Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 638-643.doi: 10.11896/jsjkx.210300080

• Interdiscipline & Application • Previous Articles     Next Articles

Prediction Model of Bubble Dissolution Time in Selective Laser Sintering Based on IPSO-WRF

ZHANG Tian-rui1,2, WEI Ming-qi1, GAO Xiu-xiu1   

  1. 1 School of Mechanical Engineering,Shenyang University,Shenyang 110041,China
    2 School of Mechanical Engineering and Automation,Northeastern University,Shenyang 110819,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:ZHANG Tian-rui,born in 1985,Ph.D,associate professor,master supervisor.His main research interests include operational reasearch and optimization.
    WEI Ming-qi,born in 1995,postgraduate master.His main research interests include quality prediction and supply chain management.
  • Supported by:
    Major Master of MIT(201675514) and National Natural Science Foundation of Liaoning Province(20180551001).

Abstract: In order to solve the problem of mass defects caused by bubbles during selective laser sintering (SLS),a weighted random forest (WRF) prediction method based on improved particle swarm optimization algorithm (IPSO) was proposed to predict the dissolution time of bubbles effectively.In this method,two key parameters of WRF,the number of split attributes and the number of decision trees,were optimized by IPSO algorithm to construct the prediction model of IPSO-WRF.Numerical examples show that compared with the prediction results of PSO-RF and PSO-KELM prediction models,IPSO-WRF prediction model based on the same training samples and test samples can get the output results of bubble dissolution time with smaller error and closer to the actual value.MAE,MAPE and RMSE indexes show that IPSO-WRF prediction model has higher nonlinear fitting ability and prediction accuracy than PSO-RF model and PSO-KELM model.Finally,the most significant input parameters affecting the bubble dissolution time are determined by sensitivity analysis,which provides a theoretical basis for the development of SLS technology.

Key words: Bubble dissolution time, Improved particle swarm optimization algorithm, Parameter sensitivity analys, Selective laser sintering, Weighted random forest

CLC Number: 

  • TH164
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