Abstract:
In an example of a method for determining a thermal footprint for a three-dimensional (3D) printed part, a part to be printed by a 3D printer is identified. A thermal footprint for the part is determined based on part geometry and heat transfer associated with printing the part.
Abstract:
Methods and apparatus for three-dimensional object representation are described. In an example, data representing a three-dimensional object is received, the data comprising an object property description associated with each of a plurality of locations within the object. Distinct object property descriptions are identified and a data object, which has a plurality of addresses, is populated with data indicative of the distinct object property descriptions, such that data indicative of each distinct object property description is represented at a different address. Data comprising a representation of the object is generated, the data comprising a data object address associated with each of a plurality of locations, wherein the data object address corresponds to the address of data indicative of the object property description for that location.
Abstract:
An example technique for generating slice data from a voxel representation can include obtaining a shape specification of the 3-D object. The example technique for generating slice data from a voxel representation can also include obtaining a material specification of the 3-D object. The example technique for generating slice data from a voxel representation can also include merging the shape specification and the material specification to create a voxel representation of the 3-D object, wherein each voxel in the voxel representation includes a plurality of data types. The example technique for generating slice data from a voxel representation can also include generating slice data from the voxel representation, wherein the slice data provides a higher resolution than that provided by the voxel representation using the plurality of data types.
Abstract:
Determining a payload estimate for a task in a process includes learning a payload control, storing a learned payload control in a database, and evaluating the payload estimate. Learning includes applying machine learning to process information to determine a learned payload control corresponding to the task in the process. Evaluating the payload estimate includes searching the database for a plurality of payload controls relevant to the task including the learned payload control, determining whether a condition of the learned payload control is met, and applying the payload control to determine the payload estimate using process information if the condition of the learned payload control is satisfied.
Abstract:
Determining compatible equipment in a manufacturing environment is disclosed. A method includes developing a list of compatible equipment for a manufacturing line to produce a product based on process information related to the product. The list of compatible equipment and a manufacturer constraint are modeled. Modeling includes at least one of developing manufacturing line scenarios, simulating the manufacturing line scenarios, and weighing the simulations against production metrics.
Abstract:
Examples of methods are described herein. In some examples, a method includes determining a graph representation of a three-dimensional (3D) object. In some examples, the graph representation includes nodes and edges associated with the nodes. In some examples, each node includes a temperature profile attribute. In some examples, the method includes predicting, using a machine learning model, a deformation of the 3D object based on the graph representation.
Abstract:
Examples of methods are described. In some examples, a method includes determining objects corresponding to a manufacturing period of three dimensional (3D) printing. In some examples, the method includes packing build volumes based on the objects. In some examples, the method includes simulating manufacturing powder degradation based on the build volumes. In some examples, the method includes determining a quantity of manufacturing powder consumption based on the manufacturing powder degradation. In some examples, the method includes adjusting a manufacturing parameter based on the quantity of manufacturing powder consumption.
Abstract:
In an example in accordance with the present disclosure, a fiber molding screen is described. The fiber molding screen includes a first region and a second region. The fiber molding screen includes a first set of pores formed within the first region. Each pore of the first set of pores has a first pore trajectory angle between a longitudinal axis of the pore and the first region surface. The fiber molding screen also includes a second set of pores formed within the second region. Each pore of the second set of pores has a second pore trajectory angle between a longitudinal axis of the pore and the second region surface. The second pore trajectory angle is different than the first pore trajectory angle.
Abstract:
Examples of methods for thermal image generation are described. In some examples, a method may include determining a score map based on first features from a model, a simulated thermal image at a first resolution, and second features of the simulated thermal image. In some examples, the method may include generating a thermal image at a second resolution based on the score map, the first features, and the second features, where the second resolution may be greater than the first resolution.
Abstract:
In an example, a method includes receiving, at a processor, object model data representing at least a portion of an object to be generated by an additive manufacturing apparatus by fusing build material. Using a processor and from the object model data, a property diffusion model for the object in object generation may be determined. Using a processor and based on the property diffusion model, a manufacturing boundary object shell around the object and encompassing an external volume may be determined. The shell may have a variable thickness determined so as to include build material for which, in generation of the object, the property modelled in the property diffusion model has a value which is predicted to conform to a predetermined parameter.