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Aluminium silicon carbide reinforced metal matrix composite (Al/SiC‐MMC) materials are rapidly replacing conventional materials in various automotive, aerospace and other industries. Accordingly, the need for accurate machining of composites has increased enormously. The present work analyzes the machining of Al/SiC composites for surface roughness. An empirical model has been developed to correlate the machining parameters and their interactions with surface roughness. Response surface regression and analysis of variance are used for making the model. The developed model can be effectively used to predict the surface roughness in machining Al/SiC‐MMC composites. The influences of different parameters in machining Al/SiC particulate composites have been analyzed through contour graphs and 3D plots.

In the current scenario, new materials are gaining popularity due to higher specific properties of strength and stiffness, increase in wear resistance, dimensional stability at higher temperature, etc. Subsequently, the need for precise machining has also been increased enormously. The purpose of this paper is to study the surface roughness during the turning of Al-10%SiC and Al-5%SiC-5%Gr composites under different cutting conditions.

Artificial neural network (ANN) has been effectively employed in solving problems with effortless computation in the areas such as fault diagnosis, process identification, property estimation, data smoothing and error filtering, product design and development, optimisation and estimation of activity coefficients. Response surface method is also used to analyse the problems involving a number of input parameters and their corresponding relationship between one or more measured dependent responses. Using Design Expert.8 evaluation software package, a simpler and more efficient statistical RSM model has been designed. RSM models are created by using 27 experimental data measurements obtained from different turning conditions of aluminium alloy composites.

In this work, the surface roughness during turning of Al-10%SiC and Al-5%SiC-5%Gr composites under different cutting conditions has been studied. The surface roughness value is proportional with the increase in feed rate and depth of cut while inversely proportional with the cutting speed. In all turning conditions, Al-10%SiC composite has lower surface roughness values than Al-5%SiC-5%Gr hybrid composite. An ANN and response surface models have been developed to predict the surface roughness of machined surface. The experimental results concur well with predicted models.

In the present trend, new materials are gaining popularity due to higher specific properties of strength and stiffness, increase in wear resistance, dimensional stability at higher temperature, etc. Subsequently, the need for precise machining has also been increased enormously. In this work, the surface roughness during turning of Al-10%SiC and Al-5%SiC-5%Gr composites under different cutting conditions has been studied.

The purpose of this paper is to determine the optimum level of geometrical parameters such as helix angle, nose radius, rake angle and machining parameters such as cutting speed, feed rate and depth of cut to arrive minimum surface roughness and tool wear during end milling of Al 356/SiC metal matrix composites (MMCs) using high speed steel end mill cutter.

L27 Taguchi orthogonal design with six factors and three levels is employed for conducting experiments. Analysis of variance (ANOVA) is carried out using Minitab16 software to find the influence of each input parameter on output performance measure. Grey-fuzzy logic multi optimisation algorithm is used to find the optimum level of the input parameters for minimum surface roughness and tool wear simultaneously.

It is found that optimal combination of helix angle 40°, nose radius 0.8 mm, rake angle 12°, cutting speed 90 m/min, feed rate 0.04 mm/rev and depth of cut 1.5 mm have generated minimum surface roughness of 0.4063 µm and tool wear of 0.0375 mm. From ANOVA analysis, it is found that cutting speed influence is more on output performance followed by helix angle and rake angle compared with other machining and geometrical parameters.

The influence of tool geometry during end milling of MMC using Grey-fuzzy logic algorithm has not been explored previously.

Recent trends in material science show a considerable interest in the manufacturing of metal matrix composites to meet the stringent demands of lightweight, high strength and corrosion resistance. Aluminium is the popular matrix metal currently in vogue that can be reinforced with ceramic materials such as particulates to meet the desired property. The purpose of this paper is to fabricate hybrid metal matrix composites to improve the dry sliding wear resistance and to study of the effect of sliding speed, load and reinforcement (alumina and graphite) on wear properties, as well as its contact friction.

The present study addresses the dry sliding wear behaviour of Al‐Si10Mg alloy reinforced with 3, 6 and 9 wt% of alumina along with 3 wt% of graphite. Stir casting method was used to fabricate the composites. Mechanical properties such as hardness and tensile strength have been evaluated. A pin‐on‐disc wear test apparatus was used to evaluate the wear rate and coefficient of friction by varying the loads of 20, 30 and 40 N, sliding speeds of 1.5 m/s, 2.5 m/s and 3.5 m/s at a constant sliding distance of 2100 m.

Mechanical properties of hybrid metal matrix composites (HMMCs) have shown significant improvement. The wear rate and coefficient of friction for alloy and composites decreased with increase in sliding speed and increased with increase in applied load. Temperature rise during wearing process for monolithic alloy was larger than that of HMMCs and Al/9% Al₂O₃/3% Gr composite showing the minimum temperature rise.The worn surfaces of the composites were investigated using scanning electron microscope.

The paper shows that aluminium composites can improve strength and wear resistance.

HMMCs has proven to be useful in improving the dry sliding wear resistance.

The purpose of this study is to optimize input parameters of particle size and applied load to determine minimum weight loss and friction coefficient for Al2O3/SiC particles-reinforced hybrid composites by using Taguchi’s design methodology.

The experimental results demonstrate that the applied size is the major parameter influencing the weight loss for all samples, followed by particle size. The applied load, however, was found to have a negligible effect on the friction coefficient. Moreover, the optimal combination of the testing parameters was predicted. The predicted weight loss and friction coefficient for all the test samples were found to lie close to those of the experimentally observed ones.

The optimum levels of the control factors to obtain better weight loss and friction coefficient were A8 (particle size, 60 μm) and B1 (applied load, 20 N), respectively. Taguchi’s orthogonal design was developed to predict the quality characteristics (weight loss and friction coefficient) within the selected range of process parameters (particle size and applied load). The results were validated through ANOVA.

Firstly, hybrid MMCs ceramic powders were produced and then mechanical tests and optimization were performed.

Nowadays, the applications of metal matrix composites are tremendously increasing in engineering fields. Consequently, the demand for precise machining of composites has also grown enormously. The purpose of this paper is to reduce production cost and simultaneously improve desired product quality through optimal parameter setting using WASPAS and MOORA.

Metal matrix composites were fabricated using stir casting process, with aluminum 6063 as matrix and titanium carbide as reinforcement. Fabricated composite samples were machined on medium duty lathe using cemented carbide tool. All the experiments were carried out based on Box–Behnken design. Comparison of multi objective optimization based on ratio analysis and weighted aggregated sum product assessment in optimizing four parameters, namely, “cutting speed,” “feed rate,” “depth of cut” and “reinforcement weight percent of composite samples”; evaluating their influence on material removal rate, cutting force and surface roughness were carried out.

The output achieved by both MOORA and WASPAS are in similar MCDM) techniques in the selection of machining parameters.

The results obtained in the present paper will be helpful for decision makers in manufacturing industries, who work in metal cutting area, to select the suitable levels for the parameters by implementing the MCDM techniques.

The novelty of this paper is making an attempt to select better MCDM technique based on the comparison of results obtained for the individual technique.

The objective of this research is focused on the design of a new hybrid composite as well as to analyse the optimum turning conditions to minimise the surface roughness and work piece surface temperature, thereby increasing the productivity.

Mechanical properties such as hardness and tensile strength of Al-Si10Mg alloy reinforced with 3, 6 and 9 wt.% of alumina along with 3 wt.% of graphite prepared by stir casting method have been evaluated. The present study addresses the machinability parameter optimisation of Al alloy-9 per cent alumina-3 per centgraphite. Experiments were conducted based on the Taguchi parameter design by varying the feed (0.1, 0.15 and 0.2 mm/rev), cutting speed (200, 250 and 300 m/min) and depth of cut (0.5, 1.0 and 1.5 mm). The results were then analysed using analysis of variance (ANOVA).

Mechanical properties of the hybrid composite increases with reinforcement content. The surface roughness decreases with increasing cutting speed and conversely increases with increasing feed and depth of cut. The work piece surface temperature increases as cutting speed, feed and depth of cut increases. The ANOVA result reveals that feed plays a major role in minimising both surface roughness and surface temperature of work piece. The cutting speed and depth of cut follow feed in the order of importance, respectively.

The vibration of the machine tool is a factor which may contribute to poor quality characteristics. This factor has not taken been into account in this analysis since major vibrations in the machine are induced due to the machining process.

Design and development of new hybrid metal matrix composites (HMMCs) with a detailed analysis on machining conditions. The findings could help in the production of composite with a higher degree of surface finish. This will enable the adoption of HMMCs as industrial product for mass scale production.

Good quality characteristics were achieved using optimum machining conditions arrived using a statistical modelling.

Metal matrix composites (MMCs) are engineered materials formed by the combination of metal matrix and reinforcement materials. They have a stiff and hard reinforcing phase in metallic matrix. The matrix includes metals such as aluminum, magnesium, copper and their alloys. The purpose of this paper is to describe the development of an aluminum alloy‐aluminum oxide composite using a new combination of vortex method and pressure die casting technique and the subsequent tribological studies.

An aluminum alloy‐aluminum oxide composite was developed using vortex method and pressure die casting technique. The aluminum alloy‐1 wt% aluminum oxide was die cast using LM24 aluminum alloy as the matrix material and aluminum oxide particles of average particle size of 16 μm as a reinforcement material. The friction and wear characteristics of the composite were assessed using a pin‐on‐disc set‐up; the test specimen, 8‐mm diameter cylindrical specimens of the composite, was mated against hardened En 36 steel disc of 65 HRC. The tests were conducted with normal loads of 9.8, 29.4 and 49 N and sliding speeds of 3, 4 and 5 m/s for a sliding distance of 5,000 m. The frictional load and the wear were measured at regular intervals of sliding distance.

The effects of normal load and sliding speed on tribological properties of the MMC pin on sliding with En 36 steel disc were evaluated. The wear rate increases with normal load and sliding speed. The specific wear rate marginally decreases with normal load. The coefficient of friction decreases with normal load and sliding speed. The wear and friction coefficient of the aluminum alloy‐aluminum oxide MMC are lower than the plain aluminum alloy. The wear and coefficient of friction of the entire specimens are lower.

The development of aluminum alloy‐aluminum oxide composite using vortex method and pressure die casting technique will revolutionize the automobile and other industries, since a near net shape at low cost and very good mechanical properties are obtained.

There are few papers available on the development of (or tribological studies of) MMCs including aluminium/aluminium alloy‐ceramic composites developed by combination of vortex method and pressure die casting technique.

The purpose of this paper is surface roughness prediction using pattern recognition for the aluminium hybrid metal matrix composite (HMMC).

Hybrid composites were manufactured using liquid metallurgy technique. The cast HMMC was machined using an industrial CNC turning centre and the machining vibration signals were acquired using an accelerometer. The acquired signals were processed and used to build a machine learning model for predicting surface finish based on the tool signature.

The authors established a technique for predicting and monitoring the surface quality during machining using a low cost accelerometer. It is capable of being integrated with the machine controller for online warning of deviations in surface roughness. The system is reconfigurable for any machining condition with a very short training period. The use of this model facilitates online surface roughness monitoring, avoiding the need for costly measuring equipment.

The model developed is innovative and not reported widely to the best of the authors' knowledge. The use of accelerometer‐based surface roughness prediction and control is an innovative approach for automation of machining process monitoring. These can be integrated into any existing machining centre as a standalone system or can be integrated into the CNC controller like Fanuc or Siemens.

The purpose of this paper is to understand the effect of graphite content on the properties of aluminum alloy/silicon carbide/granite (Al/SiC/Gr) composites.

Hardness and wear tests were applied to the powder metallurgical composites, and microstructural characterization was conducted.

Optimum graphite content for maximum wear resistance is reported as weight 6 per cent.

Results of this study may help light weight Al/SiC/Gr composites to be used in different industrial applications.