General Electric Company

Ge Infrastructure Technology, LLC

Subhrajit Roychowdhury

Xiaohu Ping

Michael Evans Graham

Harry Kirk Mathews, Jr

Justin John Gambone, Jr

William Thomas Carter

Naresh Sundaram Iyer

Vipul Kumar Gupta

Robert John Filkins

Masoud Abbaszadeh

Thomas Charles Adcock

Rogier Sebastiaan Blom

Brian Scott McCarthy

John Erik Hershey

Lembit Salasoo

Matthias Hoebel

Thomas Graham Spears

David Charles Bogdan, Jr

Mustafa Tekin Dokucu

Dean Michael Robinson

John Joseph Freer

Natarajan Chennimalai Kumar

Saikat Ray Majumder

Alexander T Chen

Victor Petrovich Ostroverkhov

Prabhjot Singh

Laura Cerully Dial

Timothy Hanlon

Felix Martin Gerhard Roerig

Anthony Joseph Vinciquerra

John Joseph Madelone, Jr

Michael Robert Tucker

Voramon Supatarawanich Dheeradhada

Juan Borja

Randal T Rausch

Sarah Felix

Joshua Tyler Mook

Daniel Jason Erno

Xiaolei Shi

Todd Jay Rockstroh

John Broddus Deaton, Jr

Arvind Rangarajan

Justin Varkey John

Donnell Eugene Crear

Srinivas Bollapragada

Balakrishna Pamulaparthy

Younkoo Jeong

David Toledano

Thomas Etter

James William Sears

Mohammed Mounir Shalaby

Lang Yuan

Ruijie Shi

Olugbenga Moses Anubi

Mengli Wang

Christina Margaret Vasil

Harry Kirk Matthews, Jr

Christopher Darby Immer

Justin John Gambone

Christian Wacker

Nitish Umang

Mitalkumar Kanabar

Jing Yu

Anthony J Vinciquerra, Iii

Brian Scott Mccarthy

Kai Hertel

Pubudu Eroshan Weerathunga

Jr Justin Gambone

David C Bogdan Jr

Jr John Broddus Deaton

Jr John Joseph Madelone

Harry Kirk Mathews

An additive manufacturing apparatus, a computing system, and a method for operating an additive manufacturing apparatus are provided. The method includes obtaining two or more images corresponding to respective build layers at a build plate, wherein each image comprises a plurality of data points comprising a feature and corresponding location at the build plate; removing variation between the features of the plurality of data points; and normalizing each feature to remove location dependence in the plurality of data points.

System and method for additive manufacturing control

Systems and methods for power system switching element (PSSE) anomaly detection are disclosed. An example PSSE anomaly detection unit may include a power system switching element position estimator (PSSEPE) and a comparison unit. The PSSEPE may be configured to receive a set of measurements and a set of control commands associated with a PSSE, calculate an anomaly confidence score based on the set of measurements and the set of control commands, and estimate a calculated PSSE position based on the set of measurements and the set of control commands. The comparison unit may be configured to receive the calculated PSSE position from the PSSEPE, receive the set of measurements and the set of control commands from the PSSEPE, receive a reported PSSE position associated with the PSSE, and determine a PSSE anomaly decision based on a difference between the reported PSSE position and the calculated PSSE position.

Systems and methods for power system switching element anomaly detection

Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.

System and methods for generating fault indications for an additive manufacturing process based on a probabilistic comparison of the outputs of multiple process models to measured sensor data

A system includes a first group of optic lenses within a focusing unit positioned along the propagation direction of a collimated laser beam, the first group of optic lenses separated by a predetermined fixed distance. The first group of optic lenses in conjunction cause the collimated beam to form as an annular beam as it passes through the first group of optic lenses. An axicon lens located distal from the first group of optic lenses along the propagation direction, the axicon lens operable to bifurcate the annular beam into two deflected collimated beam sections, and the axicon lens having a focus that causes the two deflected collimated beam sections to merge at a distance distal from the axicon lens to create an interference pattern region.

Systems and methods for direct laser melting of metals using non-diffracting laser beams

A powder melting device for an additive manufacturing system including a laser device configured to emit an energy beam and a beam modulator. The beam modulator is configured to selectively induce an angular deflection in the energy beam for a predetermined time period such that the energy beam generates a plurality of melt pools in a powder bed.

Systems and methods for fabricating a component with a simulated laser array

According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a parameter development module comprising a processor, a defined geometry for one or more parts, wherein the parts are manufactured with an additive manufacturing machine, and wherein a stack is formed from one or more parts; fabricating the one or more parts with the additive manufacturing machine based on a first parameter set; collecting in-situ monitoring data from one or more in-situ monitoring systems of the additive manufacturing machine for one or more parts; determining whether each stack should receive an additional part based on an analysis of the collected in-situ monitoring data; and fabricating each additional part based on the determination the stack should receive the additional part. Numerous other aspects are provided.

In-situ monitoring system assisted material and parameter development for additive manufacturing

An example additive manufacturing apparatus includes an energy source to melt material to form a component in an additive manufacturing process, a camera aligned with the energy source to obtain image data of the melted material during the additive manufacturing process, and a controller to control the energy source during the additive manufacturing process in response to processing of the image data. The controller adjusts control of the energy source based on a correction determined by: applying an artificial intelligence model to image data captured by a camera during an additive manufacturing process, the image data including an image of a melt pool of the additive manufacturing process; predicting an error in the additive manufacturing process using an output of the artificial intelligence model; and compensating for the error by generating a correction to adjust a configuration of the energy source during the additive manufacturing process.

Systems and methods for compression, management, and analysis of downbeam camera data for an additive machine

A method, medium, and system to obtain an assignment of one of a plurality of different additive manufacturing (AM) print parameter sets to each of a plurality of 3D volume elements forming a representation of the model of the part; define a plurality of groupings based on margin parameter values associated with the model of the part; assign a print parameter set to each grouping; automatically assign each of the plurality of print parameter sets to a laser of a multi-laser AM device; save a record of the determined print parameter sets to laser assignments; and transmit the record of the determined print parameter sets to laser assignments to an AM controller to control the multi-laser AM device to generate the part based on the model of the part and the determined print parameter sets to laser assignments, the generated part to be built in a single build.

Assignment of multiple print parameter sets in additive manufacturing

An industrial asset may have monitoring nodes that generate current monitoring node values. A dynamic, resilient estimator may split a temporal monitoring node space into normal and one or more abnormal subspaces associated with different kinds of attack vectors. According to some embodiments, a neutralization model is constructed and trained for each attack vector using supervised learning and the associated abnormal subspace. In other embodiments, a single model is created using out-of-range values for abnormal monitoring nodes. Responsive to an indication of a particular abnormal monitoring node or nodes, the system may automatically invoke the appropriate neutralization model to determine estimated values of the particular abnormal monitoring node or nodes (e.g., by selecting the correct model or using out-of-range values). The series of current monitoring node values from the abnormal monitoring node or nodes may then be replaced with the estimated values.

Virtual sensor supervised learning for cyber-attack neutralization

According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a part parameter dictionary module comprising a processor, geometry data for a plurality of geometric structures forming a plurality of parts, wherein the parts are manufactured with an additive manufacturing machine; determining, using the processor of the part parameter dictionary module, a feature set for each geometric structure; generating, using the processor of the part parameter dictionary module, one of a coupon and a coupon set for the feature set; generating an optimized parameter set for each coupon, using the processor of the part parameter dictionary module, via execution of an iterative learning control process for each coupon; mapping, using the processor of the part parameter dictionary module, one or more parameters of the optimized parameter set to one or more features of the feature set; and generating a dictionary of optimized scan parameter sets to fabricate geometric structures with a material used in additive manufacturing. Numerous other aspects are provided.

Transfer learning/dictionary generation and usage for tailored part parameter generation from coupon builds

Determining a quality score for a part manufactured by an additive manufacturing machine based on build parameters and sensor data without the need for extensive physical testing of the part. Sensor data is received from the additive manufacturing machine during manufacture of the part using a first set of build parameters. The first set of build parameters is received. A first algorithm is applied to the first set of build parameters and the received sensor data to generate a quality score. The first algorithm is trained by receiving a reference derived from physical measurements performed on at least one reference part built using a reference set of build parameters. The quality score is output via the communication interface of the device.

System and methods for determining a quality score for a part manufactured by an additive manufacturing machine

A method of transferring operational parameter sets between different domains of additive manufacturing machines includes creating a first machine domain parameter set in a first machine domain, accessing a model of a second additive manufacturing in a second machine domain, creating a second machine domain parameter set by applying transfer learning techniques including learning differences between the first machine domain and the second machine domain, adjusting the first machine domain parameter set using the differences before incorporation into the second machine domain to obtain the second machine domain parameter set, the second machine domain parameter set representing operational settings for the second additive manufacturing machine, the second additive manufacturing machine producing a product sample, determining if the product sample is within quality assurance metrics, and if the product sample is not within the quality assurance metrics, adjusting the second machine domain parameter set.

Methods and systems for generating device-specific machine learning model

An industrial asset may have monitoring nodes (e.g., sensor or actuator nodes) that generate current monitoring node values. An abnormality detection and localization computer may receive the series of current monitoring node values and output an indication of at least one abnormal monitoring node that is currently being attacked or experiencing a fault. An actor-critic platform may tune a dynamic, resilient state estimator for a sensor node and output tuning parameters for a controller that improve operation of the industrial asset during the current attack or fault. The actor-critic platform may include, for example, a dynamic, resilient state estimator, an actor model, and a critic model. According to some embodiments, a value function of the critic model is updated for each action of the actor model and each action of the actor model is evaluated by the critic model to update a policy of the actor-critic platform.

Dynamic, resilient virtual sensing system and shadow controller for cyber-attack neutralization

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Schenectady, NY, United States of America

Subhrajit Roychowdhury