Classifying tensile strength of HSLA steel: an investigation through neural networks using Mahalanobis Distance

Das, Prasun; Datta, Someswar; Bhattacharyay, B. K.

doi:10.1504/ijmms.2010.029877

Cited by 2 publications

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“…Classification of steel plates was conducted by developing ANN models vis-a `-vis using a multivariate data from steel plant, Mahalanobis Taguchi System. 278 The investigation was further extended for classification and exploring the role of input variables by considering chemical composition, including microalloys, and hot rolling parameters. Among the process variables, post-rolling cooling rate has been found to be the most significant variable, as it controls the postrolling transformation products of austenite and thus the final microstructure.…”

Section: Composition Process Property Correlationmentioning

confidence: 99%

“…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-à-vis using a multivariate data from steel plant, Mahalanobis Taguchi System 278. The investigation was further extended for classification and exploring the role of input variables by considering chemical composition, including microalloys, and hot rolling parameters.…”

Section: Composition–process–microstructure–propertymentioning

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: Composition Process Property Correlationmentioning

confidence: 99%

Section: Composition–process–microstructure–propertymentioning

confidence: 99%

Soft computing techniques in advancement of structural metals

Datta

Chattopadhyay

2013

International Materials Reviews

View full text Add to dashboard Cite

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Mahalanobis Taguchi system: a review

Ghasemi¹,

Aaghaie²,

Cudney

2015

International Journal of Quality &Amp; Reliability Management

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Purpose – The purpose of this paper is to present and analyze the current literature related to developing and improving the Mahalanobis-Taguchi system (MTS) and to present the shortcomings related to this method for future research. Design/methodology/approach – In this paper, articles in the literature are classified to give an overview on the MT strategy. For this purpose, 46 articles are considered for classification from 2000 to 2013 on the basis of: MTS contribution area, description of the issue, and results. Findings – In this paper a review on the concepts and operations of the MTS was provided as a new method in the field of pattern recognition, multivariable diagnosis, and forecasting. A large number of studies were performed in recent years consisting of developing MTS and MTS case studies. The analysis of the articles showed the fields of MTS which had more potential for future studies and developing. The comparison of the MTS to other methods and the selection of the normal group for constructing the Mahalanobis space have received the most attention by researchers. In addition, several studies concentrated on the use of other methods instead of design of experiments, finding applications for multiclass MTS and finding an alternative for the SN ratio. Originality/value – This paper contains the publications in the field of MTS chronologically and shows different areas for developing and case studies. It will be useful to researchers and professionals who are interested in pattern recognition, multivariate analysis, and forecasting.

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Classifying tensile strength of HSLA steel: an investigation through neural networks using Mahalanobis Distance

Cited by 2 publications

References 0 publications

Soft computing techniques in advancement of structural metals

Soft computing techniques in advancement of structural metals

Mahalanobis Taguchi system: a review

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