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Sensors and Materials
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Sensors and Materials, Volume 36, Number 4(4) (2024)
Copyright(C) MYU K.K.
pp. 1591-1604
S&M3620 Research Paper of Special Issue
https://doi.org/10.18494/SAM4715
Published: April 30, 2024

Geometric-angle Optimization of Milling Cutter for Processing Stainless Steel by the Finite Element Method [PDF]

I-Chiang Yang, Hung-Hsiang Huang, Liang-Yu Lu, Lian-Wang Lee, Kuan-Yeh Huang, Te-Jen Su, and Chien-Yu Lu

(Received October 20, 2023; Accepted April 5, 2024)

Keywords: finite element method, thread milling cutter, Taguchi method, back-propagation neural network (BPNN)

In response to the developing trend of technology and the increasing number of difficult-to-machine materials, the geometric angle of the cutting tool is worthy of further study. In metal cutting, the geometric angle of the cutting edge is complicated, and it is difficult to compare different cutting tool geometric angles by mathematical model calculation. Therefore, in traditional tool geometry design, many experiments are required, which is time-consuming and laborious. Compared with traditional methods, simulated milling using the finite element method (FEM) not only saves time and labor, but also materials. Moreover, the repeatability of the experiment is high, and it can accurately obtain difficult-to-measure state variables in cutting experiments. Therefore, in this study, we configured the cutting conditions and tool geometric angles according to the control variates approach, then used the FEM to construct a model of tool geometric angles for the thread milling cutter machining of stainless steel and conducted simulated cutting analysis. The thread milling cutter cutting analysis model is constructed as orthogonal cutting in the simulation. However, milling involves oblique cutting, and the effective rake angle under oblique cutting performs the same function as the rake angle under orthogonal cutting. The effects of cutting conditions and tool geometry on the milling process are simulated and validated, and the accuracy of the milling simulation is confirmed. Finally, a Taguchi orthogonal array is employed to plan tool milling simulation experiments for further analysis and research. The Taguchi method’s analysis of variance is then utilized to identify the optimal parameter combination; then, the second-stage optimization is performed with the back-propagation neural network (BPNN), followed by reversing the grinding angle and grinding the tool. After that, an actual processing experiment is conducted to verify the grinding angle. The experimental results show that the best parameters combination leads to an obvious improvement of the worst parameters combination. Therefore, it is proved that the FEM can be applied to the design and construction of the geometrical angle of the tool and is credible.

Corresponding author: Chien-Yu Lu


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
I-Chiang Yang, Hung-Hsiang Huang, Liang-Yu Lu, Lian-Wang Lee, Kuan-Yeh Huang, Te-Jen Su, and Chien-Yu Lu, Geometric-angle Optimization of Milling Cutter for Processing Stainless Steel by the Finite Element Method, Sens. Mater., Vol. 36, No. 4, 2024, p. 1591-1604.



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