A method for the additive manufacturing of at least one component (2) having defined component properties, comprising the steps of: providing at least one certified data record (4) which contains component-specific parameters and/or system-specific parameters and/or process-specific parameters certified by a provider for an additive manufacturing process of at least one specific component (2) having defined component properties, implementing at least one additive manufacturing process for the additive manufacturing of at least one specific component (2) having defined component properties on the basis of the at least one certified data record (4).

A system includes a scanner to convert a packaging sketch into a pixelated image and a convolutional neural network configured to segment the pixelated image into bounded objects including fold lines and cut lines. A controller is configured to transform the fold lines and the cut lines into control commands to a folding machine and a cutting machine. A method includes converting a packaging sketch into a pixelated image using a scanner and segmenting, using a convolutional neural network, the pixelated image into bounded objects including fold lines and cut lines. The method also includes transforming, using a controller, the fold lines and the cut lines into control commands to a folding machine and a cutting machine.

An integrated online dynamic balance terminal by 3D rapid prototyping includes a central tapered hole formed at a lower portion thereof, a plurality of identical balance cavities peripherally and spacedly formed on the integrated online dynamic balance terminal. Each two the adjacent balance cavities are separated by a cavity partition. The integrated online dynamic balance terminal further has a plurality of guiding channels indently formed on an inner peripheral surface thereof, wherein each of the four guiding channels communicates with a corresponding balance cavity through a corresponding trapezoidal hole. The integrated online dynamic balance terminal has a plurality of bored holes spacedly formed on an engagement surface. The integrated online dynamic balance terminal is configured from 3D rapid prototyping so as to form an integral one-piece structure, wherein some portions requiring high precision are arranged to undergo additional machining processes.

Process control parameters are predicted to fabricate an object using deposition. An input design geometry is provided for the object. A training data set includes 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 physically fabricated; and training data generated through a repetitive process of randomly choosing values for each of multiple process control parameters and scoring adjustments to the multiple 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 machine learning algorithm is trained using the provided training data set and a predicted optimal set of the multiple process control parameters is generated for initiating and performing the deposition process to fabricate the object.

A method of optimizing an additive manufacturing (AM) process includes receiving at least one design parameter of the AM process, receiving information relating to uncertainty in at least one other parameter of the AM process, performing uncertainty quantification in the optimization processor based on the at least one design parameters and uncertainty information to identify a shape error in an object being produced, updating the at least one design parameter of the AM process and utilizing the updated at least one design parameter in the AM process. A system for optimizing an AM process includes a design processor to produce at least one design parameter for an object to be manufactured, and an optimization processor to receive the at least one design parameter and uncertainty information to identify a shape error in the object to be manufactured and update the design parameters based on the shape error, prior or during the manufacturing process.

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 includes a scanner to convert a packaging sketch into a pixelated image and a convolutional neural network configured to segment the pixelated image into bounded objects including fold lines and cut lines. A controller is configured to transform the fold lines and the cut lines into control commands to a folding machine and a cutting machine. A method includes converting a packaging sketch into a pixelated image using a scanner and segmenting, using a convolutional neural network, the pixelated image into bounded objects including fold lines and cut lines. The method also includes transforming, using a controller, the fold lines and the cut lines into control commands to a folding machine and a cutting machine.

An integrated online dynamic balance terminal by 3D rapid prototyping includes a central tapered hole formed at a lower portion thereof, a plurality of identical balance cavities peripherally and spacedly formed on the integrated online dynamic balance terminal. Each two the adjacent balance cavities are separated by a cavity partition. The integrated online dynamic balance terminal further has a plurality of guiding channels indently formed on an inner peripheral surface thereof, wherein each of the four guiding channels communicates with a corresponding balance cavity through a corresponding trapezoidal hole. The integrated online dynamic balance terminal has a plurality of bored holes spacedly formed on an engagement surface. The integrated online dynamic balance terminal is configured from 3D rapid prototyping so as to form an integral one-piece structure, wherein some portions requiring high precision are arranged to undergo additional machining 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.

Techniques for evaluating support for an object to be fabricated via an additive fabrication device are provided. In some embodiments, a three-dimensional representation of the object is obtained and a plurality of voxels corresponding to the representation of the object is generated. A first supportedness value may be assigned to a first voxel of the plurality of voxels based on an amount of support provided by a support structure to the first voxel, and a second supportedness value determined for a second voxel of the plurality of voxels, wherein the second voxel neighbors the first voxel, and wherein the second supportedness value is determined based on the first supportedness value of the first voxel and a weight value representing a transmission rate of supportedness through voxels of the plurality of voxels.