In situ technique for synthesizing (TiB+TiC)/Ti composites

Zhang, Xiaonong; Lü, Weijie; Zhang, Di; Wu, Rui; Bian, Yujun; Fang, Pingwei

doi:10.1016/s1359-6462(99)00087-1

Cited by 219 publications

(99 citation statements)

References 22 publications

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“…After the OS is selected and the MF is generated, operation of the network is tested with the use of them (OS & MF). Twenty sample data (data of samples [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] were used from the total of 30, as data sets for network training. Table 3 shows the normalized sample data used for networks training.…”

Section: Resultsmentioning

confidence: 99%

“…9 Titanium monoboride (TiB) whiskers and titanium carbide (TiC) particles offer high modulus, relative chemical stability, and high thermal stability, while maintaining similar density and thermal expansion coefficient to those of the titanium matrix, as well as clean interfaces without any unfavorable reaction between the precipitates and the titanium matrix. [10][11][12] The reinforcements were obtained according to the high-temperature reactions as follows in Equations (1) and (2): 13,14 5Ti+B4C = 4TiB+TiC…”

Section: Introductionmentioning

confidence: 99%

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Modeling of shot-peening effects on the surface properties of a (TiB + TiC)/Ti–6Al–4V composite employing artificial neural networks

Maleki¹,

Zabihollah²

2016

Mater. Tehnol.

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Titanium matrix composites (TMCs) have wide application prospects in the field of aerospace, automobile and other industries because of their good properties, such as high specific strength, good ductility, and excellent fatigue properties. However, in order to improve their fatigue strength and life, crack initiation and growth at the surface layers must be suppressed using surface treatments. Shot peening (SP) is an effective surface mechanical treatment method widely used in industry which can improve the mechanical properties of a surface. However, artificial neural networks (ANNs) have been used as an efficient approach to predict and optimize the science and engineering problems. In the present study the effects of SP on TMC were modeled by means of ANN and the capability of the ANN in predicting the output parameters is investigated. A back-propagation (BP) error algorithm is developed for the network training. Data of experimental tests on the (TiB + TiC)/Ti-6Al-4V composite are employed in order to train the network. The volume fractions of the reinforcements (TiB + TiC) were 5 % and 8 %. ANN testing is accomplished using different experimental data thaat were not used during the network training. The distance from the surface (depth) and SP intensity are regarded as input parameters and residual stress and hardness of the Ti-6Al-4V before and after the SP and adding reinforcements are gathered as the output parameters of the network. A comparison was made between experimental and predicted data. The predicted results were in good agreement with experimental ones, which indicates that developed neural network can be used for modeling the SP process on TMCs. Keywords: titanium matrix composites, surface treatment, shot peening, artificial neural networks, residual stress, hardness Kompoziti na osnovi titana (TMCs) imajo {iroko mo`nost uporabe na podro~ju letalstva, avtomobilske in druge industrije zaradi njihovih dobrih lastnosti, kot so: velika specifi~na trdnost, dobra duktilnost in odli~na odpornost na utrujanje. Vseeno pa je za pove~anje odpornosti na utrujanje in`ivljenjsko dobo, potrebna povr{inska obdelava, da se zavre nastanek razpok in njihova rast na povr{ini. Hladno povr{insko kovanje (SP) je u~inkovita mehanska metoda, ki se v industriji pogosto uporablja za izbolj{anje mehanskih lastnosti povr{ine. Umetne nevronske mre`e (ANNs) se uporabljajo kot u~inkovit pribli`ek za napovedovanje in optimiranje znanstvenih osnov in in`eniringa tega problema. V {tudiji so bili modelirani vplivi SP na TMC s pomo~jo ANN in preiskovana je bila zmo`nost napovedovanja izhodnih parametrov z ANN. Za usposabljanje mre`e je bil razvit algoritem vzvratnega {irjenja napak (BP). Podatki iz eksperimentalnih preizkusov na (TiB + TiC)/Ti-6Al-4V kompozitu so uporabljeni za usposabljanje mre`e. Volumska dele`a delcev (TiB + TiC) za oja~anje sta bila 5 % in 8 %. ANN preizku{anje je bilo izvedeno z uporabo razli~nih eksperimentalnih podatkov, ki niso bili uporabljeni pri usposabljanju mre`e. Razdalja od povr{ine (globina) in...

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling of shot-peening effects on the surface properties of a (TiB + TiC)/Ti–6Al–4V composite employing artificial neural networks

Maleki¹,

Zabihollah²

2016

Mater. Tehnol.

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“…On the basis of the former papers, [3][4][5][6] the commercial pure titanium and TMCs reinforced with TiB were in situ fabricated by non-consumable vacuum arc remelting. The total volume percentage of reinforcements TiB was 8% and 5%, respectively.…”

Section: Methodsmentioning

confidence: 99%

Oxidation Behavior of <i>In Situ</i> Synthesized TiB/Ti Composite in Air Environment

et al. 2004

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Isothermal oxidation behavior of in situ synthesized TiB/Ti composites has been investigated. Samples of titanium matrix composites reinforced with 0, 5 and 8 vol% TiB particulates were oxidized at 823, 873, and 923 K for 300 h in atmospheric air. Scanning electron microscopy (SEM) with energy dispersive X-ray spectrometry, and transmission electron microscopy (TEM) were used to identify oxidation products and characterize oxide scale morphology. Oxidation was observed to follow parabolic kinetics. Rate of oxidation decreased with the increase of TiB reinforcements. The oxide scale formed on TiB/Ti composites was rutile-type TiO 2 . No other oxides were observed within the oxide scale. The increased oxidation resistance due to the addition of the in situ synthesized TiB reinforcement was attributed to the improved tendency for the formation of thin and dense oxide avoiding crack and spallation, the strong enough interfacial cohesion and the clean interfacial microstructure between the reinforcements and the titanium matrix alloy.

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“…A large number of researchers have been endeavoring to attain effective techniques for the economic net-shape forming of TMCs through the combination of their advantages such as high temperature strength, excellent damage tolerance and creep and wear resistance. [1][2][3] Until recently, several techniques based on in-situ synthesis mechanism have been reported such as cold and hot isostatic pressing, XD TM and self-propagation high temperature synthesis routes. [4][5][6] However, the widespread use of TMCs produced by powder metallurgy processes has been limited, mainly due to their drawbacks concerning the soundness, size and design of the products.…”

Section: Introductionmentioning

confidence: 99%

Net-Shaping of <I>In-situ</I> Synthesized (TiC+TiB) Hybrid Titanium Matrix Composites

Sung

Kim

2005

Mater. Trans.

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The aim of this study is to establish an effective technique for the economic net-shape forming of in-situ synthesized titanium matrix composites (TMCs) using a casting route. In-situ synthesis and investment casting of TMCs were carried out in a horizontal centrifugal vacuum induction melting furnace. The synthesized (TiC+TiB) TMCs were examined using scanning electron microscopy, electron probe microanalyzer and X-ray diffraction, and through thermodynamic calculations. No melts-mold reaction was observed between the synthesized composites and a SKK mold, since the SKK mold comprised both interstitial and substitutional melts-mold reaction products. The results of the in-situ synthesis and the investment casting of TMCs show that our casting route can be an effective approach for the economic net-shape forming of TMCs.

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In situ technique for synthesizing (TiB+TiC)/Ti composites

Cited by 219 publications

References 22 publications

Modeling of shot-peening effects on the surface properties of a (TiB + TiC)/Ti–6Al–4V composite employing artificial neural networks

Modeling of shot-peening effects on the surface properties of a (TiB + TiC)/Ti–6Al–4V composite employing artificial neural networks

Oxidation Behavior of <i>In Situ</i> Synthesized TiB/Ti Composite in Air Environment

Net-Shaping of <I>In-situ</I> Synthesized (TiC+TiB) Hybrid Titanium Matrix Composites

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