Premium
Research on optimum heating system design for rapid thermal response mold with electric heating based on response surface methodology and particle swarm optimization
Author(s) -
Wang Guilong,
Zhao Guoqun,
Guan Yanjin
Publication year - 2010
Publication title -
journal of applied polymer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.575
H-Index - 166
eISSN - 1097-4628
pISSN - 0021-8995
DOI - 10.1002/app.32771
Subject(s) - materials science , response surface methodology , electric heating , molding (decorative) , mold , central composite design , cavity wall , heating element , rod , thermal , finite element method , heat transfer , composite material , particle swarm optimization , mechanical engineering , mechanics , structural engineering , thermodynamics , mathematics , engineering , algorithm , pathology , medicine , statistics , alternative medicine , physics
A new electric‐heating rapid thermal response (RTR) mold with floating cavity/core for rapid heat cycle molding is investigated in this study. Process principles of Rapid heat cycle molding (RHCM) with such new electric‐heating mold are discussed and presented. Response surface methodology (RSM) is employed to develop mathematical relationships between layout of the heating elements and heating efficiency, temperature uniformity and structural strength of the floating cavity. Three explanatory variables including half distance between two adjacent heating rods, spacing between heating rods and cavity surface, and the diameter of the heating rod are used to describe the layout and scale of the heating elements. The response variables involving required heating time, maximum cavity surface temperature, and maximum von‐Mises stress are used to characterize heating efficiency, temperature uniformity, and structural strength of the floating cavity, respectively. Central composite design (CCD) method is used for factorial experiments. Finite element analyses are conducted for combination of explanatory parameters to acquire the corresponding values of the response variables. Three predictive models for the response variables are created by regression analysis. Analysis of variance (ANOVA) is used to check their accuracy. These response surface models are interfaced with an effective particle swarm algorithm for the optimum heating system design of the electric‐heating RTR mold. The developed optimum method is then used for the design of the floating electric‐heating cavity for an actual industrial product. The following heat transfer analysis results show that the temperature distribution uniformity of the cavity surface is greatly improved with the optimal cavity structure and layout of heating rods. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011