Neural Networks Approach to Optimization of Steel Alloys Composition

Abstract : The paper presents modeling of steels strength characteristics in dependence from their alloying components quantities using neural networks as nonlinear approximation functions. Further, for optimization purpose the neural network models are used. The gradient descent algorithm based on utility function backpropagation through the models is applied. The approach is aimed at synthesis of steel alloys compositions with improved strength characteristics by solving multi-criteria optimization task. The obtained optimal alloying compositions fall into martenzite region of steels. They will be subject of further experimental testing in order to synthesize new steels with desired characteristics.
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Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.315-324, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_36〉
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Petia Koprinkova-Hristova, Nikolay Tontchev, Silviya Popova. Neural Networks Approach to Optimization of Steel Alloys Composition. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.315-324, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_36〉. 〈hal-01571366〉

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