Prediction of Core Shear Strength in Sandwich Composites using Deep Learning and Support Vector Regression
Antony P. J1, Prajna M. R2, Jnanesh N. A.3
1Antony P J, Department of CSE, AJEIT Mangalore, Karnataka, India.
2Prajna M R, Department of CSE, KVGCE Sullia, Karnataka, India.
3Jnanesh N A, Department of Mech, KVGCE Sullia, Karnataka, India.
Manuscript received on 05 March 2019 | Revised Manuscript received on 12 March 2019 | Manuscript published on 30 July 2019 | PP: 2499-2505 | Volume-8 Issue-2, July 2019 | Retrieval Number: A1971058119/19©BEIESP | DOI: 10.35940/ijrte.A1971.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the present study, machine learning approaches have been developed to predict the 180 days aged core shear strength of sandwich composites. The characteristics of the sandwich composites depends on the number of factors namely fibre type i.e., Chopped strand Mat, Stitched, Chopped strand Mat and Woven Roving, core density, bond between the core and the face sheets and the ability to bear the load in flexural mode. In the current approach deep learning and SVR models were worked out by taking on six different parameters namely foam density, aging temperature and variety of fiber types as input variables. For each set of these input variables, the 180 days aged shear strength of sandwich composites with a test frequency of 30 days was determined. The paper aims at predicting the core shear strength value of stitch bond sandwich composites using other three aforementioned fibers. To create the model and confirm the accuracy of the algorithm training and test data are considered. The results obtained revealed that the deep learning model developed provides better predictive ability than the model of SVR.
Index Terms: Sandwich Composites; Core Shear Strength; Stitch Bond Mat; Support Vector Regression, Deep Learning.
Scope of the Article: Deep Learning