Apparatus and method for boundary learning optimization

An apparatus for boundary learning optimization on flexure systems, the apparatus comprising:

• (a) an artificial neural network having an input layer, multiple intermediate layers and an output layer;
• (b) a processor configured for training said artificial neural network and analyzing output from models of parameterized flexure from said artificial neural network;
• and
• (c) a non-transitory memory storing instructions executable by the processor;
• (d) wherein said instructions, when executed by the processor, perform one or more steps comprising:
• (i) training said artificial neural network to provide sufficiently accurate models of parameterized flexure using a minimal number of numerically generated performance solutions generated from different design instantiations of topologies;
• (ii) plotting a performance boundary of a desired topology using models output by said artificial neural network in an optimization step on said processor;
• and
• (iii) wherein said apparatus identifies the boundaries of performance capabilities achieved by general flexure system topologies if their geometric parameters are varied from smallest allowable feature sizes to largest geometrically compatible feature sizes for given constituent materials;
• (e) whereby boundaries of performance capabilities plotted by said apparatus fully define the design spaces of flexure systems toward allowing designers to visually identify which geometric versions of their synthesized topologies best achieve desired combinations of performance capabilities;
• and
• (f) wherein said apparatus trains neural networks for creating models based on the results of numerically generated data.