This SBIR Phase II research addresses a computational environment for optimal dimensional management of components and assemblies. The approach couples a genetic optimization method with a Monte Carlo based tolerance test. Both tolerance ranges and nominal dimensions are included as design variables. The goal is to generate the minimum cost assembly which meets all design and manufacturing considerations, which may be formed as constraints or as multiple objectives. Successful application of the technology will dramatically reduce product development times and manufacturing cost. Current tolerance analysis software does not allocate tolerances or modify nominal dimensions in an optimal fashion. Traditional nonlinear programming methods are insufficiently robust to solve the highly nonlinear, multimodal problem resulting from the consideration of a "real" component or assembly. The combination of the genetic algorithm with a tolerance analysis package has the advantage of efficient operation on a parallel or distributed computing network. This research is directed toward increasing algorithmic efficiency and reliability for both the genetic optimized and the tolerance model simulation.
