Mechanical Sciences
Mechanical Sciences
Mechanical Sciences
Articles | Volume 10, issue 1
Articles | Volume 10, issue 1
https://doi.org/10.5194/ms-10-243-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ms-10-243-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 1
Research article
 | 
14 Jun 2019
Research article |  | 14 Jun 2019

Estimation of tool life and cutting burr in high speed milling of the compacted graphite iron by DE based adaptive neuro-fuzzy inference system

Longhua Xu, Chuanzhen Huang, Rui Su, Hongtao Zhu, Hanlian Liu, Yue Liu, Chengwu Li, and Jun Wang

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Latest update: 23 Nov 2024
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Short summary
We solve the issues based on the ANFIS with DE. It indicates that once the cutting parameters are determined, we can give better predictions of tool life and heights of cutting burrs compared with other models. We redesigned the ANFIS model and this model was optimized with new learning algorithm called DE. This model can export two outputs at the same time. Based on the ANOVA, the results show that the most effect on the tool life and height of cutting burrs are cutting speed and feed rate.
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