Premium
Data‐Driven Pareto Optimization for Microalloyed Steels Using Genetic Algorithms
Author(s) -
Kumar Aman,
Chakrabarti Debalay,
Chakraborti Nirupam
Publication year - 2012
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.201100189
Subject(s) - elongation , ultimate tensile strength , genetic algorithm , pareto principle , materials science , artificial neural network , algorithm , base (topology) , pareto optimal , evolutionary algorithm , yield (engineering) , biological system , multi objective optimization , computer science , metallurgy , mathematics , mathematical optimization , artificial intelligence , biology , mathematical analysis
A data base was put together for the mechanical properties of microalloyed steels, which contained about 800 entries for ultimate tensile strength (UTS), yield strength (YS), and elongation. Using an evolutionary neural network, based upon a predator–prey genetic algorithms of bi‐objective type, this information was used to construct data‐driven models for UTS, YS, and elongation. The optimum Pareto tradeoffs between these properties were obtained using a multi‐objective genetic algorithm. The results led to some hitherto unexplored steel compositions with optimum properties. Some such steels were actually cast and the experimentally observed property values were found to be well in accord with the predicted results.