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Monitoring of Tool Wear and Surface Roughness Using ANFIS Method During CNC Turning of CFRP Composite.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Monitoring of Tool Wear and Surface Roughness Using ANFIS Method During CNC Turning of CFRP Composite./
作者:	Islam, Mofakkirul.
面頁冊數:	1 online resource (116 pages)
附註:	Source: Masters Abstracts International, Volume: 84-02.
Contained By:	Masters Abstracts International84-02.
標題:	Engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798841723011

Monitoring of Tool Wear and Surface Roughness Using ANFIS Method During CNC Turning of CFRP Composite.
Islam, Mofakkirul.

Monitoring of Tool Wear and Surface Roughness Using ANFIS Method During CNC Turning of CFRP Composite. - 1 online resource (116 pages)

Source: Masters Abstracts International, Volume: 84-02.

Thesis (M.S.E.)--The University of Texas Rio Grande Valley, 2022.

Includes bibliographical references

Islam, Md Mofakkirul, Prediction of Tool Wear and Surface Finish using ANFIS modeling during CNC turning of CFRP composites Master of Science in Engineering (MSE). May, 2022, 93 pp, 58 figures, 9 tables, references, 72 titles. Carbon fiber-reinforced plastic (CFRP) is gaining wide acceptance in areas including sports, aerospace and automobile industry . Because of its superior mechanical qualities and lower weight than metals, it needs effective and efficient machining methods. In this study, the relationship between the cutting parameters (Speed, Feed, Depth of Cut) and response parameters (Vibration, Surface Finish, Cutting Force and Tool Wear) are investigated for CFRP composite. For machining of CFRP, CNC turning operation with coated carbide tool is used. An ANFIS model with two MISO system has been developed to predict the tool wear and surface finish. Speed, feed, depth of cut, vibration and cutting force have been used as input parameters and tool wear and surface finish have been used as output parameter. Three sets of cutting parameter have been used to gather the data points for continuous turning of CFRP composite. The model merged fuzzy inference modeling with artificial neural network learning abilities, and a set of rules is constructed directly from experimental data. However, Design of Experiments (DOE) confirmation of this experiment fails because of multi-collinearity problem in the dataset and insufficient experimental data points to predict the tool wear and surface roughness effectively using ANFIS methodology. Therefore, the result of this experiment do not provide a proper representation, and result in a failure to conform to a correct DOE approach.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798841723011Subjects--Topical Terms:

561152
Engineering.
Subjects--Index Terms:

Adaptive-network-based fuzzy inference systemIndex Terms--Genre/Form:

554714
Electronic books.

Monitoring of Tool Wear and Surface Roughness Using ANFIS Method During CNC Turning of CFRP Composite.
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