Abstract: Deep learning approaches and systems are described to control the process of casting physical objects. A neural network, operating on one or more processors of a server or distributed computing resources and maintained in one or more data storage devices, is trained to recognize relationships between the target digital representation and the resulting metal parts that are cast, and a number of specific approaches are described herein to overcome technical issues in relation to misalignments between reference points, among others. These deep learning approaches are then used for generation of command or control signals which modify how the casting process is conducted. Command or control signals can be used to modify how a cast mold is made, to modify environmental variables, to modify manufacturing parameters, and combinations thereof.

Abstract: Deep learning approaches and systems are described to control the process of casting physical objects. A neural network, operating on one or more processors of a server or distributed computing resources and maintained in one or more data storage devices, is trained to recognize relationships between the target digital representation and the resulting metal parts that are cast, and a number of specific approaches are described herein to overcome technical issues in relation to misalignments between reference points, among others. These deep learning approaches are then used for generation of command or control signals which modify how the casting process is conducted. Command or control signals can be used to modify how a cast mold is made, to modify environmental variables, to modify manufacturing parameters, and combinations thereof.