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- W2116567372 abstract "An intelligent offline optimisation system for feed and spindle speed in three-axis machining of sculptured surfaces is presented. Optimisation is performed by a standard genetic algorithm seeking, in this instance, to minimise machining force variation along designated sections of the tool path. Machining force is modelled realistically, rather than mechanistically, by dedicated artificial neural networks trained with a low number of experimental data that are obtained from the very equipment on which machining is to be carried out. The artificial neural networks employ the material volume removed per tool revolution as calculated using a standard solid modelling engine. The artificial neural network model architecture is optimally determined in a rigorous way as opposed to often followed trial-and-error approaches, and it is made use of directly in the evaluation function of the optimiser. This framework is flexible enough to enable definition of constraints, additional evaluation criteria and desirable force patterns to guide optimisation. Effectiveness of the approach is successfully demonstrated for finish machining a hemispherical part." @default.
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- W2116567372 date "2013-11-26" @default.
- W2116567372 modified "2023-09-25" @default.
- W2116567372 title "Offline flexible optimisation of feed and speed in computer numerical control machining of sculptured surfaces exploiting dedicated cutting force metamodels" @default.
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