A genetic algorithm for optimization of laminated dies manufacturing

Laminated tooling is based on taking sheets of metal and stacking them to produce the final product, after cutting each layer profile using laser or other techniques. CNC machining removes the extra material and brings the final product to specific tolerances. To reduce the cost of laminated dies manufacturing, the amount of the extra material and the number of slices must likewise be reduced. This is considered an optimization problem, which can be solved by genetic algorithms (G.A.). However, in most instances, premature convergence prevents the system from searching for a more optimal solution, a common problem in many G.A. applications. To address this problem, a new niching method is presented in this paper. Using the proposed method, results show not only a significant improvement in the quality of the optimum solution but also a substantial reduction in the processing time.