A system for additive metal manufacturing, including a deposition mechanism, a translation mechanism mounting the deposition mechanism to the working volume, and a stage. A method for additive metal manufacturing including: selectively depositing a material carrier within the working volume; removing an additive from the material carrier; and treating the resultant material.

Methods for control of post-design free form deposition processes or joining processes are described that utilize machine learning algorithms to improve fabrication outcomes. The machine learning algorithms use real-time object property data from one or more sensors as input, and are trained using training data sets that comprise: i) past process simulation data, past process characterization data, past in-process physical inspection data, or past post-build physical inspection data, for a plurality of objects that comprise at least one object that is different from the object to be fabricated; and ii) training data generated through a repetitive process of randomly choosing values for each of one or more input process control parameters and scoring adjustments to process control parameters as leading to either undesirable or desirable outcomes, the outcomes based respectively on the presence or absence of defects detected in a fabricated object arising from the process control parameter adjustments.

Techniques for generating a support structure for an object are provided. In some embodiments, one or more regions of the object are identified as one or more regions to which mechanical support is to be provided and one or more support points within at least a first region of the one or more regions are identified. A support structure may be generated for the object that comprises one or more support tips coupled to the object at the one or more support points, where the support tips being generated based at least in part on a direction normal to the surface of the object at the respective support point. Techniques for providing visual feedback to a user relating to an amount of support that a support structure is expected to provide during fabrication are also provided.

Methods for control of post-design free form deposition processes or joining processes are described that utilize machine learning algorithms to improve fabrication outcomes. The machine learning algorithms use real-time object property data from one or more sensors as input, and are trained using training data sets that comprise: i) past process simulation data, past process characterization data, past in-process physical inspection data, or past post-build physical inspection data, for a plurality of objects that comprise at least one object that is different from the object to be fabricated; and ii) training data generated through a repetitive process of randomly choosing values for each of one or more input process control parameters and scoring adjustments to process control parameters as leading to either undesirable or desirable outcomes, the outcomes based respectively on the presence or absence of defects detected in a fabricated object arising from the process control parameter adjustments.

A system for additive metal manufacturing, including a deposition mechanism, a translation mechanism mounting the deposition mechanism to the working volume, and a stage. A method for additive metal manufacturing including: selectively depositing a material carrier within the working volume; removing an additive from the material carrier; and treating the resultant material.

The invention provides a sliced image processing method including following steps: analyzing a sliced object in a sliced image to determine whether the sliced object has a first contour line segment and a nearest second contour line segment, where the second contour line segment is located within a region encircled by the first contour line segment; determining whether vector directions of the first contour line segment and the second contour line segment are opposite when the sliced object has the first contour line segment and the second contour line segment, and correcting the vector direction of at least one of the first contour line segment and the second contour line segment when the vector directions of the first contour line segment and the second contour line segment are not opposite.

Disclosed herein are machine learning-based methods and systems for automated object defect classification and adaptive, real-time control of additive manufacturing and/or welding processes.

A system for additive metal manufacturing, including a deposition mechanism, a translation mechanism mounting the deposition mechanism to the working volume, and a stage. A method for additive metal manufacturing including: selectively depositing a material carrier within the working volume; removing an additive from the material carrier; and treating the resultant material.

Methods for control of post-design free form deposition processes or joining processes are described that utilize machine learning algorithms to improve fabrication outcomes. The machine learning algorithms use real-time object property data from one or more sensors as input, and are trained using training data sets that comprise: i) past process simulation data, past process characterization data, past in-process physical inspection data, or past post-build physical inspection data, for a plurality of objects that comprise at least one object that is different from the object to be fabricated; and ii) training data generated through a repetitive process of randomly choosing values for each of one or more input process control parameters and scoring adjustments to process control parameters as leading to either undesirable or desirable outcomes, the outcomes based respectively on the presence or absence of defects detected in a fabricated object arising from the process control parameter adjustments.

A system for additive metal manufacturing, including a deposition mechanism, a translation mechanism mounting the deposition mechanism to the working volume, and a stage. A method for additive metal manufacturing including: selectively depositing a material carrier within the working volume; removing an additive from the material carrier; and treating the resultant material.