K. Palanikumar

Analysis on Drilling of Glass Fiber–Reinforced Polymer (GFRP) Composites Using Grey Relational Analysis

Glass fiber–reinforced polymer (GFRP) composite materials are an economic alternative to the engineering materials because of their superior properties. The present work focuses on the use of Grey relational analysis for optimizing the drilling parameters on the surface roughness and the thrust force in the drilling of GFRP composites. Taguchis L9, 3-level orthogonal array is used for the experimentation. Optimal machining parameters are determined by the Grey relational grade obtained from the Grey relational analysis for multiperformance characteristics. Experimental results show that the machining performance in the composite machining process can be improved at optimal drilling conditions.

Surface Roughness Analysis in Machining of Titanium Alloy

The use of response surface methodology for minimizing the surface roughness in machining titanium alloy, a topic of current interest, has been discussed in this article. The surface roughness model has been developed in terms of cutting parameters such as cutting speed, feed, and depth of cut. Machining tests have been carried out using CVD (TiN–TiCN–Al2O3–TiN) coated carbide insert under different cutting conditions using Taguchis orthogonal array. The experimental results have been investigated using analysis of variance (ANOVA). The results indicated that the feed rate is the main influencing factor on surface roughness. Surface roughness increased with increasing feed rate, but decreased with increasing cutting speed and depth of cut. The predicted results are fairly close to experimental values and hence, the developed models can be used for prediction satisfactorily.

OPTIMAL MACHINING CONDITIONS FOR TURNING OF PARTICULATE METAL MATRIX COMPOSITES USING TAGUCHI AND RESPONSE SURFACE METHODOLOGIES

The present investigation focuses on the influence of machining parameters on the surface finish obtained in turning of LM25 Al/SiC particulate composites. The experiments are conducted based on Taguchis experimental design technique. In this work, the effect of machining parameters on the surface roughness is evaluated and optimum machining conditions for maximizing the metal removal rate and minimizing the surface roughness are determined using response surface methodology. A second-order response surface model for the surface roughness is developed to predict the surface roughness. The predicted values and measured values are fairly close to each other, which indicates that the developed model can be effectively used to predict the surface roughness on the machining of Al/SiC-MMC composites with 95% confidence intervals within the ranges of parameters studied.

Evaluation of Delamination in Drilling GFRP Composites

Composite materials are used in varieties of applications and are mainly used in structural components. Drilling is one of the important operations in composite structure, often a final operation during assembly. Delaminations in drilling of composite structures are a serious problem and lead to rejection and impose heavy loss. In the present investigation, delamianation associated with drilling has been studied using experimental design approach. Two different cutting tools were tried for experimentation. The experiments were conducted by using high-speed steel-made twist drill and 4-flute cutter. Empirical models were developed for predicting delamination factor in drilling GFRP composites. Regression analysis and analysis of variance (ANOVA) were used for analysis. The adequacies of the developed models have been verified by calculating correlation coefficient. The effect of cutting parameters on drilling such as spindle speed, feed, and their interactions are studied in detail.

Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool

In recent days, carbon fiber reinforced polymer (CFRP) composites play a vital role in various engineering and technological applications. They are replacing conventional materials due to their excellent properties. Tubes made of these materials are made up of either hand layup process or filament winding processes and are widely used in aircraft, automobile, sports industries, etc., The objective of this study is to examine the influence of machining parameters combination so as to obtain a good surface finish in turning of CFRP composite by cubic boron nitride (CBN) cutting tool and to predict the surface roughness values using fuzzy modeling. The results indicate that the fuzzy logic modeling technique can be effectively used for the prediction of surface roughness in machining of CFRP composites.

Evaluation of mechanical and wear properties of hybrid aluminium matrix composites

Abstract Hybrid metal matrix composites are important class of engineering materials used in automotive, aerospace and other applications because of their lower density, higher specific strength, and better physical and mechanical properties compared to pure aluminium. The mechanical and wear properties of hybrid aluminium metal matrix composites were investigated. Mica and SiC ceramic particles were incorporated into Al 356 alloy by stir-casting route. Microstructures of the samples were studied using scanning electron microscope (SEM). The chemical composition was investigated through energy dispersive X-ray (EDX) detector. The results indicate that the better strength and hardness are achieved with Al/10SiC-3mica composites. The increase in mass fraction of mica improves the wear loss of the composites.

Fuzzy Modeling and Analysis of Machining Parameters in Machining Titanium Alloy

Titanium alloys are utilized in many engineering fields such as chemical, industrial, marine, and aerospace due to their unique properties. Machining of these materials causes severe problems. At high temperatures, they become chemically active and tend to react with tool materials. In the present study, fuzzy logic (a tool in artificial intelligence) is used for the prediction of cutting parameters in turning titanium alloy (Ti-6Al-4V). The parameters considered in this study are cutting speed, feed, and the depth of cut. Fuzzy rule-based modeling is employed for prediction of tool flank wear, surface roughness, and specific cutting pressure in machining of titanium alloy. These models can be effectively used to predict the tool flank wear, surface roughness, and specific cutting pressure in machining of titanium alloys. Analysis of the influences of the individual important machining parameters on the responses have been carried out and presented in this study.

Optimization of machining parameters in turning GFRP composites using a carbide (K10) tool based on the taguchi method with fuzzy logics

Glass fiber reinforced polymer (GFRP) composite materials are finding increased applications in a variety of engineering fields. Subsequently, the need for accurate, machining of composites has increased enormously. This paper discusses the application of the Taguchi method with fuzzy logic to optimize the machining parameters for machining of GFRP composites with multiple characteristics. A multi-response performance index (MRPI) was used for optimization. The machining parameters viz., work piece (fiber orientation), cutting speed, feed rate, depth of cut and machining time were optimized with consideration of multiple performance characteristics viz., metal removal rate, tool wear, and surface roughness. The results from confirmation runs indicated that the determined optimal combination of machining parameters improved the performance of the machining process.

Multiple Performance Optimization of Machining Parameters on the Machining of GFRP Composites Using Carbide (K10) Tool

The present investigation focuses on the multiple performance machining characteristics of GFRP composites produced through filament winding. Grey relational analysis was used for the optimization of the machining parameters on machining GFRP composites using carbide (K10) tool. According to the Taguchi quality concept, a L27, 3-level orthogonal array was chosen for the experiments. The machining parameters namely work piece fiber orientation, cutting speed, feed rate, depth of cut and machining time have been optimized based on the multiple performance characteristics including material removal rate, tool wear, surface roughness and specific cutting pressure. Experimental results have shown that machining performance in the composite machining process can be improved effectively by using this approach.

Cutting Parameters Optimization for Surface Roughness in Machining of GFRP Composites using Taguchi’s Method

This article discusses the use of Taguchi’s method and Pareto ANOVA analysis for optimizing the cutting parameters in turning glass fiber reinforced plastic (GFRP) composites using a poly crystalline diamond (PCD) tool for minimizing surface roughness. The cutting parameters evaluated are cutting speed, feed rate, and depth of cut. An L27 orthogonal array, signal to noise ratio, and Pareto ANOVA analysis are used to analyze the effect of cutting parameters and its interactions. The experimental results suggest that the most significant process parameter is feed rate followed by cutting speed. The study shows that the Taguchi method and Pareto ANOVA are suitable for optimizing the cutting parameters with the minimum number of trials.