Journal ArticleDOI
Designing dual-phase steels with improved performance using ANN and GA in tandem
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In this article, artificial neural network (ANN) and multi-objective genetic algorithm (GA) are employed in tandem to design dual-phase steel with improved performance, where six different mechanical properties are modeled and optimized for simultaneous enhancement of strength and ductility.About:
This article is published in Computational Materials Science.The article was published on 2019-02-01. It has received 41 citations till now.read more
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
Dynamic differential annealed optimization: New metaheuristic optimization algorithm for engineering applications
TL;DR: A novel optimization algorithm which can be used to solve a wide range of mathematical optimization problems where the global minimum or maximum is required is proposed and is designated dynamic differential annealed optimization (DDAO).
Journal ArticleDOI
A review of recent progress in mechanical and corrosion properties of dual phase steels
TL;DR: In this article, a review of the processing routes to obtain the ferritic-martensitic microstructures, parameters of intercritical annealing (IA) treatment, primary thermomechanical treatments, and post processing of dual phase (DP) steels is presented.
Journal ArticleDOI
Processing Route Effects on the Mechanical and Corrosion Properties of Dual Phase Steel
TL;DR: In this article, the mechanical and corrosion behaviors of low carbon DP steel were studied based on different processing routes: (1) intercritical annealing (IA), (2) step quenching (SQ) via austenitization and quick transferring of the sample to the second furnace, and (3) slow SQ via furnace cooling to the desired temperature.
Journal ArticleDOI
Machine learning for design, phase transformation and mechanical properties of alloys
TL;DR: In this paper, a review of the applications of machine learning to various aspects of materials design, processing, characterisation, and some aspects of fabrication and environmental impact evaluation is presented.
References
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Journal ArticleDOI
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Book
Fundamentals of artificial neural networks
TL;DR: In this article, the authors provide a systematic account of artificial neural network paradigms by identifying the fundamental concepts and major methodologies underlying most of the current theory and practice employed by neural network researchers.
Journal ArticleDOI
An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data
TL;DR: A robust comparison of different methodologies for assessing variable importance in neural networks that can be generalized to other data and from which valid recommendations can be made for future studies is provided.