engleski

Evolutionary Numerical Workflow for Large-Scale Feature-based Remodeling and Shape Optimization

Evolutionary parametric shape optimization relies on generic shape models which provide sufficient 3D geometric modeling freedom while being modest in terms of the number of shape variables and hence dimensionality of search space. The number of shape parameters needs to be reduced towards computational efficiency in shape optimization. However, this may imply loss of generality or local modeling capacity, potentially also bias towards unintentionally predefined 3D shape templates, all of which may result in sub-optimal shapes. This paper develops a novel approach in the form of a conceptual intelligent system based on integral parametric surfaces representing objects. The computational efficiency of the models is established by engaging a sparing multitude of shape parameters in evolutionary optimization. Sufficiently generic and unbiased modeling capacity is obtained by the adaptability of the approach, as it can adapt to the features of the current state in optimization iterations. Decomposition of the integral surfaces into shape partitions is implemented based on 3D pattern recognition (edges, peaks, etc).

Evolutionary Shape Optimization ; Efficient 3D Parameterization ; Intelligent Numerical Workflow ; Adaptive Geometric Modeling ; 3D feature recognition ; Partitions

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