Optimised recrystallisation model using multiobjective evolutionary and genetic algorithms and k-optimality approach

被引：8

作者：

Halder, C. ^{[1
]}

Sitko, M. ^{[2
]}

Madej, L. ^{[2
]}

Pietrzyk, M. ^{[2
]}

Chakraborti, N. ^{[1
]}

机构：

[1] Indian Inst Technol, Dept Met & Mat Engn, Kharagpur 721302, W Bengal, India

[2] AGH Univ Sci & Technol, Dept Appl Comp Sci & Modelling, PL-30059 Krakow, Poland

来源：

MATERIALS SCIENCE AND TECHNOLOGY | 2016年 / 32卷 / 04期

关键词：

Multiobjective optimisation; Recrystallisation; k-optimality; Genetic and evolutionary algorithms; ZN-COATED FE; DIFFERENTIAL EVOLUTION; DEFORMATION;

D O I：

10.1179/1743284715Y.0000000071

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The meta-models are constructed for static recrystallisation of dual phase steels using evolutionary neural nets (EvoNN). Four mutually conflicting objectives-(i) overall kinetics, (ii) grain size, (iii) the amount of strain and (iv) the precipitate volume fraction-are optimised simultaneously using an emerging k-optimal approach incorporated in the EvoNN, using a predator-prey genetic algorithm. The first objective involved minimisation of error with respect to experimental observation. The grain size and the amount of strain were minimised, whereas the precipitate volume fraction was maximised. The aim is to control the recrystallisation process in order to achieve desired material properties of dual phase steel during the final stages of heat treatment.

引用

页码：366 / 374

页数：9

共 32 条

[1] EVALUATION OF COMBINED PARETO MULTIOBJECTIVE DIFFERENTIAL EVOLUTION ON TUNEABLE PROBLEMS [J].

Adeyemo, J. A. ;

Olofintoye, O. O. .

INTERNATIONAL JOURNAL OF SIMULATION MODELLING, 2014, 13 (03) :276-287

[2] Machine Learning Based Predictive Modeling of Machining Induced Microhardness and Grain Size in Ti-6Al-4V Alloy [J].

Arisoy, Yigit M. ;

Ozel, Tugrul .

MATERIALS AND MANUFACTURING PROCESSES, 2015, 30 (04) :425-433

[3] Promise of multiobjective genetic algorithms in coating performance formulation [J].

Chakraborti, N. .

SURFACE ENGINEERING, 2014, 30 (02) :79-82

[4]

Chakraborti N., 2013, Evolutionary Data Driven Modeling. Informatics for Material Science Engineering

[5]

Chakraborti N., 2014, STRATEGIES EVOLUTION

[6] Critical Assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research [J].

Chakraborti, Nirupam .

MATERIALS SCIENCE AND TECHNOLOGY, 2014, 30 (11) :1259-1262

[7] Evolutionary Multiobjective Optimization in Materials Science and Engineering [J].

Coello Coello, Carlos A. ;

Landa Becerra, Ricardo .

MATERIALS AND MANUFACTURING PROCESSES, 2009, 24 (02) :119-129

[8]

Deb K., 2001, WIL INT S SYS OPT

[9] On the optimal solution definition for many-criteria optimization problems [J].

Farina, M ;

Amato, P .

2002 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY PROCEEDINGS, 2002, :233-238

[10] Genetic Programming Evolved through Bi-Objective Genetic Algorithms Applied to a Blast Furnace [J].

Giri, Brijesh Kumar ;

Pettersson, Frank ;

Saxen, Henrik ;

Chakraborti, Nirupam .

MATERIALS AND MANUFACTURING PROCESSES, 2013, 28 (07) :776-782

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