Hewlett-packard Development Company, L.p.

Peridot Print LLC

Instituto Atlantico

He Luan

Jun Zeng

Juan Carlos Catana Salazar

Scott A White

Sebastia Cortes I Herms

Marcelo Aita Riss

Alyne Gomes Soares Cantal

Brian Schmitz Tani

Nailson Boaz Costa Leite

Carlos Alberto Lopez Collier De La Marliere

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

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 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 system includes a machine readable storage medium storing instructions and a processor. The processor is to execute instructions to receive contone agent maps of a three-dimensional (3D) part and sensed thermal maps from the 3D printing of the 3D part on a 3D printer. The processor is to execute instructions to generate layer sequences including the contone agent maps and the sensed thermal map for each layer of the 3D part. The processor is to execute instructions to select training samples from the layer sequences having temperature intensity variations within each layer or between neighboring layers. The processor is to execute instructions to train a neural network using the training samples to generate a model to predict thermal behavior in the 3D printer.

Predicting thermal behavior in 3D printers

Examples of a thermal behavior prediction method are described herein. In some examples of the thermal behavior prediction method, a predicted heat map of a layer corresponding to a three-dimensional (3D) model is computed using at least one neural network. The predicted heat map is computed based on a contone map corresponding to the 3D model.

Neural network thermal behavior predictions

Examples of methods for monitoring additive manufacturing by an electronic device are described herein. In some examples, a predicted thermal image is calculated for a layer. In some examples, a captured thermal image is obtained for the layer. In some examples, a risk score is calculated for the layer based on the predicted thermal image and the captured thermal image. In some examples, a mitigation operation is performed in response to determining that the risk score is outside of a threshold range.

Growing community of inventors

Palo Alto, CA, United States of America

He Luan