Hewlett-packard Development Company, L.p.

Instituto Atlantico

Peridot Print LLC

Jun Zeng

Scott A White

Gary Joseph Dispoto

Sunil Kothari

Sebastia Cortes I Herms

He Luan

Ana Patricia Del Angel

Eric Hoarau

Sebastia Cortes

Tod S Heiles

I-Jong Lin

Jan Morovic

Peter Morovic

Lihua Zhao

Marcelo Aita Riss

Steven J Simske

Ning Ge

Wei T Huang

Yan Zhao

Giordano Bruno Beretta

Adel Jilani

Juan Manuel Garcia Reyero Vinas

Patrick O Sandfort

David C Woodlock

Wei Huang

Alyne Gomes Soares Cantal

David James Tucker

Pu Huang

Goffril Obegi

Thomas J Peck

Francisco Oblea

Juan Carlos Catana Salazar

Viseth Sean

Holt Mebane

Tony S Cruz-Uribe

David Pidwerbecki

Hui Liu

Scott White

Juan Carlos Catana

Kristopher Li

Michael L Reasoner

Michelle Lynn Bockman

Francisco Jose Oblea Ramirez

Thiago Barbosa Lima De Moura

Chandrakant D Patel

Henryk Birecki

William James Allen

Wesley R Schalk

David A Champion

James Richard Przybyla

Michael H Lee

Nathan McPherson Moroney

Douglas A Sexton

Jian Fan

Michael G Monroe

Paul J Benning

Krishnendu Chakrabarty

Eric G Hanson

Luis Garcia

Kenneth James Faase

Sergio Gonzalez Martin

Scott H Clearwater

Pol Fornos

Kelly Ronk

Xavier Vilajosana

Hamid Reza Motahari Nezhad

Haggai Karlinski

Jordi Roca Vila

Brian Schmitz Tani

Daniel Mosher

Patrick O'Neil Sandfort

Jordi Roca

Sam A Stodder

Nailson Boaz Costa Leite

Nicholas Saunders

Thiago Moura

Sushant Bhattacharya

Md Abdullah Al Hafiz Khan

Jamie Machado Neto

Victor Praxedes

Hokeun Kim

Adriana Fontenele

Federico Martinez

Juan Manuel Garcia

Maria Fabiola Leyva Mendivil

Marco Antonio Medrano Acosta

Carlos Alberto Lopez Collier De La Marliere

Anthony Powell

Gene Allen McDaniel, Jr

Lei Chen

Qing Duan

Juan Carlos Salazar

Thiago B Lima De Moura

Sebastia Cortes I Hermes

Examples of methods for thermal image determination are described. In some examples, a method may include determining, using a first machine learning model, a first thermal image of a first layer of additive manufacturing. In some examples, the method may include determining, using a second machine learning model, a second thermal image of a second layer based on a simulated thermal image. The second thermal image may have a second resolution that is greater than a first resolution of the simulated thermal image in some examples. In some examples, the method may include determining, using a third machine learning model, a third thermal image based on the first thermal image and the second thermal image.

Thermal image determination

Examples of methods for predicting porosity are described herein. In some examples, a method includes predicting a height map. In some examples, the height map is of material for metal printing. In some examples, the method includes predicting a porosity of a precursor object. In some examples, predicting the porosity of the precursor object is based on the predicted height map.

Porosity prediction

Systems and methods for adapting a boundary condition of a simulation of 3D manufacturing are provided. The simulation of 3D manufacturing is based on thermal sensor data from a thermal sensor at a point of a build enclosure are provided.

Adaptive simulation in additive manufacturing

Examples of methods for determining an adaptive thermal diffusivity kernel are described herein. In some examples, an adaptive thermal diffusivity kernel is determined based on a map. In some examples, a temperature is predicted based on the adaptive thermal diffusivity kernel. In some examples, the map includes a temperature map, a shape map, or a concatenation of the temperature map and the shape map.

Adaptive thermal diffusivity

Examples of methods for model prediction are described herein. In some examples, a method includes predicting a compensated model. In some examples, the compensated model is predicted based on a three-dimensional (3D) object model. In some examples, a method includes predicting a deformed model. In some examples, the deformed mode is predicted based on the compensated model.

Model prediction

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.

Temperature prediction in three-dimensional (3D) parts

An example three-dimensional (3D) printer may include a camera to capture a low-resolution thermal image of a build material bed. The 3D printer may include an interpolation engine to generate an interpolated thermal image based on the low-resolution thermal image. The 3D printer may also include a correction engine to enhance fine details of the interpolated thermal image without distorting thermal values from portions of the interpolated thermal image without fine details to produce an enhanced thermal image.

Enhancing interpolated thermal images

Examples of methods are described herein. In some examples, a method includes determining a quantity of inner voxels in a canonical direction from each surface voxel of a set of surface voxels of a three-dimensional (3D) object model. In some examples, the method includes generating an encoded representation of the 3D object model, the encoded representation indicating a location of each surface voxel and the quantity of inner voxels for each surface voxel.

Object model encodings

Examples of methods for part packing are described herein. In some examples, a first subset and a second subset of a set of parts are determined. In some examples a first packing of the first subset is determined. In some examples, a second packing of the second subset is determined based on the first packing.

Part packing

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.

Custom three-dimensional (3D) print mode generation

Examples of methods for object manufacturing visualization by an electronic device are described herein. In some examples, a predicted thermal image of additive manufacturing is determined using a machine learning model. In some examples, a captured thermal image is obtained. In some examples, a graphical overlay of the predicted thermal image with the captured thermal image is presented.

Object manufacturing visualization

Examples of methods for thermal mapping by an electronic device are described herein. In some examples, a map is obtained. In some examples, a first thermal image at a first resolution is obtained. In some examples, a neural network is used to determine a second thermal image at a second resolution based on the map and the first thermal image. The second resolution is greater than the first resolution in some examples.

Thermal mapping

Examples of methods for object deformation determination are described herein. In some examples, a method includes aligning a first bounding box of a three-dimensional (3D) object model with a second bounding box of a scan. In some examples, the method includes determining a deformation between the 3D object model and the scan based on the alignment.

Object deformation determination

Examples of methods for detecting a material phase are described herein. In some examples, a kernel is predicted based on an input corresponding to an object and based on a machine learning model. In some examples, the machine learning model is constrained with a physical model. In some examples, a material phase is detected based on the kernel.

Material phase detection in additive manufacturing

A parts packing method is used to optimize build bed part placement for additive manufacturing. The method includes a user interface to parts packing software, enabling a process engineer to select parts from a pool of parts, group parts to have uniform orientation, and specify a range of angles and rotation of the parts. Additionally, the user interface accepts input related to process variations among different printers as well as drift over time of a single printer. The user interface feeds the data into the parts packing program to optimize build bed part placement.

User-assisted parts packing optimization

Growing community of inventors

Palo Alto, CA, United States of America

Jun Zeng