General Electric Company

Natarajan Chennimalai Kumar

Vipul Kumar Gupta

Subhrajit Roychowdhury

Arun Karthi Subramaniyan

Laura Cerully Dial

Timothy Hanlon

Anthony Joseph Vinciquerra

Voramon Supatarawanich Dheeradhada

Lembit Salasoo

Justin John Gambone, Jr

Xiaohu Ping

Alexandre Nikolov Iankoulski

Fabio Nonato De Paula

Liping Wang

John Lazos

Renato Giorgiani Do Nascimento

Felipe Antonio Chegury Viana

Justin John Gambone

Randal T Rausch

Albert Rosario Cerrone

Anthony J Vinciquerra, Iii

Isaac Mendel Asher

You Ling

Ankur Srivastava

Felipe Viana

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

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 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, apparatus and methods are provided comprising one or more component models of an analytic model for an installed product; an application programming interface (API) wrapper associated with each of the one or more component models, the API wrapper including information about one or more inputs to the component model; and wherein the component model and the API wrapper form a self-aware component. Numerous other aspects are provided.

System architecture for secure and rapid development, deployment and management of analytics and software systems

A system for estimating data in large datasets for an equipment system is provided. The system includes a data estimation and forecasting (DEF) computing device. The DEF computing device arranges a dataset in a primary matrix and parses rows of the primary matrix and generates a sample matrix by selecting primary matrix rows having non-null values for each variable. The DEF computing device adds to the sample matrix rows that include non-null values for each variable except one. The DEF computing device generates normalized values for this augmented matrix, applies several techniques including probabilistic principal component analysis (PPCA) and Markov processes, and scales the augmented matrix to normalized values. The DEF computing device generates non-null values for the variable, scales the augmented matrix back to the sample matrix, and generates a forecast for the equipment system, directing a user to update logistics processes for the equipment system.

Systems and methods for data estimation and forecasting

Some aspects are directed to data reconciliation frameworks. An example framework is configured to receive core data, the system model comprising a plurality of data records associated with at least two assets, receive a system model, the system model comprising context data indicating, execute a configuration operation of a data validation process based on the system model, execute the data validation process to identify at least one inconsistent or incomplete record among the plurality of data records, determine at least one data reconciliation technique from a plurality of data reconciliation techniques based on the system model, and apply the at least one data reconciliation technique to the core data to reconcile the at least one inconsistent or incomplete record.

Methods and systems for implementing a data reconciliation framework

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Niskayuna, NY, United States of America

Natarajan Chennimalai Kumar