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Nowadays, the metal cutting industry is focussed on high production rate, better quality and good economic rate. In the boring process, the tool holder is subjected to excessive cutting force and surface roughness which affects the finished product. This can be avoided by using Multilayer Nano metal oxide coated tools which has a positive impact in improving cutting performance and coating was done in tool insert by dip, spin and spray technique for dry, semisolid and wet condition. In this paper, Aluminium oxide + zirconium oxide, titanium oxide + zirconium oxide and zirconium oxide were used as coating material. A 27 run cutting experiment was designed and cutting force and surface roughness was measured. Experimental results were optimized using Taguchi technique and from the results it was observed that two layered TiO2 +ZrO2 deposited in series provide a better cutting enactment against the Al2O3 +ZrO2 and ZrO2

A combination of Taguchi methodology and Grey Relational Analysis (GRA) inturn coupled with principal component analysis (PCA) has been proposed through this paper. This methodology is adopted in order to evaluate and estimate the effect of machining parameters over the output responses of Wire Electrical-Discharge Machining (WEDM) performed on Magnesium based metal matrix composite. In this research, an optimal combination of process parameter was expected to be finalized so as to attain a state of maximum Material Removal Rate (MRR) that too with minimal surface roughness (Ra) value. WEDM of developed composite specimens were confirmed to be of L27 orthogonal array(OA) using Taguchi’s method, based mainly on control factors namely reinforcement weight percentage (wt.%), Doping (DP %), Pulse ON time (T-ON), Pulse OFF time (T-OFF) and wire feed rate (WF). ANOVA outcome reveals that wt.% and DP% are the most influencing parameter for MRR and Ra. Multiobjective responses were normalized using GRA; further PCA was applied to evaluate the weighting values corresponding to each performance. The optimial parameter was set and the final results obtained depending on the optimal combination was found to be with a maximum MRR of 14.9 mm3/min and a minimum Ra of 2.04 μm.

Taguchi coupled grey relation analysis is adopted to study the effects of machining parameters of Wire Electrical-Discharge Machining (WEDM) on magnesium metal matrix composite reinforced with Reduced Graphene Oxide(r-GO). An optimal combination of process parameter was expected to be finalized in this research to attain maximum Material Removal Rate (MRR) with a minimal surface roughness (Ra) value. The composite was fabricated through solvent based powder metallurgy route for varying percentage of r-GO. Experimental combination for WEDM of developed composite specimens were finalized to be L27 orthogonal array (OA) using Taguchi's method mainly based on the control factors namely reinforcement weight percentage (wt.%), pulse ON time (P1), pulse OFF time (P2) and wire feed rate (Wfd). ANOVA results revealed that wt.% and P1 are the most influencing parameters for MRR and Ra. Outputs scaleup that the developed regression model augments well with the experimental values. Using grey relation analysis (GRA) the optimal parameter was set and the final results obtained based on the optimal combination was found to be with a maximum MRR (18.38 mm3/min) and minimum Ra (3.29 μm). Traces of intermetallic formation are identified over the machined surface due to the action of machining parameters.

In the present study, wear and friction behaviour of Magnesium(Mg) Metal Matrix Composite(MMC) reinforced with Silicon carbide(SiC) doped reduced graphene oxide (r-GO) nanosheets is carried over. In addition, a mathematical model is developed to predict the influence of various control factors of the Mg composites fabricated through Solvent-based powder metallurgy process. Herein SiC is doped with varying wt. % (10, 20, 30) into r-GO nanosheets and its effect over dry sliding wear is studied at constant control parameters like that of load (10N), sliding distance (1000m) and sliding velocity (1 m/s). Optimal parameter for specific wear rate (SWR) is attained by Taguchi method and the mathematical model was developed using Artificial Neural Network in order to understand the wear behaviour of developed MMC under varying parametric condition. Analysis of variance result reveals that wt.% of r-GO have major influence on SWR and sliding velocity have least effect. Occurrence of delamination wear could also be notified over the worn out surface.

In this research effect of tin on pure magnesium based on the various mechanical, electrical and thermal properties were investigated and analysed based on certain effective mathematical models already established. The models exploited in this work involve Voigt, Reuss method from Rule of Mixture, Paul method, Maxwell method and P.G. Klemens method. The tensile strength and density values were found increasing with respect to the increment in the alloying percentage of tin while the yield strength decreased. However, any of the developed models was found not to be efficient to provide the values for electrical conductivity during validation.

In this research, a copper based surface composite was fabricated through dispersing hybrid composite particles onto its surface through friction stir processing (FSP) technique. Optical micrograph...

The exploitation of artificial neural network as a computational technique in predicting the impact energy of cast duplex stainless steels based on its chemical composition is reported in this research work. Two hundred and twenty melts of duplex stainless steel of different compositions were casted, heat-treated and tested for Charpy impact test. A multilayer feed forward ANN model was developed based on 75% of the available chemical compositions of duplex stainless steel as input and impact energy in joules as output. The prediction efficiency of the developed models was calculated based on mean absolute error and mean absolute percentage error; the best model thus sorted out was validated and tested. A multilayer feed forward ANN model with two hidden layers was selected which provided better linear correlation between the chemical composition and impact energy. Correlation performance of considered ANN model with network topology expressed in terms of mean absolute percent error was found to be 0.43% with a correlation coefficient value of 0.95714. Testing and evaluation of the developed model proved to be efficient enough for the development of duplex stainless steels with required impact toughness.

This work focuses on characterization of a novel hybrid Aluminum Metal Matrix Composite (AMMC) developed through two-step compocasting method by reinforcing constant amount of flyash cenosphere (10%) and varying quantity of graphite (2%, 4% and 6%). Morphological analysis result shows the presence of dendritic arms and homogeneous distribution of the cenosphere and graphite in Al7075 matrix. The hardness and tensile strength upturn with addition of cenosphere and vice versa for the addition of graphite. The reinforcement particles have enhanced the tensile strength of Aluminum matrix from 178 N/mm2 to 213 N/mm2 as of the composite is concerned. Addition of cenosphere improves the wear resistance considerably and wear rate decreases further with graphite addition due to its self-lubricating nature. Machinability (turning) characteristics of the developed hybrid composite were studied in detail besides optimizing the machining parameters employing Artificial Neural Network (ANN) technique. Based on the ANOVA results it was found that cutting speed and % of graphite addition have the major contribution in minimizing the surface roughness of developed composite.

A systematic view on evaluating the machining characteristics of Wire Cut Electrical Discharge Machining (WEDM) employing Taguchi Method and Grey Relational Analysis based multiobjective optimization is provided in this research article. The outcome of various WEDM processing parameters including pulse discharge on time (PulseON), pulse discharge off time (PulseOFF), wire feed rate (WireFR) along with the material characteristics of varying Boron Nitride (BN) volume fractions while machining a friction stir processed (FSPed) copper-BN surface composite was investigated. The output responses considered in this research include Material Removal Rate (MRR) and Surface roughness (Ra) that was obtained from the L27 orthogonal array based on the above said input factors. ANOVA was performed, and PulseON and BN volume fraction were found most significant for MRR, while PulseON and PulseOFF influence the most in attaining minimal Ra values. Based on the obtained experimental values for MRR and Ra, a mathe...

The main aim of this investigation focuses on fabrication of copper surface composites through friction stir processing (FSP) reinforced with boron nitride (BN) particles of varying volume fractions (5%, 10%, and 15%). Surface composites developed through single pass FSP were characterized for its microstructural, mechanical and tribological properties. Microstructural characterization indicated that developed surface composites were of good quality with reduced grain size and the SEM characterization confirmed good bonding between copper matrix and BN with uniform dispersion. Micro hardness survey of the developed surface composites showcased minimal deviation in the stir zone with increased trend in respect to the volume fraction of BN. The ultimate tensile strength, yield strength and percent elongation of FSPed composites was found to have reduced when compared with that of pure copper. BN dispersion in surface composite was effective in reducing the ductility and so maximum volume percent (15...

The wear behavior of multi-walled carbon nano-tubes (MWCNTs) reinforced copper metal matrix composites (MMCs) processed through powder metallurgy (PM) route was focused on and further investigated for varying MWCNT quantity via experimental, statistical and artificial neural network (ANN) techniques. Microhardness increases with increment in MWCNT quantity. Wear loss against varying load and sliding distance was analyzed as per L16 orthogonal array using a pin-on-disc tribometer. Process parameter optimization by Taguchi's method revealed that wear loss was affected to a greater extent by the introduction of MWCNT; this wear resistant property of newer composite was further analyzed and confirmed through analysis of variance (ANOVA). MWCNT content (76.48%) is the most influencing factor on wear loss followed by applied load (12.18%) and sliding distance (9.91%). ANN model simulations for varying hidden nodes were tried out and the model yielding lower MAE value with 3-7-1 network topology is identified to be reliable. ANN model predictions with R value of 99.5% which highly correlated with the outcomes of ANOVA were successfully employed to investigate individual parameter's effect on wear loss of Cu–MWCNT MMCs.

This work focuses on investigating the effect of Aluminum Nitride (AlN) for its 5, 10 and 15 vol% dispersion onto the surface of copper matrix through friction stir processing route. Microstructural observation confirms for the breaking down of grain size thus implying dynamic recrystallization process and also uniform dispersion along with good bonding of AlN particles with copper matrix. Hardness of the developed surface composite exhibits an increasing trend which is supposed to be the effect of dispersed particles and grain size reductions. Tensile tests prove that surface composites yields incremental behavior over strength with rise in AlN vol% even though values were far behind the strength offered by pure copper. Fractured surface micrograph of tensile specimens indicates reduction in ductility of the developed composite with dispersion of AlN particles. Wear resistance showcased a positive inclination with respect to reinforcement increments while the frictional coefficient value also possessed a propensity to increase. The fluctuation of frictional coefficient value diminished with AlN addition mainly because of reduced contact between copper and the rotating counterpart besides carrying away the exerted loads by these AlN particles. Detailed characterization of worn out surface prove that with AlN addition wear rate through adhesion has also reduced significantly.

In this research, a novel aluminum alloy and metal matrix composite was designed and developed for self healing purpose. Tin at varying weight percentages (5, 10, 15 & 20 wt%) was alloyed into aluminum along with other alloying elements to form a new set of metal alloy and 5 wt% of SiC particles was dispersed to the above said combinations to develop new sets of composite materials. Optical microscope of the developed set of samples reveals a modification in the grain structure with dispersion of tin element and with respect to increment of tin content the hardness value tends to decrease. Investigated the effect of wire electric discharge machining (WEDM) process parameters such as Pulse On time (PON), Pulse Off time (POFF), wire feed rate (WFR) along with the material elemental composition parameters Sn wt% and SiC wt% using Taguchi coupled Grey Relational Analysis. On behalf of the above said parameters a L32 orthogonal array based experimental design was finalized and based on the experimental studies single and multi criteria based optimization was conceded. Significance of each processing parameters over the output responses Material Removal Rate (MRR) and surface roughness (Ra) was examined through ANOVA method. Machine learning techniques was used and Neural Network models was developed to predict the MRR and Ra values and the experimental confirmations identified the effectiveness of the developed models.

In the present investigation, AZ31 alloy is homogeneously reinforced with 0.2, 0.3, 0.4 and 0.5 wt% of reduced graphene oxide (r-GO) nanosheets for the first time through a series of methodologies involving solvent processing, mechanical alloying, cold pressing and finally sintering under argon atmosphere at $$560^{\circ }\hbox {C}$$ . Scanning Electron Microscopy (SEM) assisted with energy-dispersive X-ray analysis revealed that this inventive fabrication route is useful to easily disperse r-GO into the matrix material and thereby attain methodical homogeneity with a uniform particle size. The attained results show that amongst the others, addition of 0.4 wt% r-GO have obviously improved the hardness up to 64.6 HV and also yielded a better inhibition efficiency of 84% on corrosion. Any further increase of r-GO content resulted for significant decrease in the wear rate up to the level of $$2.6~\hbox {mm}^{3}\hbox { Nm}^{-1}$$ .

This technical article top up in presenting the research over composites consisting of Graphene oxide(GO), Titanium di oxide (TiO2), epoxy resins along with a known curing agent. Therefore, common influences of GO as well TiO2 substances upon the curing process of epoxy systems all along with thermo-mechanical performances of GO-TiO2 epoxy composites were conversed in detail. Herein, assimilation of GO particles resulted to accelerate the curing process of epoxy systems. GO-TiO2 epoxy composites were found to be capable of increasing ultimate tensile strength by up to 59%, flexural strength by 79%, and notable increment in their mechanical properties too was evident while compared with the outcomes of unmodified epoxy based thermosets developed for the purpose. Fracture surface along with tensile properties were made experiential by the way of field-emission SEM and further observations over these micrographs shows in for the occurrence of ductile fracture mechanism with respect to GO addition. Thermal analysis results make public that decomposition temperature of developed composite was up to 470 °C.

Copper-based surface composite dispersed with varying fractions of hybrid reinforcement was fabricated through friction stir processing (FSP). Hybrid reinforcement particles were prepared from aluminum nitride (AIN) and boron nitride (BN) particles of equal weight proportion. Based on design of experiments, wear characteristics of the developed copper surface composites were estimated using pin-on-disk tribometer. Experimental parameters include volumetric fraction of hybrid reinforcement particles (5, 10, and 15 vol %), load (10, 20, 30 N), sliding velocity (1, 1.5, and 2 m/s), and sliding distance (500, 1000, and 1500 m). Microstructural characterization demonstrated uniform dispersion of hybrid reinforcement particles onto the copper surface along with good bonding. Hardness of the developed surface composites increased with respect to increase in hybrid particle dispersion when compared with copper substrate while a reduction in density values was revealed. Analysis on wear rate values proved that wear rate decreased with increase in hybrid particle dispersion and increased with increase in load, sliding velocity, and distance. Analysis of variance (ANOVA) specified load as the most significant factor over wear rate values followed by volume fractions of particle dispersion, sliding velocity, and distance. Regression model constructed was found efficient in predicting wear rate values. Analysis of worn out surfaces through scanning electron microscopy (SEM) revealed the transition of severe to mild wear with respect to increase in hybrid reinforcement particle dispersion. A feed forward back propagation algorithm-based artificial neural network (ANN) model with topology 4-7-1 was developed to predict wear rate of copper surface composites based on its control factors.

Friction Stir Processing (FSP) technique was employed in this research to fabricate copper surface composites through incorporating a hybrid mixture of reinforcement particles. Hybrid combination of advanced nitride based ceramic particles (25% AlN + 75% BN) was dispersed onto the surface of copper matrix at varying volume fractions through FSP technique. Results demonstrated an increase in mechanical properties with respect to increase in the amount of particle dispersion. Mechanism of fracture of developed set of surface composites was studied with the aid of the fractography. A tremendous increase in wear resistance was observed with respect to increase in the hybrid particle dispersion owing the hardness and the self-lubricating characteristics of dispersed ceramic particles.

Machine learning models were introduced to develop a relationship between the elemental composition and degraded mechanical properties in metallic materials due to the presence of hydrogen. Single layer and multilayer feed forward back propagation algorithm was developed as artificial neural network based machine learning models to predict the mechanical properties of hydrogen charged metallic materials. Multilayer feed forward back propagation model was used to predicts the tensile strength, had a network topology of 12-13-3-2. And the single layer feed forward back propagation model was employed to predict the percentage of elongation, has a network topology of 12-11-1. The developed models were validated and tested with unknown inputs and their capability was studied. The models were evaluated using Mean Absolute (MAE) value and represented the scatter diagram to demonstrate the efficiency of the models. The R-value for both the models seems to prove that the models are ready to be used in the practical applications.