Multi-Response Optimization of Abrasive Waterjet Cutting on r-GO-Reinforced Fibre Intermetallic Laminates through Moth–Flame Optimization Algorithm

Abstract

Laminated metal-composite structures, also known as fibre metal laminates (FMLs), have emerged as prominent engineering materials in various industries, particularly in the domains of aircraft and automobile manufacturing. These materials are sought after due to their enhanced impact and fatigue resistance capabilities. The machining of FMLs plays a crucial role in achieving near-net shapes for the purpose of joining and assembling components. Delamination is a prevalent issue encountered during the process of conventional machining, thus rendering FMLs are challenging materials to machine. This study aims to investigate the cutting process of novel fibre intermetallic laminates (FILs) using the abrasive water jet (AWJ) cutting technique. The FILs consists of carbon and aramid fibers that are adhesively bonded with a resin matrix filled with reduced graphene oxide (r-GO) nano fillers. Moreover, these laminates contain embedded Nitinol shape memory alloy sheets as the skin materials. Specifically, the study aims to investigate the impact of different factors, such as the addition of reduced graphene oxide (r-GO) in the laminates (ranging from 0 to 2 wt%), traverse speed (ranging from 400 to 600 mm/min), waterjet pressure (ranging from 200 to 300 MPa), and nozzle height (ranging from 2 to 4 mm), on the material removal rate (MRR), delamination factor (FD), and kerf deviation (KD). ANOVA was used in the statistical analysis to determine the most influential parameters and their effects on the selected responses. The optimal AWJC parameters are determined using a metaheuristic-based moth–flame optimization (MFO) algorithm in order to enhance cut quality. The efficacy of MFO is subsequently compared with similar well-established metaheuristics such as the genetic algorithm, particle swarm algorithm, dragonfly algorithm, and grey-wolf algorithm. MFO was found to outperform in terms of several performance indices, including rapid divergence, diversity, spacing, and hypervolume values, among the algorithms compared.