General Electric Company

The Boeing Company

Dean Michael Robinson

Harry Kirk Mathews, Jr

Subhrajit Roychowdhury

Rogier Sebastiaan Blom

John Joseph Freer

Michael Evans Graham

Changjie Sun

Ananda Barua

Prabhjot Singh

Arvind Rangarajan

Sathyanarayanan Raghavan

Adegboyega Masud Makinde

Yanyan Wu

Pinghai Yang

Michael Charles Ostrowski

Kena Kimi Yokoyama

Vinod Padmanabhan Kumar

Tyler Nathaniel Nelson

Alexander Bernard Flavian Rebello

Nicholas Joseph Kray

Weiping Wang

Brendon James Leary

Shridhar Champaknath Nath

Gregory Alan Mohr

Garth M Nelson

Evan John Dozier

John Dennis Jackson

Joseph Block

Narendra Amalendu Soman

Chunhe Gong

James A Day

Zuozhi Zhao

Olalekan Solomon Odesanya

A compensation field indicating an amount of distortion compensation to be applied across at least a portion of a component is determined, and a nominal computer-aided design (CAD) model of a component is modified based on the compensation field. The amount of distortion compensation corresponds to a multiplication product of: (i) a deviation between the nominal CAD model and a physical representation of the component having been produced based on the nominal CAD model, and (ii) a nonlinear scale factor map that includes a map associating a plurality of locations of the nominal CAD model to corresponding ones of a plurality of scale factors respectively representing an increase or a decrease in the amount of distortion compensation to be applied based on a simulated effect upon the component in response to an iterative simulation process.

Hybrid measurement and simulation based distortion compensation system for additive manufacturing processes

A method and system, the method including receiving a nominal computer-aided design (CAD) model of a component; producing a physical representation of the component based on the CAD model using an additive manufacturing (AM) process; measuring the produced physical representation of the component to obtain measurement data of the physical representation of the component; determining a deviation between a geometry of the CAD model and the measurement data of the physical representation of the component; calculating a nonlinear scale factor using an iterative simulation process; determining a compensation field based on the determined deviation between the geometry of the CAD model and the measurement data of the physical representation of the component and the calculated nonlinear scale factor; modifying the nominal CAD model by the determined compensation field; and producing a physical representation of the component based on the modified nominal CAD model.

A method, medium, and system to receive a specification defining a model of a part to be produced by an additive manufacturing (AM) process; execute an AM simulation on the model of the part to determine a prediction of thermal distortions to the part; execute a topology optimization (TO) to create TO supports that counteract the predicted thermal distortions; generate at least one rule-based support based on a geometry of the part to interface with the part at one or more regions other than the TO supports; combining the TO supports and the at least one rule-based support to generate a set of hybrid supports; save a record of the set of hybrid supports; and transmit the record of the set of hybrid supports to an AM controller to control an AM system to generate a support structure for an AM production of the part.

Hybrid support structures for additively printed parts

A method, medium, and system to execute an additive manufacturing (AM) simulation on a model of a part; determine, based on the AM simulation, a prediction of a temperature and displacement distribution in the part at a particular time in the AM process; apply the predicted temperature and displacement distributions in the part as a boundary conditions on a support design space to determine a temperature distribution throughout the support design space; and execute a thermal-structural topology optimization based on the determined temperature and displacement distributions throughout the support design space to determine a distribution of material in the design space for a thermal support structure to interface with the part that optimally reduces a thermal gradient in the part with a minimum of material and results in the generation of an optimized AM support structure.

Multiphysics automated support generation for additively manufactured components

A manufacturing computer device for dynamically adapting additive manufacturing of a part is configured to store a model of the part including a plurality of build parameters. The manufacturing computer device is also configured to receive current sensor information of at least one current sensor reading of a melt pool from a build of the part in progress. The computer device is further configured to determine one or more attributes of the melt pool based on the current sensor information. Moreover, the computer device is configured to calculate at least one unseen attribute of the melt pool. In addition, the computer device is configured to determine an adjusted build parameter based on the at least one unseen attribute, the one or more attributes, and the plurality of build parameters. The computer device is also configured to transmit the adjusted build parameter to a machine currently manufacturing the part.

Systems and method for advanced additive manufacturing

A manufacturing computer device for dynamically adapting additive manufacturing of a part is provided. The manufacturing computer device includes at least one processor in communication with at least one memory device. The at least one memory device stores a build file for building the part including a plurality of geometries that each include one or more values of a first build parameter. The processor is programmed to receive sensor information of a build of the part by a machine, compare the sensor information for each geometry of the plurality of geometries to the corresponding one or more values of the first build parameter, determine one or more values for a second build parameter for each of the geometries based on the one or more differences, and generate an updated build file for the part including the one or more values for the second build parameter.

A manufacturing computer device for dynamically adapting additive manufacturing of a part is configured to store a build file for building the part including one or more build parameters and receive build information. The manufacturing computer device is also configured to compare the sensor information to the one or more build parameters to determine one or more differences. The computer device is further configured to determine one or more adjustments to the one or more build parameters. Moreover, the computer device is configured to generate an updated build file based on the one or more adjustments. In addition, the computer device is further configured to transmit the updated build file to at least one machine of the plurality of machines for manufacture.

A method for integrating a measurement device for use in measuring a machine component includes providing a coordinate measuring device, such as a coordinate measuring machine (CMM), and integrating with a plurality of nondestructive examination (NDE) capabilities with a plurality of coordinate measuring device capabilities to form an inspection probe. The method further includes integrating the NDE inspection probe with the coordinate measuring device such that the inspection probe substantially simultaneously measures a plurality of NDE measurements and external/internal geometry and defects of machine component, which are linked to actual component dimensional information provided by CMM. The inspection data can be simultaneously linked to and/or displayed together with a CAD model to enable a direct comparison between the inspection data and the nominal requirements carried on the CAD model.

Method and system for multimodal inspection with a coordinate measuring device

Systems and methods are disclosed for consistently translating or converting between geometric dimensioning and tolerancing information and variation parameters for a three dimensional variation analysis tool. The methods and systems may receive geometric dimensioning and tolerancing information; translate, with a computer, the received geometric dimensioning and tolerancing information into variation parameters for a three dimensional variation analysis tool; and output the variation parameters.

System and method for translating variation information between computer 3D variation models and geometric dimensioning and tolerancing callouts

A method of re-engineering a part includes generating a parametric master model for the part from an editable geometry for the part and generating a manufacturing context model from a design master model. The design master model includes the parametric master model, and the manufacturing context model includes a number of tooling features. The method further includes creating a tooling master model from the manufacturing context model. The tooling master model includes a tooling geometry for the part. A system for re-engineering a part includes a part design master model module configured to generate the parametric master model from the editable geometry and a tooling master model module configured to receive the parametric master model, to generate the manufacturing context model from the parametric master model, and to create the tooling master model from the manufacturing context model.

Method and system for reverse and re-engineering parts

A method of creating a tooling master model for a manufacturing process for a part includes generating a manufacturing context model from a parametric model for the part. The tooling master model includes a tooling geometry for the part, and the manufacturing context model includes a number of tooling features. A system for generating the tooling master model includes a computer aided design (CAD) system configured to receive the parametric model and to generate the manufacturing context model from the parametric model.

Method and system for creating a tooling master model for manufacturing parts

A method for performing geometric dimension and tolerance stack-up analysis for an assembly, the method comprising receiving a target assembly dimension for stack-up analysis, where the assembly includes at least one part. The method further comprises receiving a feature corresponding to the part and receiving feature tolerance data associated with the feature. The feature tolerance data includes at least one of size tolerance and geometric tolerance. Stack-up rules are accessed in response to receiving the feature tolerance data. The stack-up rules include instructions to determine if a form tolerance, an orientation tolerance and a profile tolerance should be included in a stack-up tolerance for the feature. The stack-up rules also include formulas to calculate a nominal dimension and the stack-up tolerance for the feature when the feature tolerance data applies to features of sizes. The nominal dimension and the stack-up tolerance are derived in response to the stack-up rules and the feature tolerance data. Stack-up analysis is performed in response to the nominal dimension and the stack-up tolerance. Performing stack-up analysis results in a mean and standard deviation for the target assembly dimension.

Method, system and computer product for performing geometric dimension and tolerance stack-up analysis

A method of nesting a computer model of a part ( ) into a computer model of a fixture ( ) useful for situations where the original positions of the part and fixture are completely separate and for situations where the respective part and fixture surfaces overlap. The model of the fixture is first inset by a distance D sufficient to eliminate any overlap between the modeled surfaces. The minimum normal distance segment ( ) between the inset fixture surface and the part surface may then be determined using standard CAD system capabilities. A vector is then constructed having a length D beginning at the minimum distance point on the inset fixture surface ( ) and extending in the direction of the minimum normal distance segment ( ) to a point ( ) on the original fixture model surface ( ). The minimum distance segment between point ( ) and the surface of the part ( ) is then determined to identify a point ( ). The respective point pair ( ) is recorded. Similar point pairs are recorded for each degree of freedom of movement between the part and the fixture. An orthonormal transformation matrix is then identified for moving the part to a position that minimizes the aggregate distance between the respective point pairs. This process may be repeated until the aggregate distance satisfies an acceptance criteria, whereupon the part model is deemed to be nested into the fixture model.

Method for nesting a computer model of a part with a computer model of a fixture

A method ( ) for manufacturing a precision part ( ) utilizing a non-precision fixture ( ). The non-precision fixture is precisely measured ( ) and modeled in a CAD program ( ) together with a model of the part ( ). The part model is nested ( ) into the fixture model, and a transformation matrix describing the movement of a coordinate system of the part during the step of nesting is recorded ( ). The transformation matrix may then be used to transform ( ) a tool path that had been developed for the originally designed shape of the fixture. Accordingly, imprecision in the location of a part within an imprecisely measured fixture may be accounted for during subsequent manufacturing operations.

Method of manufacturing precision parts with non-precision fixtures

A method for determining an adaptive feedrate for a machine tool used to machine a workpiece initially cut from stock using a stock-cutting program. Initial workpiece geometry is obtained from a geometry modeling program using the stock-cutting NC program as an input. Such automatically-generated initial workpiece geometry is used to derive adjusted (i.e., adaptive) feedrates in accordance with a known method.

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Schenectady, NY, United States of America

Dean Michael Robinson