Intelligent design of feeders for castings by augmenting CAD with genetic algorithms
The design of feeders for castings has been mostly based on empirical rules, heuristics and past experience. This paper presents a novel approach to the problem by augmenting genetic algorithms with CAD to optimize the feeder dimensions. The synergy of CAD and genetic algorithms generates a class of optimal feeders that also satisfy the standard criteria laid down in literature. A CAD platform is used as the front-end to create the solid model of the casting, divide it into feeding sections and calculate the volume and surface area of each feeding section. The feeding sections are made such that each section satisfies the feedpath requirement and ensures a critical modulus gradient. The genetic algorithm works on this data to design feeders keeping the yield as the optimization parameter and simultaneously satisfying the constraints of minimum volume and modulus.
cad genetic algorithm feedpath modulus gradient yield volume criterion modules criterion feeding units optimization of feeder dimensions