Neural Networks Analysis of Steel Plate Processing Augmented by Multi‐objective Genetic Algorithms | Zendy

Pettersson F. | Zendy; Chakraborti N. | Zendy; Singh S.B. | Zendy

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Neural Networks Analysis of Steel Plate Processing Augmented by Multi‐objective Genetic Algorithms

Author(s) -

Pettersson F.,

Chakraborti N.,

Singh S.B.

Publication year - 2007

Publication title -

steel research international

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.603

H-Index - 49

eISSN - 1869-344X

pISSN - 1611-3683

DOI - 10.1002/srin.200706302

Subject(s) - artificial neural network , ultimate tensile strength , pruning , genetic algorithm , set (abstract data type) , variable (mathematics) , algorithm , yield (engineering) , process (computing) , computer science , mathematics , biological system , engineering , structural engineering , materials science , artificial intelligence , mathematical optimization , composite material , biology , mathematical analysis , programming language , operating system , agronomy

An earlier neural network analysis of processing of steel plates through hot rolling was subjected to a further refined analysis through some flexible neural networks that evolved using a multi‐objective predator‐prey genetic algorithm. The original data set expressing the Yield Strength and Ultimate Tensile Strength of the rolled slabs in terms of a total of 108 process variables were subjected to a systematic pruning through this evolutionary approach, till the nitrogen content of the steel emerged as the most significant input variable. A theoretical explanation is provided for this slightly unexpected result.

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