Tool materials influence on surface roughness and oversize in machining glass fiber reinforced polypropylene (GFR-PP) composites

Glass fiber reinforced Polypropylene (GFR-PP) is used in manufacturing industries like bicycles, auto bodies, aircraft, and civil applications due to superior properties. Machining of fiber-reinforced plastics is problematic especially when drilling due to their inherent in-homogeneity, anisotropy and limited plastic deformation. Drilling is often required to facilitate the assembly of the parts to get the final products. Surface quality in drilled composites is an essential design characteristic in many situations, such as accurate fits, aesthetic requirements, etc. The present work deals with detailed investigation on the influence of tool materials and machining parameters during drilling of GFR-PP composite material. The study mainly focused on machined surface quality such as surface roughness of the drilled hole and dimensional inaccuracies such as oversize of the hole. The better dimensional and surface quality of drilled hole is observed from solid carbide drill with a spindle speed of 2500?rpm and a feed rate of 0.05?mm/rev. Regression model is developed using experimental data for estimating the surface roughness and oversize. The developed model has high R-sq value which shows the strong relationship between the model and the response variables. The effect of drilling process parameters and associated interactions are discussed in detail.     

Sustainable Drilling of Nano SiC Reinforced Al Matrix Composites Using MQL and Cryogenic Cooling for Achieving the Better Surface Integrity

Silicon - Metal Matrix Nano Composites (MMNCs) are progressive alternatives of formal metal materials, are presently accomplishing a growing inclination of research and engineering approaches for...     

Experimental Investigation on the Performance of a Solar Still Using SiO2 Nanoparticles /Jatropha curcas L

Silicon - Now, enticing systematic civic since everywhere the world is used in green synthesis and benefit of the simple is eco-friendly with an emergent method of producing nanoparticles (NPs)....     

Synthesis and characterization of magnetic and fluorescent styrene co‐polymer nanofiber

F luorescent and magnetic poly(styrene) (PS) based random co‐polymer nanofiber was synthesized in a controlled manner via chemical polymerization in three steps. A fluorescent and magnetic nanohybrid {Fe₃O₄/Congored (CR)} was separately prepared and chemically grafted onto the epichlorohydrin (ECH) units of the random co‐polymer. Characterizations of the above synthesized polymers were done with the help of Fourier transform infrared (FTIR) spectroscopy, UV–visible spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, fluorescence emission spectroscopy, field emission scanning electron microscopy (FESEM), scanning electron microscopy (SEM), vibrating sample magnetometer (VSM) measurement, differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and gel permeation chromatography (GPC) like analytical techniques. The FESEM results indicated that after the grafting of nanohybrid onto the random co‐polymer backbone, the polymer exhibited a nanofiber like morphology. Due to the surface functionalization and encapsulation reactions, the magnetic moment value of the nanohybrid and its nanocomposites were found to be reduced. Synthesis and characterization of magnetic and fluorescent random co‐polymer based nanofiber is the primary target of the present investigation and its application is extended to the catalysis field. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42796.     

Experimental investigation and optimisation in drilling of GFRP composites

Glass fibre-reinforced polymer (GFRP) composite materials are one of the important materials and are economic alternative to engineering materials because of their superior properties. This paper presents an effective approach for the optimisation of drilling parameters with multiple performance characteristics based on the Tagugch’s method with grey relational analysis. Taguchi’s L₁₆, 4-level orthogonal array has been used for the experimentation. The drilling parameters such as spindle speed and feed rate are optimised with consideration of multiple performance characteristics, such as thrust force, workpiece surface roughness and delamination factor. Response table and response graph are used for the analysis. The analysis of grey relational grade indicates that feed rate is the more influential parameter than spindle speed. The results indicate that the performance of drilling process can be improved effectively through this approach.     

Nanoclay Addition and Core Materials Effect on Impact and Damage Tolerance Capability of Glass Fiber Skin Sandwich Laminates

This study explores the effects of modified (OMMT) nanoclay and core material on low velocity impact behavior and damage tolerance capability of glass fiber reinforced (FRP) polyester resin – polystyrene foam (PS) sandwich laminates. The FRP and sandwich laminates are prepared by a compression molding technique for investigation. Low velocity impacts are carried out on all the fabricated laminates by using a instrumented drop weight impact tower with the energy level of 30 J and load–energy–time plots were recorded using data acquisition software. Post impact flexural tests have been conducted to evaluate the damage tolerance capability of the fabricated composite laminates. X-ray Diffraction (XRD) results have been obtained for the samples, where the nanoclay has indicated that intergallery spacing of the layered clay increases with the matrix. Scanning Electron Microscopy (SEM) has given the morphological picture of the nanoclay dispersion in the polymer fracture samples. The results of the study show that the impact properties and damage tolerance capability of the 4% nanoclay polyester sandwich have been greatly increased.     

Optimizing the Machining Parameters for Minimum Surface Roughness in Turning of GFRP Composites Using Design of Experiments

In recent years, glass fiber reinforced plastics (GFRP) are being extensively used in variety of engineering applications in many different fields such as aerospace, oil, gas and process industries. However, the users of FRP are facing difficulties to machine it, because of fiber delamination, fiber pull out, short tool life, matrix debonding, burning and formation of powder like chips. The present investigation focuses on the optimization of machining parameters for surface roughness of glass fiber reinforced plastics (GFRP) using design of experiments (DoE). The machining parameters considered were speed, feed, depth of cut and workpiece (fiber orientation). An attempt was made to analyse the influence of factors and their interactions during machining. The results of the present study gives the optimal combination of machining parameters and this will help to improve the machining requirements of GFRP composites.     

Application of grey fuzzy logic for the optimization of drilling parameters for CFRP composites with multiple performance characteristics

Carbon Fibre Reinforced Plastic (CFRP) composite materials have potential applications in various domains. In machining, drilling is essentially required to join different structures. But CFRP drilling poses many problems that decrease the quality of holes. In this paper, Taguchi’s L₂₇ orthogonal array is used to perform drilling of CFRP composite plates. To improve the quality of the holes drilled, the optimal combination of drilling parameters is chosen using grey relational analysis. Grey fuzzy optimization of drilling parameters is based on five different output performance characteristics, namely, thrust force, torque, entry delamination, exit delamination and eccentricity of the holes. Analysis of variance (ANOVA) is used to find the percentage contribution of the drilling parameters and found that feed rate is the most influential factor in drilling of CFRP composites.     

Application of the central composite design in optimization of machining parameters in drilling hybrid metal matrix composites

In this study, the application of response surface methodology (RSM) and central composite design (CCD) for modeling, optimization, and an analysis of the influences of dominant machining parameters on thrust force, surface roughness and burr height in the drilling of hybrid metal matrix composites produced through stir casting route. Experiments are carried out using Al 356-aluminum alloy reinforced with silicon carbide of size 25 μm and Mica of size 45 μm. Drilling test is carried out using carbide drill of 6 mm diameter. The design of experiment concept has been used to optimize the experimental conditions. The experimental data are collected based on a three-factor-three-level full central composite design. The multiple regression analysis using RSM is used to establish the input–output relationships of the process. The mathematical models are developed and tested for adequacy using analysis of variance and other adequacy measures using the developed models. The main and interaction effect of the input variables on the predicted responses are investigated. The predicted values and measured values are fairly close, which indicate that the developed models can be effectively used to predict the responses in the drilling of hybrid metal matrix composites. The optimized drilling process parameters have been obtained by numerical optimization using RSM by ensuring the minimum thrust force of 84 N, surface roughness of 1.67 μm, and the burr height of 0.16 mm. After the drilling experiments, a scanning electron microscope (SEM) is used to investigate the machined surface and tool wear.     

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 Taguchi's 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.