling behavior of the tool. The micro-texture parameters of the stickiness area have the greatest influence on the milling behavior of the tool. A texture parameter is optimized, and the overall milling behavior of the variable density micro-texture TiAlN coated milling cutter is optimal. This study provides a theoretical basis for the design and preparation of variable distribution density micro-textured tools.

    Highlights

  1. Based on the two-zone method, the mathematical distribution model of the variable distribution density micro-texture of the rake face of the tool is constructed.

  2. The influence mechanism of micro-texture parameters on the milling behavior of a ball-end milling cutter is explored.

  3. The multi-objective optimization of the variable density micro-texture parameters was carried out, and the optimal parameter combination was obtained.

  4. It is verified that the variable distribution density of the micro-texture has a positive effect on the cutting performance of the tool.

Acknowledgments

The authors thank the National Natural Science Foundation for this research funding and support. The authors thank you to the authors in the course of this research to help and support, from project selection to content design, from the experimental process to the paper writing are given careful guidance.

Author contributions

Experimental design, SC.Y; micro-texture preparation, LK.L; the experimental platform was built, LK.L; experimental data organization, CS.H; milling behavior analysis, LK.L and CS.H; parameter optimization, SC.Y; writing-original preparation, LK.L; writing-reviewing and editing, SC.Y; funding acquisition, SC.Y. All authors have read and agreed to the published version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded by the National Natural Science Foundation of China, grant no. 51875144.

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