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Resin transfer molding process optimization using numerical simulation and design of experiments approach
Author(s) -
Gou Jihua,
Zhang Chuck,
Liang Zhiyong,
Wang Ben,
Simpson James
Publication year - 2003
Publication title -
polymer composites
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 82
eISSN - 1548-0569
pISSN - 0272-8397
DOI - 10.1002/pc.10000
Subject(s) - transfer molding , mold , fractional factorial design , materials science , process (computing) , molding (decorative) , factorial experiment , computer simulation , design of experiments , process variable , volume fraction , process design , mechanical engineering , composite material , computer science , process engineering , mathematics , engineering , simulation , process integration , statistics , machine learning , operating system
The art of resin transfer molding (RTM) process optimization requires a clear understanding of how the process performance is affected by variations in some important process parameters. In this paper, maximum pressure and mold filling time of the RTM process are considered as characteristics of the process performance to evaluate the process design. The five process parameters taken into consideration are flow rate, fiber volume fraction, number of gates, gate location, and number of vents. An integrated methodology was proposed to investigate the effects of process prameters on maximum pressure and mold filling time and to find the optimum processing conditions. The method combines numerical simulation and design of experiments (DOE) approach and is applied to process design for a cylindrical composite part. Using RTM simulation, a series of numerical experiments were conducted to predict maximum pressure and mold filling time of the RTM process. A half‐fractional factorial design was conducted to identify the significant factors in the RTM process. Furthermore, the empirical models and sensitivity coefficients for maximum pressure and mold filling time were developed. Comparatively close agreements were found among the empirical approximations, numerical simulations, and actual experiments. These results were further utilized to find the optimal processing conditions for the example part.