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.

Research highlights
► In this research a new method to prevent premature convergence through population in genetic algorithm is presented.
► The proposed method then applied to a laminated tool optimization process.
► Results show a considerable improvement on the quality of solution. Also the results show that the suggested method could reach a more reliable solution in less time.