Prediction of martensite and austenite start temperatures of the Fe-based shape memory alloys by artificial neural networks

Eyerci̇oğlu, Ömer; Kanca, Erdoğan; Pala, Murat; Özbay, Erdoğan

doi:10.1016/j.jmatprotec.2007.09.085

Cited by 34 publications

(13 citation statements)

References 32 publications

(27 reference statements)

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“…To predict the phase transformation behavior and to design shape-memory property, the optimum concentrations of the alloy components can be calculated with various equations for the Gibbs free energy difference between the £-and ¾-phases, 2831) and various empirical equations for M s temperature of the £ ¼ ¾ transformation. 32,33) On the other hand, the roles of silicon are diversified and complicated. Silicon hardens the parent matrix to suppress the dislocation gliding, while it promotes the £ ¼ ¾ martensitic transformation through lowering the stacking fault energy.…”

Section: Functions Of Elementsmentioning

confidence: 99%

Design Concept and Applications of Fe–Mn–Si-Based Alloys—from Shape-Memory to Seismic Response Control

et al. 2016

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FeMnSi-based alloys exhibit a shape-memory effect associated with deformation-induced £ ¼ ¾ martensitic transformation and its reversion. The £ ¼ ¾ martensitic transformation also enhances mechanical properties, such as strength, hardness, wear-resistance and low-cycle fatigue lives of the alloys. In this article, we review fundamental researches on transformation behavior, microstructural and crystallographic characteristics, and functional and mechanical properties of the FeMnSi-based alloys, and introduce various examples of their practical applications. A special emphasis is placed on their new application as architectural seismic dampers, which were developed based on a new finding of the passive two-way martensitic transformations under cyclic tensile-compressive loading.

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Section: Functions Of Elementsmentioning

confidence: 99%

Design Concept and Applications of Fe–Mn–Si-Based Alloys—from Shape-Memory to Seismic Response Control

et al. 2016

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“…Each ANN must have three layers: input layer, hidden layer and output layer. The greatest number of ANNs has one hidden layer though there may be more of them [12,13,14].…”

Section: Artificial Neural Network (Ann) 31 Overview Of Annmentioning

confidence: 99%

Modelling of Kerf Width in Plasma Jet Metal Cutting Process using ANN Approach

et al. 2018

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Abstract:In this paper Artificial Neural Network (ANN) model was developed for prediction of kerf width in plasma jet metal cutting process. Process parameters whose influence was analyzed are cutting height, cutting speed and arc current. An L18 (2 1 x3 7 ) Taguchi orthogonal array experiment was conducted on aluminium sheet of 3 mm thickness. Using the experimental data a feed -forward backpropagation artificial neural network model was developed. After the prediction accuracy of the developed model was verified, the model was used to generate plots that show influence of process parameters and their interactions on analzyed kerf width and to get conlusions about process parameters values that lead to minimal kerf width.

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“…It uses a hyperbolic tangent sigmoid transfer function. The summation u i is transferred using a scalar-to-scalar function called an ''activation or transfer function", f(u i ), to yield a value called the unit's ''activation", given as: The most commonly used training algorithm for the multi-layer perception is a back propagation algorithm (BPA) [73]. It is a gradient descent method to minimize the error for a particular training pattern.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

An artificial neural network based decision support system for energy efficient ship operations

Beşikçi

Arslan

Turan

et al. 2016

Computers & Operations Research

191

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Reducing fuel consumption of ships against volatile fuel prices and greenhouse gas emissions resulted from international shipping are the challenges that the industry faces today. The potential for fuel savings is possible for new builds, as well as for existing ships through increased energy efficiency measures; technical and operational respectively. The limitations of implementing technical measures increase the potential of operational measures for energy efficient ship operations. Ship owners and operators need to rationalise their energy use and produce energy efficient solutions. Reducing the speed of the ship is the most efficient method in terms of fuel economy and environmental impact. The aim of this paper is twofold: (i) predict ship fuel consumption for various operational conditions through an inexact method, Artificial Neural Network ANN; (ii) develop a decision support system (DSS) employing ANN based fuel prediction model to be used on-board ships on a real time basis for energy efficient ship operations. The fuel prediction model uses operating data -'Noon Data' -which provides information on a ship's daily fuel consumption. The parameters considered for fuel prediction are ship speed, revolutions per minute (RPM), mean draft, trim, cargo quantity on board, wind and sea effects, in which output data of ANN is fuel consumption. The performance of the ANN is compared with multiple regression analysis (MR), a widely used surface fitting method, and its superiority is confirmed. The developed DSS is exemplified with two scenarios, and it can be concluded that it has a promising potential to provide strategic approach when ship operators have to make their decisions at an operational level considering both the economic and environmental aspects.

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Prediction of martensite and austenite start temperatures of the Fe-based shape memory alloys by artificial neural networks

Cited by 34 publications

References 32 publications

Design Concept and Applications of Fe–Mn–Si-Based Alloys—from Shape-Memory to Seismic Response Control

Design Concept and Applications of Fe–Mn–Si-Based Alloys—from Shape-Memory to Seismic Response Control

Modelling of Kerf Width in Plasma Jet Metal Cutting Process using ANN Approach

An artificial neural network based decision support system for energy efficient ship operations

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Prediction of martensite and austenite start temperatures of the Fe-based shape memory alloys by artificial neural networks

Cited by 34 publications

References 32 publications

Design Concept and Applications of Fe&ndash;Mn&ndash;Si-Based Alloys&mdash;from Shape-Memory to Seismic Response Control

Design Concept and Applications of Fe&ndash;Mn&ndash;Si-Based Alloys&mdash;from Shape-Memory to Seismic Response Control

Modelling of Kerf Width in Plasma Jet Metal Cutting Process using ANN Approach

An artificial neural network based decision support system for energy efficient ship operations

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Product

Resources

About

Design Concept and Applications of Fe–Mn–Si-Based Alloys—from Shape-Memory to Seismic Response Control

Design Concept and Applications of Fe–Mn–Si-Based Alloys—from Shape-Memory to Seismic Response Control