Design of silicone rubber according to requirements based on the multi-objective optimization of chemical reactions

The explicit expression between composition and mechanical properties of silicone rubber was derived from the physics of polymer elasticity, the implicit expression among material composition, reaction conditions and reaction efficiency was obtained from chemical thermodynamics and kinetics, and then an implicit multi-objective optimization model was constructed. Genetic algorithm was applied to optimize material composition and reaction conditions, and the finite element method of cross-linking reaction processes was used to solve multi-objective functions, on the basis of which a new optimization methodology of cross-linking reaction processes was established. Using this methodology, rubber materials can be designed according to pre-specified requirements.

[1]  Ajay K. Ray,et al.  Multiobjective optimization of an industrial wiped film poly(ethylene terephthalate) reactor: some further insights , 2001 .

[2]  Diffusion and Chemical Reaction by the Finite Element Method , 2003 .

[3]  Taizo Hanai,et al.  Analysis of initial conditions for polymerization reaction using fuzzy neural network and genetic algorithm , 2003, Comput. Chem. Eng..

[4]  R. J. Duh,et al.  Optimization of cure kinetics parameter estimation for Structural Reaction Injection Molding/Resin Transfer Molding , 2001 .

[5]  Yuxi Jia,et al.  Finite element simulation of mechanical property evolvement of silicone rubber in the hot vulcanizing process , 2004 .

[6]  Douglas S. Galvao,et al.  Designing conducting polymers using genetic algorithms , 2002 .

[7]  Guoqun Zhao,et al.  Investigation of computer-aided engineering of silicone rubber vulcanizing (I)—vulcanization degree calculation based on temperature field analysis , 2003 .

[8]  Roderick V. N. Melnik.  Computationally efficient algorithms for modelling thermal degradation and spiking phenomena in polymeric materials , 2003, Comput. Chem. Eng..

[9]  Sunil C. Joshi,et al.  Three-dimensional finite-element/nodal-control-volume simulation of the pultrusion process with temperature-dependent material properties including resin shrinkage , 2001 .

[10]  Cliff T. Ragsdale,et al.  Combining a neural network with a genetic algorithm for process parameter optimization , 2000 .

[11]  N. Pantelelis.  Optimised cure cycles for resin transfer moulding , 2003 .

[12]  Guoqun Zhao,et al.  Investigation of computer-aided engineering of silicone rubber vulcanizing (II)—finite element simulation of unsteady vulcanization field , 2002 .

[13]  E. Pardo,et al.  Finite element model of interdiffusion and reaction in polydisperse polymer blends , 2000 .

[14]  Christodoulos A. Floudas,et al.  Optimization of polymer synthesis through distributed control of polymerization conditions , 2002 .

[15]  Harry H. Hilton,et al.  A finite element approach for cure simulation of thermosetting matrix composites , 1997 .

[16]  C. K. Kwong,et al.  A Hybrid Neural Network and Genetic Algorithm Approach to the Determination of Initial Process Parameters for Injection Moulding , 2001 .