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Designing High Strength Multi-phase Steel for Improved Strength-Ductility Balance Using Neural Networks and Multi-objective Genetic Algorithms
The properties of steels depend in a complex way on their composition and heat treatment and neural networks have therefore recently been widely used for capturing these relationships. Two differentExpand
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Informatics-Based Uncertainty Quantification in the Design of Inorganic Scintillators
A soft computing platform, integrating rough sets, fuzzy inferences, and genetic algorithms, is used to develop a series of design rules as a guideline for optimizing inorganic scintillator materialsExpand
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Evolution of glass forming ability indicator by genetic programming
Abstract A symbolic regression technique has been employed to evolve the functional relationship among the characteristic transformation temperatures, viz. glass transition temperature (Tg), onsetExpand
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Genetic algorithm based optimization for multi-physical properties of HSLA steel through hybridization of neural network and desirability function
A genetic algorithm (GA) based optimization of the composite desirability of the tensile properties of thermomechanically processed high strength low alloy (HSLA) steel plates is proposed. TheExpand
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In silico Design of High Strength Aluminium Alloy Using Multi-objective GA
TLDR
A genetic algorithm based multi-objective optimization is employed using genetic algorithm, for designing novel age-hardenable aluminium alloy with improved properties. Expand
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Investigating the role of metallic fillers in particulate reinforced flexible mould material composites using evolutionary algorithms
TLDR
Multi-objective optimizations of maximizing equivalent thermal conductivity and minimizing effective modulus of elasticity of composite mould materials are conducted using evolutionary algorithms (EAs). Expand
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Simulating Time Temperature Transformation Diagram of Steel Using Artificial Neural Network
Design and development of steel is essentially governed by the Time-Temperature-Transformation (TTT) diagram. The diagram predicts the phase evolution during isothermal transformation schedules for aExpand
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Genetic Algorithms in Optimization of Strength and Ductility of Low-Carbon Steels
A comparative study between the conventional goal attainment strategy and an evolutionary approach using a genetic algorithm has been conducted for the multiobjective optimization of the strength andExpand
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Identification of Factors Governing Mechanical Properties of TRIP-Aided Steel Using Genetic Algorithms and Neural Networks
TLDR
Mechanical properties of transformation induced plasticity (TRIP)-aided multiphase steels are modeled by neural networks using two methods of reducing the network connectivity, viz. a pruning algorithm and a predator prey algorithm, to gain understanding on the impact of steel composition and treatment. Expand
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Effect of copper and microalloying (Ti, B) addition on tensile properties of HSLA steels predicted by ANN technique
Abstract The present study aims to model the composition, process and properties of Cu plus Ti, B microalloyed low carbon steels by using the artificial neural network (ANN) technique. This tool isExpand
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