SCWOA: an efficient hybrid algorithm for parameter optimization of multi-pass milling process

被引:39
|
作者
Khalilpourazari, Soheyl [1 ]
Khalilpourazary, Saman [2 ]
机构
[1] Kharazmi Univ, Dept Ind Engn, Fac Engn, Tehran, Iran
[2] Urmia Univ Technol, Dept Mech Engn, Fac Engn, Orumiyeh, Iran
关键词
Parameter optimization; sine-cosine algorithm; whale optimization algorithm; multi-pass milling process; non-linear optimization;
D O I
10.1080/21681015.2017.1422040
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In order to minimize total production time in multi-pass milling process, selecting optimal values of the process parameters is of great importance. The parameter optimization model of multipass milling process is a Constrained Non-Linear Programming formulation. Due to nonlinearity and complexity of the mathematical model, developing new solution methodologies, which can provide efficient solutions, is essential. For this purpose, in this paper a novel hybrid algorithm called Sine-Cosine Whale Optimization Algorithm is proposed for parameter optimization problem of multi-pass milling process in order to minimize total production time. The SCWOA uses exploration and exploitation abilities of the two basic algorithms to achieve better solutions. To show efficiency of the proposed algorithm, an experimental study is carried out and the result of the proposed algorithm is compared to the ones in the literature. The findings revealed that SCWOA provides promising solutions which result in significantly lower production time.
引用
收藏
页码:135 / 147
页数:13
相关论文
共 50 条
  • [31] Evaluating Process Parameters of Multi-Pass Turning Process Using Hybrid Genetic Simulated Swarm Algorithm
    Gayatri, R.
    Baskar, N.
    JOURNAL OF ADVANCED MANUFACTURING SYSTEMS, 2015, 14 (04) : 215 - 233
  • [32] Optimisation of multi-pass milling using genetic algorithm and genetic simulated annealing
    Wang, ZG
    Wong, YS
    Rahman, M
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2004, 24 (9-10): : 727 - 732
  • [33] Optimisation of multi-pass milling using genetic algorithm and genetic simulated annealing
    Z.G. Wang
    Y.S. Wong
    M. Rahman
    The International Journal of Advanced Manufacturing Technology, 2004, 24 : 727 - 732
  • [34] Multi-objective optimization of multi-pass face milling using particle swarm intelligence
    Yang, Wen-an
    Guo, Yu
    Liao, Wenhe
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2011, 56 (5-8): : 429 - 443
  • [35] Multi-objective optimization of multi-pass face milling using particle swarm intelligence
    Wen-an Yang
    Yu Guo
    Wenhe Liao
    The International Journal of Advanced Manufacturing Technology, 2011, 56 : 429 - 443
  • [36] Selection and optimization of process parameters during multi-pass laser bending process
    Nikhil, P.
    Kumar, Vikash
    Babu, K. Arun
    OPTICS AND LASER TECHNOLOGY, 2025, 184
  • [37] Selection of optimal conditions in multi-pass face-milling using a genetic algorithm
    Shunmugam, MS
    Reddy, SVB
    Narendran, TT
    INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2000, 40 (03): : 401 - 414
  • [38] Prediction of residual stress regeneration in multi-pass milling
    Fergani, Omar
    Jiang, Xiaohui
    Shao, Yamin
    Welo, Torgeir
    Yang, Jianguo
    Liang, Steven
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2016, 83 (5-8): : 1153 - 1160
  • [39] Performance-based optimization of multi-pass face milling operations using Tribes
    Onwubolu, Godfrey C.
    INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2006, 46 (7-8): : 717 - 727
  • [40] Prediction of residual stress regeneration in multi-pass milling
    Omar Fergani
    Xiaohui Jiang
    Yamin Shao
    Torgeir Welo
    Jianguo Yang
    Steven Liang
    The International Journal of Advanced Manufacturing Technology, 2016, 83 : 1153 - 1160