Optimisation of CRTM

More generally, the process optimisation problem is solved using an optimisation solver, which interacts with the process simulator to minimise the objective function. The optimisation solver can be based on any one of many optimisation techniques. One group of possible optimisation techniques are the gradient-search methods. These methods rely on analytic or semi-analytic expressions for the objective function and objective function 

As a precursor to the optimisation, an optimum mesh density for SimLCM needs to be found for the particular problem (geometry, materials, etc.) under study. This involves finding a mesh which is fine enough to ensure an adequately converged objective function for a wide range of sets of the design variables P, v, but coarse enough not to increase computa- 

14 The Genetic Algorithm (a) an initial population and the elimination of inefficient solutions (hollow circles), (b) generation of a new population through reproduction and mutation. 

In Fig. 11.16 are shown the first three generations output by the GA for the helmet problem. It can be seen how each successive generation dominates the previous generation, in terms of lower process time and lower 

15 A fireman s helmet (a) finite element mesh, (b) SimLCM output for normal stress distribution during mould closure. (Source Reprinted from reference 38. Copyright (2009), with permission from Elsevier Ltd.) 

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