CIESC Journal ›› 2018, Vol. 69 ›› Issue (10): 4449-4455.doi: 10.11949/j.issn.0438-1157.20180530

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Optimization strategy of weld line assisted by air traps improvement based on Kriging and NSGA-Ⅱ

WANG Menghan, TU Shunli, YU Chunli   

  1. College of Material Science and Engineering, Chongqing University, Chongqing 400030, China
  • Received:2018-05-22 Revised:2018-07-23 Online:2018-10-05 Published:2018-07-27
  • Supported by:

    supported by the Fundamental Research Funds for the Central Universities (CDJZR14130006).


Based on forming mechanism of weld line, effects of air traps condition and melt temperature on bonding quality of weld line were taken into consideration. Thus, an optimization strategy of weld line assisted by improving air traps condition was proposed. And a multi-objective evaluation system was established to evaluate bonding quality of weld line from two aspects, namely area of air traps and temperature at flow front in weld line. A multi-objective optimization methodology based on Kriging surrogate model and nondominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) was proposed to optimize process parameters. Optimization of weld line of a perforated plate was given as an example. Latin hypercube sampling (LHS) was utilized to arrange experiments. A surrogate model based on Kriging was established and verified to map relationship between process parameters and optimization objectives. Then NSGA-Ⅱ was used to gain Pareto Front and optimal solution of multi-objective optimization problem. Simulated and practical confirmation experiments were carried out to verify validity of obtained optimal solution. Results show that established multi-objective optimization methodology is effective to multi-objective optimization problems of weld lines. And proposed optimization strategy of weld line assisted by improving air traps condition was proved to be highly effective.

Key words: molding, weld lines, air traps, multi-objective optimization, numerical simulation, Kriging model, NSGA-Ⅱ

CLC Number: 

  • TQ320.66

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