公开(公告)号：US20220016833A1

公开(公告)日：2022-01-20

申请号：US17299645

申请日：2018-12-19

Applicant: HEWLETT-PACKARD DEVELOPMENT COMPANY, L.P. ,  INSTITUTO ATLANTICO

Inventor： Jun Zeng ,  Alyne Gomes Soares CANTAL ,  Juan Carlos Catana Salazar

IPC: B29C64/171 ,  B29C64/386 ,  B33Y50/00 ,  G05B19/4099

Abstract: 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.

公开(公告)号：US20200379436A1

公开(公告)日：2020-12-03

申请号：US16606967

申请日：2018-02-19

Applicant: HEWLETT-PACKARD DEVELOPMENT COMPANY, L.P. ,  INSTITUTO ATLANTICO

Inventor： Marcelo Aita Riss ,  Thiago Barbosa Lima De Moura ,  Alyne Gomes Soares Cantal ,  Ana Patricia Del Angel ,  Jun Zeng ,  Scott A White ,  Sebastia Cortes I Herms

IPC: G05B19/4099 ,  G06T15/00

Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions that when executed by the processor are to cause the processor to access a reduced resolution three-dimensional (3D) model file of an object to be built in a build bed of a 3D printing system, the reduced resolution 3D model file comprising a reduced resolution file of a first resolution 3D model file of the object. The instructions may also cause the processor to determine a packing arrangement for the object and a plurality of other objects to be built in the build bed through use of the reduced resolution 3D model file and output the determined packing arrangement for the 3D printing system to print the object and the other objects.

公开(公告)号：US20210342211A1

公开(公告)日：2021-11-04

申请号：US17262769

申请日：2019-01-21

Applicant: Hewlett-Packard Development Company, L.P. ,  INSTITUTO ATLANTICO

Inventor： Tiago Barbosa Melo ,  Claudio Andre Heckler

IPC: G06F11/07 ,  G06N20/00 ,  G06F3/12

Abstract: Examples of methods for fault prediction model training with audio data are described herein. In some examples, service event data and audio data corresponding to client devices are received. In some examples, a portion of the audio data is selected based on the service event data. In some examples, a machine learning model is trained for fault prediction based on the portion of audio data.