Calculation model of formation of nonmetallic inclusions of multilayer morphology in weld metal

2018 ◽  
Vol 2018 (2) ◽  
pp. 5-10
Author(s):  
L.A. Taraborkin ◽  
◽  
V.V. Golovko ◽  
Keyword(s):  
Weld Metal ◽  
Nonmetallic Inclusions ◽  
Calculation Model

Effect of nonmetallic inclusions on cold crack susceptibility of armor casting steel weld metal

2013 ◽  
Vol 14 (12) ◽  
pp. 2137-2142 ◽  
Author(s):  
Soon-Cheol Park ◽  
Yong-Jun Lee ◽  
Hui-Jun Yi ◽  
Geum-Bin Yu ◽  
Sung-Su Kang
Keyword(s):  
Weld Metal ◽  
Nonmetallic Inclusions ◽  
Steel Weld ◽  
Steel Weld Metal ◽  
Casting Steel ◽  
Crack Susceptibility

scholarly journals APPLICATION OF BARIUM-STRONTIUM CARBONATITE FOR PRODUCTION OF WELDING FLUXES BASED ON SILICOMANGANAZE PRODUCTION SLAG

2018 ◽  
Vol 61 (8) ◽  
pp. 596-600
Author(s):  
N. A. Kozyrev ◽  
L. P. Bashchenko ◽  
O. E. Kozyreva ◽  
A. R. Mikhno
Keyword(s):  
Weld Metal ◽  
Nonmetallic Inclusions ◽  
Weld Zone ◽  
Chemical Compositions ◽  
Low Alloy Steels ◽  
Barium Strontium ◽  
Welding Fluxes ◽  
Study Results ◽  
Alloy Steels ◽  
In Series

The study results of introduction of barium-strontium carbonatite of various fractional composition into flux based on silicomanganese production slag are presented. The principal possibility of using their mixtures for depositing and welding of low-alloy steels is shown, while the use of barium-strontium carbonatite makes it possible to reduce contamination of weld metal with nonmetallic inclusions. In series of experiments in laboratory conditions, various compositions of welding fluxes were made and investigated. As components, barium-strontium modifier BSC produced by “NPK Metallotechnoprom” LC under TU 1717-001-75073896-2005 was used, wt. %: 13.0 – 19.0 % BaO; 3,5 – 7,5 % SrO; 17.5 – 25.5 % CaO; 19.8 – 29.8 % SiO2 ; 0.7 – 1.1 % MgO; 2.5 – 3.5 % K2O; 1.0 – 2.0 % Na2O; 1.5 – 6.5 % Fe2O3 ; 0 to 0.4 % MnO; 1.9 – 3.9 % of Al2O3 ; 0.7 – 1.1 % TiO2 ; 16.0 – 20.0 % CO2 as well as silicomanganese slag produced by JSC “EVRAZ – West-Siberian Metallurgical Combine”, wt. %: 6.91 – 9.62 % Al2O3 ; 22.85 – 31.70 % CaO; 46.46 – 48.16 % SiO2 ; 0.27 – 0.81 % FeO; 6.48 – 7.92 % MgO; 8.01 – 8.43 % MnO; 0.28 – 0.76 % F; 0.26 – 0.36 % Na2O; up to 0,62 % K2O; 0.15 – 0.17 % S; 0.01 % P. Basis of the flux is silicomanganese production slag, into which a flux additive was introduced. Flux additive was produced in two ways. The first one: by mixing barium-strontium modifier with liquid glass in a ratio of 75 and 35 %, respectively. The second variant is as follows: dust of strontium-barium modifier of fraction less than 0.2 mm was used as a flux additive. The technology of flux-additive manufacturing is described. Welding of rollers was carried out using ASAW-1250 welding tractor. Regimes of surfacing were worked out. The chemical compositions of fluxes, slag crusts, flux and weld metal were determined. Metallographic studies of metal were performed. The results of analysis for presence of nonmetallic inclusions in weld zone were carried out in accordance with GOST 1778 – 70. Studies indicate a decrease in contamination of weld metal by silicates that are not deformed and absence of brittle silicates. 

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

Regarding the mechanism of formation of nonmetallic inclusions conglomerates of system Al2O3-MgO

2019 ◽  
Vol 75 (12) ◽  
pp. 1341-1351
Author(s):  
A. A. Safronov ◽  
V. S. Dub ◽  
V. V. Orlov ◽  
K. L. Kosyrev ◽  
A. S. Loskutov ◽  
...  
Keyword(s):  
Nonmetallic Inclusions ◽  
Mechanism Of Formation

Effect of Shield Gas Composition on Cold Cracking Susceptibility of YS 600 MPa Flux Cored Arc Weld Metal

2015 ◽  
Vol 53 (7) ◽  
pp. 480-487 ◽  
Author(s):  
Namhyun Kang ◽  
Guo Xian ◽  
Myungjin Lee ◽  
Junghoon Lee
Keyword(s):  
Weld Metal ◽  
Cold Cracking ◽  
Gas Composition

Development of the Partial Discharge Inception Calculation Model in a Non-uniform Air Gap Considering the Effect of Humidity

2011 ◽  
Vol 131 (12) ◽  
pp. 1017-1023 ◽  
Author(s):  
Norihito Yanagita ◽  
Tatsuro Kato ◽  
Toshiaki Rokunohe ◽  
Takeshi Iwata ◽  
Hiroki Kojima ◽  
...  
Keyword(s):  
Partial Discharge ◽  
Calculation Model ◽  
Air Gap
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