Parameters optimization of selected casting processes using teaching-learning-based optimization algorithm

被引:58
|
作者
Rao, R. Venkata [1 ]
Kalyankar, V. D. [1 ]
Waghmare, G. [1 ]
机构
[1] SV Natl Inst Technol, Dept Mech Engn, Surat 395007, Gujarat, India
关键词
Parameter optimization; Squeeze casting; Die casting; Continuous casting; Mathematical models; TLBO algorithm; HEURISTIC-SEARCH TECHNIQUE; SQUEEZE-CAST; MULTIOBJECTIVE OPTIMIZATION; GENETIC ALGORITHM; HEAT-TRANSFER; DIE; MICROSTRUCTURE; DESIGN; SYSTEM; SPRAY;
D O I
10.1016/j.apm.2014.04.036
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In the present work, mathematical models of three important casting processes are considered namely squeeze casting, continuous casting and die casting for the parameters optimization of respective processes. A recently developed advanced optimization algorithm named as teaching-learning-based optimization (TLBO) is used for the parameters optimization of these casting processes. Each process is described with a suitable example which involves respective process parameters. The mathematical model related to the squeeze casting is a multi-objective problem whereas the model related to the continuous casting is multi-objective multi-constrained problem and the problem related to the die casting is a single objective problem. The mathematical models which are considered in the present work were previously attempted by genetic algorithm and simulated annealing algorithms. However, attempt is made in the present work to minimize the computational efforts using the TLBO algorithm. Considerable improvements in results are obtained in all the cases and it is believed that a global optimum solution is achieved in the case of die casting process. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码:5592 / 5608
页数:17
相关论文
共 50 条
  • [1] Parameters Optimization of Continuous Casting Process Using Teaching-Learning-Based Optimization Algorithm
    Rao, Ravipudi Venkata
    Kalyankar, Vivek D.
    SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING, (SEMCCO 2012), 2012, 7677 : 540 - 547
  • [2] Parameter optimization of machining processes using teaching-learning-based optimization algorithm
    Pawar, P. J.
    Rao, R. Venkata
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2013, 67 (5-8): : 995 - 1006
  • [3] Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm
    Rao, R. Venkata
    Kalyankar, V. D.
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2013, 26 (01) : 524 - 531
  • [4] Erratum to: Parameter optimization of machining processes using teaching-learning-based optimization algorithm
    P. J. Pawar
    R. Venkata Rao
    The International Journal of Advanced Manufacturing Technology, 2013, 67 (5-8) : 1955 - 1955
  • [5] Structural optimization with teaching-learning-based optimization algorithm
    Dede, Tayfun
    Ayvaz, Yusuf
    STRUCTURAL ENGINEERING AND MECHANICS, 2013, 47 (04) : 495 - 511
  • [6] Parameters optimization of fabric finishing system of a textile industry using teaching-learning-based optimization algorithm
    Kumar, Rajiv
    Tewari, P. C.
    Khanduja, Dinesh
    INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS, 2018, 9 (02) : 221 - 234
  • [7] Design optimization of robot grippers using teaching-learning-based optimization algorithm
    Rao, R. Venkata
    Waghmare, Gajanan
    ADVANCED ROBOTICS, 2015, 29 (06) : 431 - 447
  • [8] Multi-objective optimization using teaching-learning-based optimization algorithm
    Zou, Feng
    Wang, Lei
    Hei, Xinhong
    Chen, Debao
    Wang, Bin
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2013, 26 (04) : 1291 - 1300
  • [9] A note on teaching-learning-based optimization algorithm
    Crepinsek, Matej
    Liu, Shih-Hsi
    Mernik, Luka
    INFORMATION SCIENCES, 2012, 212 : 79 - 93
  • [10] Improved Teaching-Learning-Based Optimization Algorithm
    Zhai, Junchang
    Qin, Yuping
    Zhao, Zhen
    Yao, Minghai
    2018 37TH CHINESE CONTROL CONFERENCE (CCC), 2018, : 3112 - 3116