Inkbit, LLC

Massachusetts Institute of Technology

Desai Chen

Wojciech Matusik

Aaron Weber

Gregory Ellson

Javier E Ramos

Harrison Wang

Kiril Vidimce

Davide Marini

Wenshou Wang

Yan Zhang

Walter Henry Zengerle, Iii

Pitchaya Sitthi-Amorn

Bo Zhu

Piotr Krzysztof Didyk

Melina Skouras

David I W Levin

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.

Optical scanning for industrial metrology

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.

Reflective coating for material calibration

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.

Precision system for additive fabrication

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.

Calibration for additive manufacturing

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.

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.

Machine learning for additive manufacturing

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.

Edge profilometer

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 at each location of the plurality of locations, thereby causing printing of the layer.

Object model encoding for additive fabrication

Intelligent additive manufacturing

A method for determining estimated depth data for an object includes scanning the object to produce scan data corresponding to a surface region of the object using a first scanning process, configuring an artificial neural network with first configuration data corresponding to the first scanning process, and providing the scan data as an input to the configured artificial neural network to yield the estimated depth data as an output, the estimated depth data representing a location of a part of the object in the surface region.

Depth reconstruction in additive fabrication

A method for additive fabrication includes determining numerical calibration transforms for calibrating an imaging sensor and a printhead assembly to a common coordinate system, where at least some of the numerical calibration transforms include nonlinear transforms.

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Arlington, MA, United States of America

Desai Chen