Hewlett-packard Development Company, L.p.

Sunil Kothari

Jun Zeng

Gary Joseph Dispoto

Tod S Heiles

Goffril Obegi

Thomas J Peck

Viseth Sean

Francisco Oblea

Lihua Zhao

Juan Carlos Catana

Kristopher Li

Md Abdullah Al Hafiz Khan

Michael L Reasoner

Wesley R Schalk

Douglas A Sexton

Eric Hoarau

Kelly Ronk

Todd Goyen

Juan Carlos Catana Salazar

Francisco Jose Oblea Ramirez

Luke Bockman

Juan Carlos Salazar

Gene Allen McDaniel, Jr

Anthony Powell

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

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

A system for detecting three-dimensional (3D) part drag includes an infrared image capture device to capture a plurality of thermal images of a 3D part build region of a 3D printing device on which a part is built, and an image analysis module to detect drag of the part based on a difference image produced by subtracting a first thermal image from a second thermal image.

Detecting three-dimensional (3D) part drag

In an example, an apparatus includes an image processing system, a print engine, and a vision system. The image processing system generates electronic signals based on a model of an object to be fabricated using an additive manufacturing process. The print engine performs the additive manufacturing process in a plurality of passes based on the electronic signals. The vision system acquires a plurality of thermal images of the plurality of passes and assigns individual passes to individual images based on data acquired during a build of a calibration object by the additive manufacturing process. The print engine may further include a material coater to spread a powder coating material, a plurality of fluid ejection devices to eject a fusing agent, and an emitter to emit energy to fuse the fusing agent and the powder coating material into a layer of the object to be fabricated.

Identifying passes of additive manufacturing processes depicted in thermal images

Examples disclosed herein relate to 3D printer fresh and recycled powder supply management. In one implementation, a processor estimates fresh and recycled powder use and recycled powder creation by a 3D printer based on a set of print jobs. The processor may coordinate fresh and recycled powder resources based on the powder estimate and stored information about powder resources.

3D printer fresh and recycled powder supply management

A control and feedback technique to determine the proper location of powder control temperature regions includes detecting solid parts having specified shape and thermal characteristics. The solid parts are generated by a 3D printer. The solid parts include powder material fused on a layer by layer basis. A powder control temperature region is selected in each layer of powder material, wherein the region is located in unfused powder material adjacent to the solid parts, and wherein the region is selected based on characteristics of the shape of a build area and thermal imaging associated with any of a structural formation of the solid parts adjacent to the region, and an amount of thermal energy radiated by the solid parts adjacent to the region. The amount of thermal energy to cause fusing of the powder material on each subsequent layer is modified based on location of the powder control temperature region.

Selection of powder control temperature regions in 3D printing

In some examples, with respect to region of interest monitoring and control for additive manufacturing, a blob detection analysis may be performed on first and second component images associated with additive manufacturing of a component, and blobs that remain a same shape and include same centroids on the first and second component images may be identified. A further blob detection analysis may be performed on first and second thermal images associated with the first and second component images, and a determination may be made as to whether one of the identified blobs includes a same shape and a different centroid between the first and second thermal images. Based on a determination that the one of the identified blobs includes the same shape and the different centroid, an indication of a thermal camera misalignment associated with the additive manufacturing may be generated.

Region of interest monitoring and control for additive manufacturing

In some examples, a distribution of values of a property of a given layer to be printed as part of three-dimensional (3D) printing is predicted, wherein the predicting is based on a distribution of values of the property in a previous layer that has been printed as part of the 3D printing. 3D printing of an object is controlled based on the predicted distribution of values of the property of the given layer.

Predicting distributions of values of layers for three-dimensional printing

A system and method for providing three dimensional printing production quality prediction is described herein. The system may include logic to scan a region of interest associated with a layer of a plurality of parts during printing to obtain an input for the region of interest and compute an input metric associated with the layer based on the input. In response to an initial set of print jobs, at least a portion of the plurality of parts is mechanically tested to determine at least one output. In response to a production print job, a likelihood of a part of the plurality of parts to satisfy a quality specification is predicted by inputting the input metric to at least one machine learning function comprising the at least one output, wherein the at least one machine learning function is trained during the initial set of print jobs.

Printing production quality prediction

In some examples, a request to print a first three-dimensional (3D) part is received. In response to determining that the first 3D part is not similar to any 3D part referred to by an information base, a representation of the first 3D part is extracted, an indication to conduct an operation to produce a design rule for the first 3D part is sent. In response to determining that the first 3D part is similar to a matching 3D part referred to by the information base, a design rule for the matching 3D part is retrieved to print the first 3D part, where the design rule for the matching 3D part specifies a dependency of a property of the matching 3D part on an aspect associated with printing the matching 3D part.

Rules for printing three-dimensional parts

Examples disclosed herein relate to determining data usage effectiveness. A processor may determine data usage effectiveness information related to an entity's workflow based on workflow data collected related to the entity's operations and metrics determined based on the data. The determination may be based on a comparison of the information related to the entity's workflow to target workflow information and priority information associated with the target workflow information. The processor may output information related to the determined data usage effectiveness.

Data usage effectiveness determination

Examples disclosed herein relate to determining a manufacturing batch. In one implementation, the manufacturing batch relates to 3D printing. A processor may determine component parts of a product that. In one implementation, a processor determines a batch of the component parts related to different products based on a comparison to other potential batches.

Determine a manufacturing batch

Systems and methods associated with resource provisioning are disclosed. One example method includes dividing a set of printing resources into a first partition and a second partition. The example method also includes provisioning the first partition to handle print jobs from a print queue that have a specified attribute. The first partition may be provisioned when print jobs having the specified attribute exceed a first predefined threshold.

Resource provisioning

Scheduling of manufacturing jobs is described herein. In accordance with an example, the scheduling of manufacturing jobs includes obtaining an order parameter associated with a product to be manufactured on a manufacturing equipment set. A product dimension of an intermediate product for the product is determined based on the order parameter and a production attribute. Further, the manufacturing job is scheduled for the product amongst manufacturing jobs for other products to be manufactured on the manufacturing equipment set, based on the product dimension of the intermediate product and a scheduling criterion. The scheduling criterion can be a parameter to regulate the scheduling of the manufacturing jobs on the manufacturing equipment set.

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Palo Alto, CA, United States of America

Sunil Kothari