Comparison of Artificial Neural Networks with Gaussian Processes to Model the Yield Strength of Nickel-base Superalloys

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概要

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• The abilities of artificial neural networks and Gaussian processes to model the yield strength of nickel-base superalloys as a function of composition and temperature have been compared on the basis of simple well-known metallurgical trends (influence of Ti, Al, Co, Mo, W, Ta, of the Ti/Al ratio, γ volume fraction and the testing temperature). The methodologies are found to give similar results, and are able to predict the behaviour of materials that were not shown to the models during their creation. The Gaussian process modelling method is the simpler method to use, but its computational cost becomes larger than that of neural networks for large data sets.

• 社団法人 日本鉄鋼協会の論文

著者

• Mackay D.

  Department Of Physics Cavendish Laboratory University Of Cambridge

• Bhadeshia H.

  Department Of Materials Science And Metallurgy University Of Cambridge

• TANCRET F.

  Department of Materials Science and Metallurgy, University of Cambridge

• Tancret F.

  Department Of Materials Science And Metallurgy University Of Cambridge

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