Genetic Algorithm based Resistive Susceptor Design for Uniform Heating During the Induction Bonding Process

In this research work, cut mesh design optimization for the induction bonding process using genetic algorithms (GAs) is investigated to solve the problem of nonuniform heating, which leads to nonuniform temperature fields and temperature gradients exceeding the process window required for bonding. Cut patterns in the metal mesh can redirect the magnetically induced electric currents generated thus changing the temperature distribution. In this work, the heat generation model for determining current and heat generation distribution for a given coil and mesh size, coded as a Mathematica function, was coupled with a simple genetic algorithm. The cost function to be minimized by the GA was the ratio of the maximum heat generation in the mesh to the minimum heat generation. Two studies were performed with the GA-based design optimization: the first with a six sided square mesh and the second using a ten sided square mesh. The best cut mesh designs obtained from the GA were compared with the globally optimal designs, where available, and with the baseline mesh. The GA could not reach the global optima due to the complex nature of the design search space. However, it was determined that the GA was able to reduce the variations in heat generation in the mesh for all cases and delivered significant improvements over the baseline case in reasonable computational time, evaluating less than 2% of the possible cut mesh patterns. Thus the genetic algorithm based design optimization was proven to be a computationally efficient tool in the generation of good cut mesh designs for the induction bonding process.