Abstract: Systems and methods for machine-learning-based additive manufacturing use a machine-learning model to process an input vector describing a new part transaction, thereby providing part optimization outputs and command initiation outputs to configure additive manufacturing of a new part. The machine-learning model is trained based on entries in a user experience database, each entry in the user experience database including data defining requirements for an additively manufactured part previously fabricated or attempted to be fabricated, specifications describing an additive manufacturing fabrication device, a selection of a raw material type fed to the fabrication device for fabrication of the additively manufactured part, a fabrication spatial orientation of the additively manufactured part within the fabrication device, a fabrication slicing resolution of the additively manufactured part, and a toolpath taken by the fabrication device in fabricating the additively manufactured part.

Abstract: Systems and methods for machine-learning-based additive manufacturing use a machine-learning model to process an input vector describing a new part transaction, thereby providing part optimization outputs and command initiation outputs to configure additive manufacturing of a new part. The machine-learning model is trained based on entries in a user experience database, each entry in the user experience database including data defining requirements for an additively manufactured part previously fabricated or attempted to be fabricated, specifications describing an additive manufacturing fabrication device, a selection of a raw material type fed to the fabrication device for fabrication of the additively manufactured part, a fabrication spatial orientation of the additively manufactured part within the fabrication device, a fabrication slicing resolution of the additively manufactured part, and a toolpath taken by the fabrication device in fabricating the additively manufactured part.