Abstract

The anisotropic and heterogeneous properties of glass fiber-reinforced plastic (GFRP) composites lead to a challenging machining process. The end milling process of these materials generates excessive cutting force that leads to several undesirable damages such as high surface roughness and delamination. Therefore, it is necessary to model the cutting force during the end milling process of GFRP composites materials to obtain an accurate prediction of cutting force. End milling process parameters, i.e., depth of cut (Aa), feeding speed (Vf), and spindle speed (n) are used as an input parameter and each has three levels. Hence, a randomized full factorial 3 × 3 × 3 is applied as the design of experiments. On the other hand, the cutting force (Fc) was used as an output parameter. In this study, an adaptive network-based fuzzy inference system (ANFIS) method is applied to model the cutting force during the end milling process of GFRP composites.

Original languageEnglish
Title of host publicationInnovative Science and Technology in Mechanical Engineering for Industry 4.0
Subtitle of host publicationProceedings of the 4th International Conference on Mechanical Engineering, ICOME 2019
EditorsVivien Djanali, Fahmi Mubarok, Bambang Pramujati, Suwarno
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735419346
DOIs
Publication statusPublished - 10 Dec 2019
Event4th International Conference on Mechanical Engineering: Innovative Science and Technology in Mechanical Engineering for Industry 4.0, ICOME 2019 - Yogyakarta, Indonesia
Duration: 28 Aug 201929 Aug 2019

Publication series

NameAIP Conference Proceedings
Volume2187
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference4th International Conference on Mechanical Engineering: Innovative Science and Technology in Mechanical Engineering for Industry 4.0, ICOME 2019
Country/TerritoryIndonesia
CityYogyakarta
Period28/08/1929/08/19

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