USING ADDITIVE RATIO ASSESSMENT AND ARTIFICIAL BEE COLONY (ARAS-ABC) OPTIMIZATION ALGORITHM DURING DRILLING OF CARBON NANOMATERIAL/GLASS FIBER-REINFORCED POLYMER LAMINATED COMPOSITES

Kuldeep Kumar () and Rajesh Kumar Verma
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Kuldeep Kumar: Materials & Morphology Laboratory, Mechanical Engineering Department, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh 273010, India
Rajesh Kumar Verma: ��Department of Mechanical Engineering, School of Engineering Harcourt Butler Technical University, Kanpur, Uttar Pradesh 208002, India

Surface Review and Letters (SRL), 2023, vol. 30, issue 11, 1-20

Abstract: In manufacturing industries, polymers are widely used due to their exceptional physiochemical and mechanical characteristics. It consists of high strength, low weight, corrosive resistivity, and ease of fabrication. Glass fiber is more cost-effective and easily available than other fibers such as carbon, aramid and kevlar. The most challenging issue for the manufacturer in the laminated polymer is the non-homogeneity and anisotropic behavior. This nature also hinders the machining performance of laminated polymer composites, which are entirely different from metals and their alloys. The supplements of nanomaterials enhanced the physiomechanical properties and the machining efficiency of fiber laminates. This work highlights the machining (drilling) aspect of glass fiber-reinforced polymer composites modified by multiwall carbon nanotube (MWCNT). The effect of drilling factors such as spindle speed (S), feed rate (F), and MWCNT weight percent (wt.%) on machining responses such as Thrust force (Th), Torque (T), and Surface roughness (SR) has been investigated. The drilling operations were conducted using the 5 mm diameter TiAN (Sic coated) according to the response surface methodology (RSM) design. The process constraints were controlled by the hybrid module of additive ratio assessment (ARAS) and the Artificial Bee Colony (ABC) algorithm. The nature-inspired principles of the bee are used to optimize the objective function. The multiple responses were aggregated using the ARAS method, and its objective function is fed into the ABC algorithm. It was remarked that the hybrid ARAS-ABC is more capable than the traditional ARAS, with an overall improvement of 7.33% in assessment values. The scanning electron microscopy (SEM) test confirms the feasibility of the proposed hybrid (ARAS-ABC) module to achieve a favorable machining environment while drilling modified nanocomposites.

Keywords: Nanocomposites; GFRP; drilling; MWCNT; ARAS; bee (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218625X23500749

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