Optimization of CNC Turning Parameters with Carbide Tool for Surface Roughness Analysis Using Taguchi Analysis

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Parameter optimization in milling of glass fiber reinforced plastic (GFRP) using DOE-Taguchi method

Introduction: Optimization of machining parameters is essential for improving expected outcome of any machining operation. Case Description: The aim of this work is to find out optimum values of machining parameters to achieve minimal surface roughness during milling operation of GFRP. Discussion and Evaluation: In this machining operation speed, depth of cut and feed rate are considered as parameters affecting surface roughness and Design of Experiment (DOE)-Taguchi method tool is used to plan experiments and analyse results. Conclusion: Analysis of experimental results presents optimum values of these three parameters to achieve minimal surface roughness with speed as a major contributing factor. Speed—200 rpm, depth of cut—1.2 mm and feed—40 mm/min are an optimal combination of machining parameter to produce minimal surface roughness during milling of GFRP

Machinability study of Carbon Fiber Reinforced Polymer in the longitudinal and transverse direction and optimization of process parameters using PSO–GSA

Carbon Fiber Reinforced Polymer (CFRP) composites are widely used in aerospace industry in lieu of its high strength to weight ratio. This study is an attempt to evaluate the machinability of Bi-Directional Carbon Fiber–Epoxy composite and optimize the process parameters of cutting speed, feed rate and drill tool material. Machining trials were carried using drill bits made of high speed steel, TiN and TiAlN at different cutting speeds and feed rates. Output parameters of thrust force and torque were monitored using Kistler multicomponent dynamometer 9257B and vibrations occurring during machining normal to the work surface were measured by a vibration sensor (Dytran 3055B). Linear regression analysis was carried out by using Response Surface Methodology (RSM), to correlate the input and output parameters in drilling of the composite in the longitudinal and transverse directions. The optimization of process parameters were attempted using Genetic Algorithm (GA) and Particle Swarm Optimization–Gravitational Search Algorithm (PSO–GSA) techniques

Multi-performance optimization of micro-drilling using Taguchi technique based on membership function

383-390Carbon fibre reinforced polymers (CFRP) tends to be employed in the manufacture of aerospace industry, construction and costly sports cars, wherever robust and lightweight materials are needed. The present aims to evaluate and optimize the micro-drilling machining process on carbon fibre reinforced polymer composite material in terms of multiple performance characteristics of circularity and cylindricity errors. Micro-drilling tests based on Taguchi L27 orthogonal array are carried out on CFRP laminates under varying cutting conditions of spindle speed, feed rate and drill diameters. The process outcomes are assessed in-terms of circularity and cylindricity errors. The influence and percentage contribution of process parameters are examined by analysis of variance (ANOVA). Taguchi methodology supported by membership function is used for the optimization of the multiple responses transformed to the output of signal-to-noise (S/N) ratio. The scope of the experimental design is to minimize circularity and cylindricity errors simultaneously. Confirmation tests are performed to verify the effectiveness of the proposed hybrid approach

Optimization of Drilling Process Parameters Via Taguchi, TOPSIS and RSA Techniques

Carbon Fiber Reinforced Polymer (CFRP) is the most preferred composite material due to its high strength, high modulus, corrosion resistance and rigidity and which has wide applications in aerospace engineering, automobile sector, sports instrumentation, light trucks, airframes. This paper is an attempt to carry out drilling experiments as per Taguchi’s L27 (313) orthogonal array on CFRP under dry condition with three different drill bit type (HSS, TiAlN and TiN). In this research work Response Surface Analysis (RSA) is used to correlate the effect of process parameters (cutting speed and feed rate) on thrust force, torque, vibration and surface roughness. This paper also focuses on determining the optimum combination of input process parameter and the drill bit type that produces quality holes in CFRP composite laminate using Multi-objective Taguchi technique and TOPSIS. The percentage of contribution, influence of process parameters and adequacy of the second order regression model is carried out by analysis of variance (ANOVA). The results of experimental investigation demonstrates that feed rate is the pre-dominate factor which affects the response variables

Multi-response Optimization in Drilling of Carbon Fiber Reinforced Polymer Using Artificial Neural Network Correlated to Meta-heuristics Algorithm

AbstractThis paper aims to optimize the drilling process parameters using artificial neural network (ANN) linked with the most popular meta-heuristics technique such as Hybrid Particle Swarm Optimization Gravitational Search Algorithm (PSOGSA) and Genetic Algorithm (GA). An aerospace grade T300 Carbon Fiber-Epoxy composite laminate of 8 mm thick was made of T300 Polyacrylonitrile (PAN) based Carbon Fiber and two part Epoxy resin was use for this study. The Carbon Fiber used is Bi-directional (BD) with a ply thickness of 0.25 mm and lay-up sequence of [60/90/0/90/90/60/0/60/60/60/60/45/90/90/0/45/60/90/60]. Drilling experiments were conducted on a composite laminate by varying the cutting speed (30, 40 and 50 m/min), feed rate (0.025, 0.05 and 0.1 mm/rev) and drill bit type (HSS, TiAlN and TiN). The experimental results in the form of thrust force, torque and surface roughness obtained are correlated with process parameters through artificial neural network (ANN) and optimized by PSOGSA and GA. The optimization results indicates that the proposed hybrid PSOGSA performances much better than the GA