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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/11942
Title: Neural network approach for estimating the residual tensile strength after drilling in uni-directional glass fiber reinforced plastic laminates
Authors: Mishra R.
Malik J.
Singh I.
Davim J.P.
Published in: Materials and Design
Abstract: The drilling of fiber reinforced plastics (FRP) often results in damage around the drilled hole. The drilling induced damage often serves to impair the long-term performance of the composite products with drilled holes. The present research investigation focuses on developing a predictive model for the residual tensile strength of uni-directional glass fiber reinforced plastic (UD-GFRP) laminates with drilled hole which has not been developed worldwide till now. Artificial neural network (ANN) predictive approach has been used. The drill point geometry, the feed rate and the spindle speed have been used as the input variables and the residual tensile strength as the output. The results of the predictive model are in close agreement with the training and the testing data. © 2010 Elsevier Ltd. All rights reserved.
Citation: Materials and Design (2010), 31(6): 2790-2795
URI: https://doi.org/10.1016/j.matdes.2010.01.011
http://repository.iitr.ac.in/handle/123456789/11942
Issue Date: 2010
Keywords: A. Glass fiber reinforced epoxy composites
C. Drilling
E. Residual tensile strength
ISSN: 2641275
Author Scopus IDs: 57199009739
53463705200
56375560400
6701555575
Author Affiliations: Mishra, R., Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, 247 667, India
Malik, J., Department of Electronics and Computer Engineering, Indian Institute of Technology Roorkee, Roorkee, 247 667, India
Singh, I., Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, 247 667, India
Davim, J.P., Department of Mechanical Engineering, University of Aveiro, Campus Santiago, Aveiro 3810193, Portugal
Corresponding Author: Mishra, R.; Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, 247 667, India; email: roshan.mishra87@gmail.com
Appears in Collections:Journal Publications [ME]

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