Development of prediction model for mechanical properties of batch annealed thin steel strip by using artificial neural network modelling

Myllykoski, P.; Larkiola, Jari; Nylander, J.

doi:10.1016/0924-0136(96)02361-8

Cited by 15 publications

(11 citation statements)

References 2 publications

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“…for Ti-6Al-4V alloy 199 or in industries. 186,200,201 Provided reliable database is available in industries, the models find their use in estimation of relative significance of the variables and their application in development of intelligent on-line process control systems owing to its capability to predict the properties at different stages of processing without the necessity of costly experiments.…”

Section: Process-property Correlationmentioning

confidence: 99%

“…different locations of batch annealing coils). 277 In the course of analysing the predictions, the authors have demonstrated that samples collected from different locations and/or having different thermal history contributed significantly in the variation in accuracy of prediction. Classification of steel plates was conducted by developing ANN models vis-a `-vis using a multivariate data from steel plant, Mahalanobis Taguchi System.…”

Section: Composition Process Property Correlationmentioning

confidence: 99%

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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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Section: Process-property Correlationmentioning

confidence: 99%

Section: Composition Process Property Correlationmentioning

confidence: 99%

Soft computing techniques in advancement of structural metals

Datta

Chattopadhyay

2013

International Materials Reviews

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“…The application of NNs in several industrial processes can be found in the extensive literature ( [42][43][44]46], among others). In [19,22,29], NNs were proposed as an efficient model to predict mechanical properties in industrial processes.…”

Section: Introductionmentioning

confidence: 99%

Using artificial neural networks to represent a diesel–biodiesel engine

Lage

Hanriot

Zárate

2020

J Braz. Soc. Mech. Sci. Eng.

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In this work, six computational models based on artificial neural networks were developed to simulate an operating diesel engine fuelled with 8% biodiesel in order to predict performance and emissions of a diesel-biodiesel engine in a group generator. The ANN models were used to simulate a diesel-biodiesel engine that has four cylinders with a volume of 3.9 l, a compression ratio of 17:1, direct injection, and a rated power of 49 kW. The models were validated against experimental data for 10 kW, 20 kW, and 30 kW loads. The models were capable of accurately predicting the output power, thermal efficiency, and emissions of CO 2 , CO, NO, and NOx. Their comparison with experimental results showed a satisfactory agreement and the reliability of their predicted results for new operating conditions.

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“…In technical applications the error is mainly represented by the following parameters with obvious labelling: SSE, R-square, Adjusted R-Square, Root Mean Squared Error RMSE [8].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks Application in Modal Analysis of Tires

Koštial¹,

Jančíková²,

Bakošová³

et al. 2013

Measurement Science Review

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The paper deals with the application of artificial neural networks (ANN) to tires' own frequency (OF) prediction depending on a tire construction. Experimental data of OF were obtained by electronic speckle pattern interferometry (ESPI). A very good conformity of both experimental and predicted data sets is presented here. The presented ANN method applied to ESPI experimental data can effectively help designers to optimize dimensions of tires from the point of view of their noise.

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Development of prediction model for mechanical properties of batch annealed thin steel strip by using artificial neural network modelling

Cited by 15 publications

References 2 publications

Soft computing techniques in advancement of structural metals

Soft computing techniques in advancement of structural metals

Using artificial neural networks to represent a diesel–biodiesel engine

Artificial Neural Networks Application in Modal Analysis of Tires

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