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Linen Fabric-Jute Fabric-Fumed Silica-Epoxy Sandwich Laminate: AWJ Machining and Multi-Response Optimisation

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Abstract

Since fabric-polymer laminates are being used in several engineering applications, it is imperative to compute and analyse the performance of any novel fabric reinforced polymer laminates while machining. Given these perspectives, an endeavor has been made to formulate a polymer sandwich laminate by incorporating industrial by-product & natural fibres and to machine the sandwich laminate using Abrasive Water Jet (AWJ) machining. For enhancing the machining process, a multi-objective optimisation is employed. In this study 2 wt.% fumed silica added epoxy reinforced with linen fabric (3 layers) & jute fabric (2 layers) sandwich laminate is machined using AWJ machining and the optimal combination of machining parameters are predicted using Taguchi-grey relational technique for multi-response characteristics such as Material Removal Rate (MRR) and Kerf Inclination (KI). For multi-response optimisation the traverse speed, the standoff distance, and the abrasive flow rate are considered as input machining parameters. In AWJ machining, the MRR increases with traverse speed and abrasive flow rate. Whereas, passing of higher abrasive particles at higher traverse speed causes reduction in kerf inclination. Using Taguchi-grey relational analysis (GRA), the optimal combination of AWJ machining process parameters for maximum MRR and minimum kerf inclination is attained. For determining the greatest significant parameter in optimisation, ANOVA is carried out. The standoff distance has the highest influence on multi performance characteristics of AWJ machining of laminate followed by traverse speed and abrasive flow rate. The confirmation test revealed that the grey relation grade value has improved by 1.81% from predicted grey relation grade value.

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Correspondence to C. K. Arvinda Pandian.

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Pandian, C.K.A., Jailani, H.S. Linen Fabric-Jute Fabric-Fumed Silica-Epoxy Sandwich Laminate: AWJ Machining and Multi-Response Optimisation. Silicon 13, 1239–1248 (2021). https://doi.org/10.1007/s12633-020-00515-0

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