General Electric Company

Megha Navalgund

Mattias Fager

Manoharan Venugopal

Rajashekar Venkatachalam

V S S Srivatsa Ponnada

Venkata Dharma Surya Narayana Sastry Rachakonda

Pär Christoffer Arumskog

Vikram Reddy Melapudi

Xiaoming Liu

Robert August Kaucic

Mahesh Kumar Asati

Gokul Swamy

Sharath Sridhar Aramanekoppa

Prafull Sharma

Joseph Manuel Portaz

Paer Christoffer Arumskog

Venkata Rachakonda

Sharan Arumugam

Aman Tiwari

Simon Blomé

Fredrik Tommy Mohlin

Manikandasriram Srinivasan Ramanagopal

Prakash Mandayam Comare

Anders Ingvarsson

Ponnada V S S Srivatsa

Srikanth Reddy Tenkayyagaari

Steven T Slusher

An additive manufacturing system may include an additive manufacturing machine and a control system. The additive manufacturing machine may include one or more irradiation devices respectively including a beam source configured to emit an energy beam, an optical assembly that has one or more optical elements configured to focus the energy beam emitted by the beam source, a beam source sensor configured to determine a beam source sensor value from a source measurement beam representative of the energy beam prior to the energy beam passing through one or more optical elements of the optical assembly, and an optics sensor configured to determine an optics sensor value from an optics measurement beam representative of the energy beam downstream from the one or more optical elements of the optical assembly. The control system may include an irradiation control module configured to provide one or more control commands to the additive manufacturing machine based at least in part on the beam source sensor value and/or based at least in part on the optics sensor value.

Energy beam systems for additive manufacturing machines

A system for diagnosing an additive manufacturing device is provided. The system includes one or more processors, one or more non-transitory memory modules communicatively coupled to the one or more processors and storing machine-readable instructions. The machine-readable instructions, when executed, cause the one or more processors to: determine parameters associated with at least one subsystem of the additive manufacturing device, the parameters being related to a build generated by the additive manufacturing device; compare the parameters with threshold values; and determine a failure mode, among a plurality of failure modes, associated with a subsystem of the at least one subsystem of the additive manufacturing device based on the comparison of the parameters with the threshold values.

Systems, and methods for diagnosing an additive manufacturing device

A system for diagnosing an additive manufacturing device is provided. The system includes a first module configured to: obtain one or more parameters for a digital twin of a component of the additive manufacturing device based on raw data from the component of the additive manufacturing device; and generate physics features for the digital twin of the component of the additive manufacturing device based on the one or more parameters and one or more transfer functions, a second module configured to obtain one or more classifiers for classifying the component as a first condition or a second condition based on physics features; and a third module configured to: determine a health of the component based on the generated physics features of the first model and the one or more classifiers.

Systems, and methods for diagnosing an additive manufacturing device using a physics assisted machine learning model

A system includes a plurality of movable members and an electromagnetic wave probe disposed within at least one movable member among the plurality of movable members. The actuation system is coupled to the plurality of movable members. The actuation system is configured to actuate and removably position the plurality of movable members to form a wave guide around at least a portion of an outer peripheral surface of the pipe assembly and position the electromagnetic wave probe proximate to the portion of the outer peripheral surface of the pipe assembly.

System and method for detecting an anomaly in a pipe assembly

A method for automatically identifying defects in turbine engine blades is provided. The method comprises acquiring one or more radiographic images corresponding to one or more turbine engine blades and identifying one or more regions of interest from the one or more radiographic images. The method then comprises extracting one or more geometric features based on the one or more regions of interest and analyzing the one or more geometric features to identify one or more defects in the turbine engine blades.

Method for automatic identification of defects in turbine engine blades

A method for processing a radiographic image of a scanned object is provided. The method comprises acquiring radiographic image data corresponding to a scanned object and identifying one or more regions of interest in the radiographic image data corresponding to the scanned object. The method further comprises performing an image-contrast comparison of the radiographic image data corresponding to the scanned object and one or more reference radiographic images, to identify one or more defects in the radiographic image data corresponding to the scanned object.

Method and system for identifying defects in a radiographic image of a scanned object

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Bengaluru, India

Megha Navalgund