An iterative method to optimise non-linear production processes is described. Compared to classical design-of-experiment methods, it starts with a small number of experiments. A preliminary data-based model is developed, from which a vector of process parameters with (potentially) improved performance is calculated. The result of the experiment carried out with this new process parameters enlarge the database and result in an improved process model. The iteration is stopped if the requirements on the product properties are fulfilled. The method is applied to the selective laser sintering process of titanium powder and the goal of the optimisation is to produce a ring with prescribed geometry. One of the novelty of our approach is to feed the data base with experimental results obtained from process simulation: our idea is to use the sintering machine only once and to produce a part which is “first time right”. The simulation is based on a 3D finite element model of the selective laser sintering process. The input data are the various process parameters and the model is sufficiently detailed to predict the density and the bounding quality (sintering potential) of the manufactured part. Keywords: Selective laser sintering, Finite Element method, neural networks, process optimisatio
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