Effect of reinforcement, compact pressure and hard ceramic coating on aluminium rock dust composite performance

Prakash, K. Soorya; Moorthy, R. Sathiya; Gopal, P.; Kavimani, V.

doi:10.1016/j.ijrmhm.2015.07.037

Cited by 52 publications

(9 citation statements)

References 17 publications

(17 reference statements)

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“…Similary the Same trend was observed for tensile strength and impact strength. Since on comparing all the three composites 6 weight percentage of titanium carbide had the better result third specimen is considered for these experimental investigations [22][23][24].…”

Section: Properties Of Niobium Metal Matrixmentioning

confidence: 99%

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Material characterization and unconventional machining on synthesized Niobium metal matrix

Sivakandhan¹,

Loganathan

Murali

et al. 2020

Mater. Res. Express

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The Purpose of this work is to develop a niobium based metal matrix alloy through sintering based powder metallurgy technique. The 2, 4 and 6 weight percentage of Titanium Carbide (TiC) is added to the alloy. Various material properties such as hardness, tensile strength, impact strength and density are measured after the addition of TiC particle. The characterization, microstructure and particle size of the developed metal matrix are learnt through Scanning Electron Microscope (SEM) and Energy

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Section: Properties Of Niobium Metal Matrixmentioning

confidence: 99%

“…The quality of laser cutting depends on the gas pressure and laser power [22]. Better surface quality can be achieved by pulsed mode type of laser beam [23].…”

Section: Introductionmentioning

confidence: 99%

Material characterization and unconventional machining on synthesized Niobium metal matrix

Sivakandhan¹,

Loganathan

Murali

et al. 2020

Mater. Res. Express

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“…The optimal constraints of WR and CoF were recognized and so was abridged wear rate 19,20 . Powder metallurgy is commonly used for the production of processed MMC materials with sufficient potential that can be used in many applications instead of obtaining a few admirable properties over other competing methods 21 .…”

Section: Introductionmentioning

confidence: 99%

Applications of Artificial Neural Network Simulation for Prediction of Wear Rate and Coefficient of Friction Titanium Matrix Composites

Arun¹,

Jasmin

Kamesh

et al. 2023

Mat. Res.

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The Artificial Neural Network (ANN) techniques were utilized to predict wear rate and CoF of the Ti-5Al-2.5Sn matrix reinforced with B 4 C particle manufactured by the powder metallurgy. TMCs and wear test samples were characterized by the Scanning Electron Microscope (SEM). Dry sliding wear narrative of the composites was estimated on a pin-on-disc machine at various loads of 20-60N, sliding velocity of 2-6m/s and sliding distance from 1000m-3000m. The wear rate of the composite was reduced by augmentation in weight fraction of boron carbide from 3-9%. The benefits of interfacial TMCs with B 4 C are: increase in strength, wear-resistance, and volume fraction. ANN was planned and utilizes a Levenburg-Marquardt program algorithm to reduce the mean squared error using a back-propagation technique. The input parameters are considered to include load, sliding velocity, and sliding distance. The experimental results of an ANN model and regression model are compared. ANN replicas have been urbanized to foreshow experimental rate of wear and CoF of TMCs and examined that ANN predictions have exceptional concord with deliberated values. Accordingly, the prediction of wear rate and CoF of TMCs using ANN in earlier actual manufacture will significantly save the manufacturing time, exertion, and expenditure.

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“…Aluminum alloys are reinforced with ceramic particles such as SiC, TiB 2 , and B 4 C to synthesize metal matrix composites (MMC) with enhanced mechanical properties [ 2 ]. MMC materials are often subjected to post-processing machining and forming processes, in order to obtain final engineering products [ 3 ]. It is tedious to machine such higher strength MMCs using conventional machining processes.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Abrasive Water Jet Machining of SiC Reinforced Aluminum Alloy Based Metal Matrix Composites Using Taguchi–DEAR Technique

et al. 2021

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Since the importance of introducing new engineering materials is increasing, the need for machining such higher strength materials has also considerably increased. In the present research, an endeavor was made to introduce a Taguchi–DEAR methodology for the abrasive water-jet machining process, while machining a SiC-reinforced aluminum composite. Material removal rate, taper angle, and surface roughness were considered as the quality measures. The optimal arrangement of input process factors in the AWJM process was found to be 2800 bar (WP), 400 mg/min (AF), 1000 mm/min (FR), and 4 mm (SOD), among the chosen factors, with an error accuracy of 0.8%. The gas pressure had the most significance for formulating the performance measures, owing to its ability to modify the impact energy and crater size of the machined specimen.

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Effect of reinforcement, compact pressure and hard ceramic coating on aluminium rock dust composite performance

Cited by 52 publications

References 17 publications

Material characterization and unconventional machining on synthesized Niobium metal matrix

Material characterization and unconventional machining on synthesized Niobium metal matrix

Applications of Artificial Neural Network Simulation for Prediction of Wear Rate and Coefficient of Friction Titanium Matrix Composites

Optimization of Abrasive Water Jet Machining of SiC Reinforced Aluminum Alloy Based Metal Matrix Composites Using Taguchi–DEAR Technique

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