Surface Integrity of LM6 Aluminum Metal Matrix Composite when Machined with High Speed Steel and Uncoated Carbide Cutting Tools

Loh, Kee Shyuan; Izamshah, R.; Ali, Mohd Amran; Kasim, Mohd Shahir; Sulaiman, Mohd Amri; Ahmad, Siti Sarah Nadia; Raffi, Nurul Fatin Mohamad

doi:10.15282/jmes.6.2014.11.0081

Cited by 15 publications

(14 citation statements)

References 14 publications

(12 reference statements)

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“…From the image of the AMC IV sample (Figure 3), the worn surface was characterised by the localised shallow grooves and very fine scratches under SEM micrograph. This is due to the load bearing capacity of the particle mixture [7,19]. It is noteworthy that MPS along with Mg element in AMC showed better performance in terms of hardness and wear resistance.…”

Section: Wear Analysismentioning

confidence: 93%

“…Aluminium alloys are very soft and have limited properties in terms of hardness and wear resistance, however, these properties can be relatively made highly resistant by introducing predominantly hard but brittle particles such as SiC, Al2O3, and B4C [4]. Recent researches [5][6][7][8][9] have shown that aluminium matrix composite (AMC) possesses desired potential properties such as high specific strength and stiffness, required wear resistance to withstand abrasive properties compared to monolithic materials which are demanded by automotive and aerospace industries for the tribological application. Other researchers [10][11][12][13][14] showed that silicon carbide (SiC) is ideal reinforcement candidate for several matrix materials, including aluminium because of its significant ability to enhance the strength, modulus, thermal stability, and abrasive wear resistance of the matrix materials.…”

Section: Introductionmentioning

confidence: 99%

“…Mishra et al [9] investigated the influence of applied load, sliding speed and sliding distance on the wear rate and coefficient of friction. Huang et al [15] reported that the wear resistance of AMCs with 3µm SiC particles is considerably lower compared to 20 µm SiC particles.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wear study of Mg-SiCp reinforcement aluminium metal matrix composite

Maleque¹,

Radhi²,

Rahman³

2016

J. Mech, Eng, Sci.

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Lightweight aluminium metal matrix composite materials hold potential requisite for modern tribological applications due to its inherent and better wear resistant properties over monolithic metallic materials. This study emphasised on the development of Al based metal matrix composite with SiCp as a reinforcement and magnesium (Mg) as a wetting agent using hybrid stir casting process. The study further analysed the effects of different size variations of silicon carbide particles such as the coarse particle size, fine particle size, intermediate particle size and mixed particle size in the fabrication of the composites on the hardness and wear properties. The pin-on-disc test was also done at room temperature in a dry sliding wear condition. It was observed that the mixed particle size SiCp in composite exhibited superior hardness with the value of 98.2 compared to other particle sizes of SiCp. This is due to the fact that mixed particle size supports a greater fraction of applied load while the fine and intermediate particle sizes sustain the hardening due to dislocation. The multiple particle size reinforced composite exhibits better performance than the single particle size in terms of wear resistance as the wear rate was the lowest with the value of 0.99 X 10 -5 . It can be concluded that the Mg addition in the composite showed better and tailored properties with a mixed particle size of SiCp of aluminium metal matrix composite.

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Section: Wear Analysismentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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Wear study of Mg-SiCp reinforcement aluminium metal matrix composite

Maleque¹,

Radhi²,

Rahman³

2016

J. Mech, Eng, Sci.

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“…Surface roughness is mostly based on the cutting parameters (cutting speed, feed rate, and depth of cut) and sometimes some other parameters [4]. In the literature, various models for the optimum surface roughness have been reported [5][6][7][8][9][10]. The feed rate was found to be the dominant factor among the cutting parameters (cutting speed, feed rate and cutting time) on the surface roughness and flank wear during the turning of AISI H11, using the response surface methodology (RSM) [11,12].…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network and regression-based models for prediction of surface roughness during turning of red brass (C23000)

Hanief¹,

Wàňi²

2016

J. Mech, Eng, Sci.

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In this study, models based on artificial neural networks (ANN) and regression analysis were developed to predict the surface roughness during the turning of red brass (C23000), using a high-speed steel (HSS) tool. The full factorial design approach was used for experimentation to achieve a high level of confidence. The influence of cutting parameters (cutting speed, feed rate and depth of cut) on the surface roughness was also investigated using ANOVA. The ANN model having hyperbolic tangent sigmoid (tansig) and linear (purelin) transfer functions was used for the hidden and output layers respectively. The regression model based on the power law was also developed. It was found that at a speed of 840 m/sec and depth of cut of 0.1 mm, the surface roughness changed by 11.3% when changing the feed rate by 5.6%. However, the surface roughness changed by only 6.8% when changing the velocity by 6% at the feed rate of 0.12 mm and depth of cut 0.1 mm. A similar trend was observed for different feed rates, speeds and depths of cut. It was concluded that the feed rate was the most significant factor influencing the surface roughness, followed by the depth of cut and cutting speed. The models developed were compared using statistical methods: coefficient of determination (R 2 ) and mean absolute percentage error (MAPE). R 2 was found to be 0.99784 and 0.9969 for the ANN and regression models, respectively. Similarly, MAPE was found to be 1.4243% and 4.8161% for the ANN and regression models respectively. It was concluded that the ANN model is a superior choice over the multiple regression model for the prediction of surface roughness. The accuracy of the ANN can be attributed to its ability to capture the nonlinearities involved in the turning operation.

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“…Metal matrix composites (MMC) have the potential to meet the design requirements of the automobile industry with significant weight saving and a high strength-to-weight ratio [1][2][3]. Recent developments in aluminum-based MMC make it the ideal candidate to replace heavier materials like iron and steel in the car for weight reduction [4].…”

Section: Introductionmentioning

confidence: 99%

Fatigue Life Analysis and Optimization of a Passenger Car Steering Knuckle Under Operating Conditions

Sivananth¹,

Vijayarangan²

2015

Int. J. Automot. Mech. Eng.

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This paper presents a methodology for analyzing the multiaxial loads acting on a steering knuckle under operating conditions. The knuckle is a critical structural part which connects the wheel in a car to the steering and suspension system. Computeraided modeling and analysis were carried out on the knuckle, and the results were compared for conventional and metal matrix composites (MMC) with ANSYS software. The simulation showed that the steering-arm region failed under cyclic load conditions. Accordingly, the knuckle was optimized with a genetic algorithm and a modified design was proposed. Steering knuckles were fabricated from three different materials: ductile iron, LM 6 alloy and MMC with a modified design were experimentally tested in the steering-arm region to evaluate the fatigue life. Simulation results showed that the fatigue life of the MMC knuckle was 1.946E+06 fatigue cycles after optimization whereas experimental results showed 1.20E+06 cycles. Knuckle weight reduction of 60% was achieved by replacing ductile iron with MMC. Hence it is recommended to use MMC steering knuckles in automobiles.

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Surface Integrity of LM6 Aluminum Metal Matrix Composite when Machined with High Speed Steel and Uncoated Carbide Cutting Tools

Cited by 15 publications

References 14 publications

Wear study of Mg-SiCp reinforcement aluminium metal matrix composite

Wear study of Mg-SiCp reinforcement aluminium metal matrix composite

Artificial neural network and regression-based models for prediction of surface roughness during turning of red brass (C23000)

Fatigue Life Analysis and Optimization of a Passenger Car Steering Knuckle Under Operating Conditions

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