General Electric Company

Vipul Kumar Gupta

Subhrajit Roychowdhury

Timothy Hanlon

Justin John Gambone, Jr

Anthony Joseph Vinciquerra

Laura Cerully Dial

Xiaohu Ping

Natarajan Chennimalai Kumar

Lembit Salasoo

Voramon Supatarawanich Dheeradhada

Joshua Tyler Mook

Arvind Rangarajan

Brian Scott McCarthy

Christina Margaret Vasil

Randal T Rausch

Anthony J Vinciquerra, Iii

John Erik Hershey

Xiaolei Shi

Naresh Sundaram Iyer

Scott Andrew Weaver

Scott Michael Oppenheimer

Andrew David Deal

Erica Elizabeth Sampson

Mengli Wang

Alexander T Chen

Justin John Gambone

Jr Justin Gambone

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

A method, medium, and system including determining a material property value to assign to each of the plurality of 3D volume elements, wherein the material property values assigned to the plurality of 3D volume elements are classified into a predetermined number of bins that correspond to a plurality of different additive manufacturing (AM) print parameter sets, generating a plurality of transfer functions to determine relationships between the material property values assigned to the plurality of 3D volume elements and a plurality of desired AM print parameter sets, automatically determining, based on the plurality of transfer functions, an assignment of one of the plurality of different AM print parameter sets to each of the plurality of 3D volume elements, and validating the determined assignments of the plurality of different AM print parameter sets for the plurality of 3D volume elements based on the plurality of transfer functions.

Autozoning of additive manufacturing print parameters

A method of testing a multi-specimen additive manufacturing build plate includes acquiring and installing the multi-specimen build plate in a test system, aligning one or more force exertion tools with respective selected specimens, imparting a force on the selected specimen(s), collecting test data from each selected specimen, and analyzing the collected data to identify a potential correlation between material behavior for the selected specimen and its applied manufacturing build parameter(s). A system and a non-transitory medium are also disclosed.

High-throughput testing system and method for additive manufacturing

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

Methods and systems for optimizing additive process parameters for an additive manufacturing process. In some embodiments, the process includes receiving initial additive process parameters, generating an uninformed design of experiment utilizing a specified sampling protocol, next generating, based on the uninformed design of experiment, response data, and then generating, based on the response data and on previous design of experiment that includes at least one of the uninformed design of experiment and informed design of experiment, an informed design of experiment by using the machine learning model and the intelligent sampling protocol. The last process step is repeated until a specified objective is reached or satisfied.

Machine learning assisted development in additive manufacturing

A method, medium, and system to automatically determine parameter sets for an additive manufacturing (AM) of a part, the method including executing a load analysis on a model of a part to emulate a load on each of a plurality of regions of the part; determining a representation of the model of the part as a plurality of discrete three-dimensional (3D) volume elements; determining, based on an output of the load analysis, a life or material property value to assign to each of the plurality of 3D volume elements; automatically determining an assignment of one of a plurality of additive manufacturing (AM) print parameter sets to each of the plurality of 3D volume elements; and saving a record of the determined assignments of the AM print parameter sets to each of the plurality of 3D volume elements.

A method of additive manufacturing machine (AMM) build process control includes obtaining AMM machine and process parameter settings, accessing sensor data for monitored physical conditions in the AMM, calculating a difference between expected AMM physical conditions and elements of the monitored conditions, providing the machine and process parameter settings, monitored conditions, and differences to one or more material property prediction models, computing a predicted value or range for the monitored conditions, comparing the predicted value or range to a predetermined target range, based on a determination that predicted value(s) are within the predetermined range, maintaining the machine and process parameter settings, or based on a determination that one or more of the predicted value(s) is outside the predetermined range, generating commands to compensate the machine and process parameter settings, and repeating the closed feedback loop at intervals of time during the build process. A system and a non-transitory medium are also disclosed.

Quality assessment feedback control loop for additive manufacturing

According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a platform comprising a segmentation module and a processor, a defined geometry for one or more geometric structures forming one or more parts, wherein the parts are manufactured with an additive manufacturing machine; generating a build file including an initial parameter set to fabricate each part; fabricating the part based on the build file; receiving sensor data for the fabricated part; generating a parameter set for each layer that forms the part, via execution of an iterative learning control process for each layer; generating raw power data for each layer that forms the part, using the processor, based on the generated parameter set; applying a noise reduction process to the raw power data; and generating a segmented build file, using the segmentation module, via application of the noise reduction process on the raw power data. Numerous other aspects are provided.

Sensor based smart segmentation

An apparatus and method for rapid screening of material properties in a plurality of additively manufactured test specimens. The apparatus includes a build plate having the plurality of additively manufactured test specimens disposed on a first substantially planar surface. The plurality of additively manufactured test specimens are coupled to at least one actuator to one of individually or simultaneously translationally displace each of the test specimens along an axis 'z', and perpendicular to the build plane of the build plate to test material properties of each of the plurality of additively manufactured test specimens. A sensor is coupled to each of the plurality of additively manufactured test specimens. Load vs. displacement data may be used to monitor the progression of monotonic and/or cyclic tests of the plurality of additively manufactured test specimens.

Apparatus and method for rapid screening of material properties in a plurality of additively manufactured test specimens

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Guilderland, NY, United States of America

Vipul Kumar Gupta