Corrigendum to “Application of neural network and genetic algorithm to powder metallurgy of pure iron” [Mater. Des. 32 (6) (2011) 3183–3188]

Reihanian, M.; Asadullahpour, S.R.; Hajarpour, S.; Gheisari, Kh.

doi:10.1016/j.matdes.2014.04.065

Cited by 3 publications

(4 citation statements)

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“…This method is a further for ineffective derivative-based approaches to deal with problems in which the fitness function is discontinuous, non-differentiable, stochastic, or highly nonlinear. This feature make GA is available for a wide variety of applications (Reihanian et al, 2011;Ahmadi et al, 2014e;2015a;2015b;2015c;Baghban et al, 2015;Ahmadi and Bahadori, 2015;Ahmadi, 2015).…”

Section: Genetic Algorithmmentioning

confidence: 99%

A least-squares support vector machine approach to predict temperature drop accompanying a given pressure drop for the natural gas production and processing systems

Ahmadi

Hasanvand²,

Bahadori

2015

International Journal of Ambient Energy

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Precise estimation of temperature variations throughout gas-production systems can enhance designing the production amenities. Routine method for determining the temperature profiles in gas production system are based on the gas composition and flash calculations. However, if the gas compositions are not available, the gas production system can be modeled by employing a black-oil approach, which is also a method for calculating the oil/gas resources A c c e p t e d M a n u s c r i p t 2 and for modeling the gas reservoir operation. Accordingly, for black oil models and when the natural gas compositions are not accessible, applying robust predictive tools in this research is of high interest in natural production systems. The current study plays emphasis on applying the predictive model based on least square support vector machine (LSSVM) to estimate precisely the proper temperature drop associated a given pressure drop throughout natural gas production systems based on the black-oil approach to acquire an accurate result for the temperature drop of a natural gas streams. Genetic algorithm (GA) was used to optimize hyper parameters (γ and σ 2 ) which are embedded in LSSVM model. Using this method is simple and accurate to determine the temperature drop through the natural gas stream with minimum uncertainty.

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Section: Genetic Algorithmmentioning

confidence: 99%

A least-squares support vector machine approach to predict temperature drop accompanying a given pressure drop for the natural gas production and processing systems

Ahmadi

Hasanvand²,

Bahadori

2015

International Journal of Ambient Energy

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“…Such an approach has also been found effective in optimisation of the sintering parameter of pure iron. 225 In an effort to maximising the recoverable strain by optimising process parameters in Ti rich NiTi, ANN models were used as objective function and GA was used to establish a trade off between strength and shape recovery. 226 In the study cryogenic treatment was identified as an effective treatment for improving shape recovery and the prediction was experimentally validated.…”

Section: Process-property Correlationmentioning

confidence: 99%

“…The results were validated by comparison of the results with corresponding experimentally developed process maps. Such an approach has also been found effective in optimisation of the sintering parameter of pure iron 225. In an effort to maximising the recoverable strain by optimising process parameters in Ti rich NiTi, ANN models were used as objective function and GA was used to establish a trade off between strength and shape recovery 226.…”

Section: Composition–process–microstructure–propertymentioning

confidence: 99%

Soft computing techniques in advancement of structural metals

Datta

Chattopadhyay

2013

International Materials Reviews

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Current trends in the progress of technology demand availability of materials resources ahead of the advancing fronts of the application areas. During the last couple of decades, significant progress has been made in computational and experimental design of materials. Among the potential computational techniques, soft computing stands in distinction due to the inherent flexibility in capturing the complexity of the problem in global scale. Since 1990s remarkable success has been achieved in soft computing activities in different facets of materials science and engineering. Extensive efforts have been devoted in design of metals and alloys based on composition-process-microstructure-property correlation. The present review aims to address the contribution of soft computing in the field of structural metals and alloys including processing and joining. The critical issues concerning applicability of particular techniques in specific materials problem have been particularly emphasised encompassing the scope of integrating the gradual progress in different techniques in hybrid and tandem framework to address greater complexities in larger length and time scale. Attempt has also been made to emphasise on the evolution of newer knowledge and materials through soft computing activities. Finally, the potential of soft computing techniques in futuristic design approaches has been critically enumerated.

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“…Simply and effective method that can be used to form this structure is powder metallurgy. The final product has near-net shape with uniform morphology [16].…”

Section: Introductionmentioning

confidence: 99%

Static Corrosion Test of Porous Iron Material with Polymer Coating

Markušová-Bučková

Oriňáková

Oriňák

et al. 2016

Powder Metallurgy Progress

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At present biodegradable implants received increased attention due to their use in various fields of medicine. This work is dedicated to testing of biodegradable materials which could be used as bone implants. The samples were prepared from the carbonyl iron powder by replication method and surface polymer film was produced through sol-gel process. Corrosion testing was carried out under static conditions during 12 weeks in Hank's solution. The quantity of corrosion products increased with prolonging time of static test as it can be concluded from the results of EDX analysis. The degradation of open cell materials with polyethylene glycol coating layer was faster compared to uncoated Fe sample. Also the mass losses were higher for samples with PEG coating. The polymer coating brought about the desired increase in degradation rate of porous iron material.

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Corrigendum to “Application of neural network and genetic algorithm to powder metallurgy of pure iron” [Mater. Des. 32 (6) (2011) 3183–3188]

Cited by 3 publications

References 0 publications

A least-squares support vector machine approach to predict temperature drop accompanying a given pressure drop for the natural gas production and processing systems

A least-squares support vector machine approach to predict temperature drop accompanying a given pressure drop for the natural gas production and processing systems

Soft computing techniques in advancement of structural metals

Static Corrosion Test of Porous Iron Material with Polymer Coating

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