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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.

The lack of real-time insights into how materials evolve during laser powder bed fusion has limited the adoption by inhibiting part qualification.  The developed approach provides key data needed to fabricate born qualified parts.

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.

A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.

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.

We present the design, assembly and demonstration of functionality for a new custom integrated robotics-based automated soil sampling technology as part of a larger vision for future edge computing- and AI- enabled bioenergy field monitoring and management technologies called

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.

Creating a framework (method) for bots (agents) to autonomously, in real time, dynamically divide and execute a complex manufacturing (or any suitable) task in a collaborative, parallel-sequential way without required human interaction.

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