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Here we present a solution for practically demonstrating path-aware routing and visualizing a self-driving network.

In additive manufacturing, choice of process parameters for a given material and geometry can result in porosities in the build volume, which can result in scrap.

Distortion generated during additive manufacturing of metallic components affect the build as well as the baseplate geometries. These distortions are significant enough to disqualify components for functional purposes.

For additive manufacturing of large-scale parts, significant distortion can result from residual stresses during deposition and cooling. This can result in part scraps if the final part geometry is not contained in the additively manufactured preform.

In additive manufacturing large stresses are induced in the build plate and part interface. A result of these stresses are deformations in the build plate and final component.

Electrochemistry synthesis and characterization testing  typically occurs manually at a research facility.

Materials produced via additive manufacturing, or 3D printing, can experience significant residual stress, distortion and cracking, negatively impacting the manufacturing process.

Quantifying tool wear is historically challenging task due to variable human interpretation.  This capture system will allow for an entire side and the complete end of the cutting tool to be analyzed.

This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.