Abstract: An approach to improving optical scanning increases the strength of optical reflection from the build material during fabrication. In some examples, the approach makes use of an additive (or a combination of multiple additives) that increases the received signal strength and/or improves the received signal-to-noise ratio in optical scanning for industrial metrology. Elements not naturally present in the material are introduced in the additives in order to increase fluorescence, scattering or luminescence. Such additives may include one or more of: small molecules, polymers, peptides, proteins, metal or semiconductive nanoparticles, and silicate nanoparticles.

Abstract: A method includes generating correction data for a construction material that is used by an additive-manufacturing machine to manufacture an object. This correction data compensates for an interaction of the construction material with first radiation that has been used to illuminate the construction material.

Abstract: The present disclosure relates to materials for photoinitiated cationic ring-opening polymerization (ROP). The present disclosure also relates to uses of the materials, e.g., in 3D printing.

Abstract: A method comprises using a three-dimensional additive manufacturing process to produce an interlocking volume, wherein using the additive manufacturing process includes depositing successive layers, each of which includes a first material distributed according to a first interlocking material pattern and a second material distributed according to a second interlocking material pattern, said second material differing from said first material.

Abstract: The present disclosure relates to materials for ring-opening metathesis polymerization (ROMP). The present disclosure also relates to uses of the materials, e.g., in 3D printing.

Abstract: An approach to precision additive fabrication uses jetting of cationic compositions in conjunction with a non-contact (e.g., optical) feedback approach. By not requiring contact to control the surface geometry of the object being manufactured, the approach is tolerant of the relative slow curing of the cationic composition, while maintaining the benefit of control of the deposition processes according to feedback during the fabrication processes. This approach provides a way to manufacture precision objects and benefit from material properties of the fabricated objects, for example, with isotropic properties, which may be at least partially a result of the slow curing, and flexible structures, which may not be attainable using conventional jetted acrylates.

Abstract: Arrangement of the parts, particularly addressing arrangement for 3D fabrication, makes use of spatial frequency domain representations of the parts, such that acceptable (or optionally desirable) relative locations (and optionally orientations) of a part relative to another part or an arrangement of multiple other parts makes use of spatial frequency domain representations of the parts. In some examples, acceptable relative locations may be defined as relative locations that do not result in parts spatially overlapping and/or maintain a desired (e.g., minimum, average, etc.) separation between parts.

Abstract: An additive fabrication approach involves fabricating a platform on a build plate. The fabrication system is then calibrated based on the fabricated platform, and an object is then fabricating on the fabricated platform according to the calibration.

Abstract: A method for additive manufacturing includes forming an object including depositing a first material including a first coloring component and a second material including a second coloring component, wherein both the first material and the second material further include a corresponding fluorescent component, scanning the object, including causing an emission of an optical signal from the object, wherein the emission of the optical signal is caused at least in part by an emission from the fluorescent components interacting with the first coloring component and the second coloring component as it passes from the fluorescent components to the surface of the object, sensing the emission of the optical signal, and determining presence of the first material and the second material based at least in part on the sensed emission of the optical signal.

Abstract: The present disclosure relates to combinations, build materials, and kits containing vinyl sulfonyl agents for thiol-ene polymerization. The present disclosure also relates to uses of the combinations, materials, and kits, e.g., in 3D printing.

Abstract: An inkjet printer includes a mixer and a metering system that system provides appropriate amounts of the resin's precursors into the mixer. The mixer thoroughly mixes these precursors and feeds the resulting resin to the printheads. The inkjet printer may include a cleaning system that removes residual resin during or after printing.

Abstract: An approach to intelligent additive manufacturing makes use of one or more of machine learning, feedback using machine vision, and determination of machine state. In some examples, a machine learning transformation receives data representing a partially fabricated object and a model of an additional part (e.g., layer) of the part, and produces a modified model that is provided to a printer. The machine learning predistorter can compensate for imperfections in the partially fabricated object as well as non-ideal characteristics of the printer, thereby achieving high accuracy.

Abstract: A profilometer provides, to a controller, a feedback signal indicative of topography of an exposed surface of an object that is being manufactured by a 3d printer. The profilometer includes an emitter and a camera. The emitter illuminates a region of surface of the object with a pattern having an edge that defines a boundary of an illuminated portion of the surface. The camera receives an image that transitions between a first state in which the edge is visible in the image at a location that is indicative of the surface's depth and a second state in which the edge is not visible at all. From this second state, the controller obtains information representative of a depth of the surface.

Abstract: An approach to intelligent additive manufacturing makes use of one or more of machine learning, feedback using machine vision, and determination of machine state. In some examples, a machine learning transformation receives data representing a partially fabricated object and a model of an additional part (e.g., layer) of the part, and produces a modified model that is provided to a printer. The machine learning predistorter can compensate for imperfections in the partially fabricated object as well as non-ideal characteristics of the printer, thereby achieving high accuracy.

Abstract: A method for additive fabrication by 3D printing includes processing model data representing material transition boundaries of an object to be printed to form build data for use in controlling printing of a plurality of successive layers to form the object, the build data comprising, for each location of a plurality of locations in a two-dimensional arrangement, material transition data for representing heights of material transitions in a third dimension, and repeating for each layer of the plurality of successive layers, receiving surface height data representing a height of a partial fabrication of the object at respective locations of a plurality of locations on a surface of the partial fabrication for each location of the plurality of locations, using the height at the location to access the material transition data corresponding to the location in the build data, and using the material transition data to determine material to be deposited at that location, and causing emission of the determined material

Abstract: A method for manufacturing a part includes fabricating an object in an additive fabrication stage, the object including a solid mold forming a cavity in the shape of a part with uncured or incompletely cured build material disposed therein. The build material in the cavity is cured in a curing stage that occurs at least partially after the additive fabrication stage. The build material undergoes a phase change mechanism occurring during the additive fabrication stage and a distinct polymerization mechanism occurring during the curing stage and at least partly after the additive fabrication stage of the object and cures the build material by a polymerization process.

Abstract: A method for managing the temperature of three-dimensional object during additive manufacturing includes depositing material on a partial fabrication of the three-dimensional object, determining thermal properties of the three-dimensional object after depositing the material, and controlling cooling of the object according to said thermal properties. In some examples, the determining of the thermal properties includes determining a temperature variation across the object and controlling position-dependent cooling of the object in a feedback arrangement. Resulting uniform temperature of the object under fabrication can improve dimensional accuracy and material properties of the object.

Abstract: A manufacturing method includes using an additive manufacturing process to form a first object. The first object includes layers, among which is a first layer that comprises a first material and a second material. The first material surrounds the second material within the first layer. Both materials are solids that transition into liquids at corresponding first and second temperatures, the first temperature being lower than the second temperature. The method includes, after having formed the first object, exposing the first object to a temperature that is between the first temperature and the second temperature such that the first material melts, thereby leaving behind a second object, the second object comprising the second material.

Abstract: An approach to intelligent additive manufacturing makes use of one or more of machine learning, feedback using machine vision, and determination of machine state. In some examples, a machine learning transformation receives data representing a partially fabricated object and a model of an additional part (e.g., layer) of the part, and produces a modified model that is provided to a printer. The machine learning predistorter can compensate for imperfections in the partially fabricated object as well as non-ideal characteristics of the printer, thereby achieving high accuracy.

Abstract: An approach to intelligent additive manufacturing makes use of one or more of machine learning, feedback using machine vision, and determination of machine state. In some examples, a machine learning transformation receives data representing a partially fabricated object and a model of an additional part (e.g., layer) of the part, and produces a modified model that is provided to a printer. The machine learning predistorter can compensate for imperfections in the partially fabricated object as well as non-ideal characteristics of the printer, thereby achieving high accuracy.