Abstract: A method of custom print mode generation in a three-dimensional (3D) printing device may include printing a plurality of parts with a plurality of 3D printing devices, the parts each being printed using different process parameters, and capturing a plurality of images of the parts. The method may also include, with an image analysis module, analyzing the images to classify the parts into a plurality of defect gradings, and adjusting a number of the process parameters based on characteristics of the parts identified by a user as undesirable. The examiner may also include, with a recommending module, creating a custom print mode based on the parts defect gradings and adjusted process parameters.

Abstract: A computing device comprising a controller is disclosed herein. The controller is to access print data of a virtual build volume including a 3D object to be generated by a 3D printer; modify the print data to include a 3D structure at a location within the build volume to encapsulate an amount of build material; receive powder degradation data corresponding to the powder degradation of the encapsulated amount of build material; and calibrate an additive manufacturing parameter based on the powder degradation data.

Abstract: An example system includes a simulation engine to determine a plurality of thermal states that will be experienced by powder at a voxel of a three-dimensional print volume as a result of printing a particular build. Each thermal state corresponds to a time during the printing or cooling from the printing. The system includes a stress engine to calculate a stress to the powder at the voxel based on the plurality of thermal states. The system includes a degradation engine to determine an amount of degradation of the powder at the voxel based on the stress.

Abstract: Examples of apparatuses for agent map generation are described. In some examples, an apparatus includes a memory to store a layer image. In some examples, the apparatus includes a processor coupled to the memory. In some examples, the processor is to generate, using a machine learning model, an agent map based on the layer image.

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: Examples of methods are described. In some examples, a method includes generating, using a first branch of a machine learning model, a first agent map based on a layer image. In some examples, the method includes generating, using a second branch of the machine learning model, a second agent map. In some examples, the first agent map and the second agent map indicate printing locations for different agents.

Abstract: Examples of methods are described. In some examples, a method includes determining, using a variational autoencoder model, a latent space representation. In some examples, the latent space representation is of object model data. In some examples, the method includes predicting manufacturing powder degradation. In some examples, predicting the manufacturing powder degradation is based on the latent space representation.

Abstract: A system for detecting three-dimensional (3D) part drag includes an infrared image capture device to capture a plurality of thermal images of a 3D part build region of a 3D printing device on which a part is built, and an image analysis module to detect drag of the part based on a difference image produced by subtracting a first thermal image from a second thermal image.

Abstract: In an example, an apparatus includes an image processing system, a print engine, and a vision system. The image processing system generates electronic signals based on a model of an object to be fabricated using an additive manufacturing process. The print engine performs the additive manufacturing process in a plurality of passes based on the electronic signals. The vision system acquires a plurality of thermal images of the plurality of passes and assigns individual passes to individual images based on data acquired during a build of a calibration object by the additive manufacturing process. The print engine may further include a material coater to spread a powder coating material, a plurality of fluid ejection devices to eject a fusing agent, and an emitter to emit energy to fuse the fusing agent and the powder coating material into a layer of the object to be fabricated.

Abstract: Examples disclosed herein relate to 3D printer fresh and recycled powder supply management. In one implementation, a processor estimates fresh and recycled powder use and recycled powder creation by a 3D printer based on a set of print jobs.

Abstract: Examples disclosed herein relate to 3D printer fresh and recycled powder supply management. In one implementation, a processor estimates fresh and recycled powder use and recycled powder creation by a 3D printer based on a set of print jobs. The processor may coordinate fresh and recycled powder resources based on the powder estimate and stored information about powder resources.

Abstract: A control and feedback technique to determine the proper location of powder control temperature regions includes detecting solid parts having specified shape and thermal characteristics. The solid parts are generated by a 3D printer. The solid parts include powder material fused on a layer by layer basis. A powder control temperature region is selected in each layer of powder material, wherein the region is located in unfused powder material adjacent to the solid parts, and wherein the region is selected based on characteristics of the shape of a build area and thermal imaging associated with any of a structural formation of the solid parts adjacent to the region, and an amount of thermal energy radiated by the solid parts adjacent to the region. The amount of thermal energy to cause fusing of the powder material on each subsequent layer is modified based on location of the powder control temperature region.

Abstract: In some examples, with respect to region of interest monitoring and control for additive manufacturing, a blob detection analysis may be performed on first and second component images associated with additive manufacturing of a component, and blobs that remain a same shape and include same centroids on the first and second component images may be identified. A further blob detection analysis may be performed on first and second thermal images associated with the first and second component images, and a determination may be made as to whether one of the identified blobs includes a same shape and a different centroid between the first and second thermal images. Based on a determination that the one of the identified blobs includes the same shape and the different centroid, an indication of a thermal camera misalignment associated with the additive manufacturing may be generated.

Abstract: In some examples, a distribution of values of a property of a given layer to be printed as part of three-dimensional (3D) printing is predicted, wherein the predicting is based on a distribution of values of the property in a previous layer that has been printed as part of the 3D printing. 3D printing of an object is controlled based on the predicted distribution of values of the property of the given layer.

Abstract: A system and method for providing three dimensional printing production quality prediction is described herein. The system may include logic to scan a region of interest associated with a layer of a plurality of parts during printing to obtain an input for the region of interest and compute an input metric associated with the layer based on the input. In response to an initial set of print jobs, at least a portion of the plurality of parts is mechanically tested to determine at least one output. In response to a production print job, a likelihood of a part of the plurality of parts to satisfy a quality specification is predicted by inputting the input metric to at least one machine learning function comprising the at least one output, wherein the at least one machine learning function is trained during the initial set of print jobs.

Abstract: Systems and methods of predicting temperature during a build of a three-dimensional (3D) part include determining a temperature profile at a plurality of layers of a part based on geometric characteristics of the 3D part as defined by a 3D part file, and adjusting a process parameter of the build based on the determined temperature.

Abstract: A method of custom print mode generation in a three-dimensional (3D) printing device may include printing a plurality of parts with a plurality of 3D printing devices, the parts each being printed using different process parameters, and capturing a plurality of images of the parts. The method may also include, with an image analysis module, analyzing the images to classify the parts into a plurality of defect gradings, and adjusting a number of the process parameters based on characteristics of the parts identified by a user as undesirable. The examiner may also include, with a recommending module, creating a custom print mode based on the parts defect gradings and adjusted process parameters.

Abstract: A grazing light system for detecting three-dimensional (3D) Additive Manufacturing Device (3D) part lift and drag may include a grazing light directed along an x, y plane of a build region to illuminate the surface of the build region, an image capture device to capture an image of the build region as illuminated by the grazing light, and an image analysis module to detect protrusions of the part along the x, y plane based on variations of luminance information within data representing the image.

Abstract: A system for detecting three-dimensional (3D) part drag includes a layer deposition device, and a sensor to detect a change in a process parameter associated with the operation of the layer deposition device within a 3D part build region of a 3D printing device on which a part is built, the change in a process parameter indicating part drag.

Abstract: In some examples, with respect to region of interest monitoring and control for additive manufacturing, a blob detection analysis may be performed on first and second component images associated with additive manufacturing of a component, and blobs that remain a same shape and include same centroids on the first and second component images may be identified. A further blob detection analysis may be performed on first and second thermal images associated with the first and second component images, and a determination may be made as to whether one of the identified blobs includes a same shape and a different centroid between the first and second thermal images. Based on a determination that the one of the identified blobs includes the same shape and the different centroid, an indication of a thermal camera misalignment associated with the additive manufacturing may be generated.