Soft computing techniques during drilling of bi-directional carbon fiber reinforced composite

Shetty, Nagaraj and Herbert, Mervin A and Shetty, Raviraj and Shetty, Divakara S and Vijay, G S (2016) Soft computing techniques during drilling of bi-directional carbon fiber reinforced composite. Applied Soft Computing, 41 (41). pp. 466-478. ISSN 1568-4946

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Abstract

tDue to the intricacy of machining processes and inconsistency in material properties, analytical mod-els are often unable to describe the mechanics of machining of carbon fiber reinforced polymer (CFRP)composites. Recently, soft computing techniques are used as alternate modeling and analyzing meth-ods, which are usually robust and capable of yielding comprehensive, precise, and unswerving solutions.In this paper, drilling experiments as per the Taguchi L27experimental layout are carried out on bi-directional carbon fiber reinforced polymer (BD CFRP) composite laminates using three types of drillingtools: high speed steel (HSS), uncoated solid carbide (USC) and titanium nitride coated SC (TiN-SC). Thefocus of this work is to determine the best drilling tool that produces good quality drilled holes in BDCFRP composite laminates. This paper proposes a novel prediction model ‘genetic algorithm optimisedmulti-layer perceptron neural network’ (GA-MLPNN) in which genetic algorithm (GA) is integrated withMulti-Layer Perceptron Neural Network. The performance capability of response surface methodology(RSM) and GA-MLPNN in prediction of thrust force is investigated. RSM is also used to evaluate the influ-ence of process parameters (spindle speed, feed rate, point angle and drill diameter) on thrust force. GAis used to optimize the thrust force and its optimization performance is compared with that of RSM. Itis observed that the GA-MLPNN is better predicting tool than the RSM model. The investigation in thispaper demonstrates that TiN-SC is the best tool for drilling BD CFRP composite laminates as minimumthrust force is developed during its use

Item Type: Article
Uncontrolled Keywords: DrillingCarbon fiber reinforced polymercompositesResponse surface methodologyArtificial Neural NetworkGenetic AlgorithmMulti-objective optimizationa
Subjects: Engineering > MIT Manipal > Mechanical and Manufacturing
Depositing User: MIT Library
Date Deposited: 24 Mar 2016 15:28
Last Modified: 24 Mar 2016 15:30
URI: http://eprints.manipal.edu/id/eprint/145642

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