Genetic Algorithm–Based Design and Development of Particle-Reinforced Silicone Rubber for Soft Tooling Process

Abstract

In order to enhance the solidification rate of soft tooling process, design of a silicone rubber composite mold material is carried out based on multiobjective optimization (MOO) of conflicting objectives. The elitist nondominated sorting genetic algorithm (NSGA-II), a genetic algorithm–based MOO tool, is used to find the optimum parameters first by obtaining the Pareto-optimal front and then selecting a single solution or a small set of solutions for manufacturing applications using a suitable multi-criterion decision making technique. Based on the optimal design parameters, an experimental study in soft tooling process is carried out in particle-reinforced silicone, and it is observed that the solidification time is minimized appreciably keeping the same advantages of soft tooling process.

Keywords:

• Composites
• Evolutionary algorithm
• Multiobjective optimization
• Silicone rubber
• Soft tooling
• Solidification time