1996
DOI: 10.1016/0924-0136(96)02361-8
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Development of prediction model for mechanical properties of batch annealed thin steel strip by using artificial neural network modelling

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Cited by 15 publications
(11 citation statements)
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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%
See 1 more Smart Citation
“…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%
“…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%
“…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%