Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system, method, and computer-readable medium, to receive a query to execute against a knowledge graph, the knowledge graph representing information pertaining to a particular scientific domain and the query including at least one variable represented by the knowledge graph; examining the knowledge graph to identify variables therein that are also specified in the query; generate a scientific model based on the query and the identified variables in the knowledge graph the execution of the model providing an answer to the query; and transmitting a record of the generated model to a data store and persisting the record in the data store.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system for similarity analysis-based information augmentation for a target component includes an information augmentation (IA) computer device. The IA computer device identifies a target component input variable with unavailable data. The IA computer device executes a similarity analysis function, identifying at least two test components with data for the input variable exceeding a threshold. The IA computer device generates parameter distributions for test data for each test component. The IA computer device generates model coefficients using the parameter distributions, determining a proportional mix of the parameter distributions. The IA computer device authors a predictive model configured to generate at least one predicted value for the target data for the at least one input variable for the target component by including the at least one model coefficient in the predictive model. The IA computer device generates, using the predictive model, the at least one predicted value.

Abstract: 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.

Abstract: 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.

Abstract: A system for determining a decrease in service life to a target component is provided. The system includes a service life modeling (SLM) computing device, which identifies a physics variable for a test component. The SLM computing device generates a likelihood function for the physics variable. The SLM computing device applies probabilistic techniques to the physical measurements together with a set of coefficients. The SLM computing device generates a hybrid service life model for the test component. The SLM computing device calibrates the hybrid service life model. The SLM computing device applies the hybrid service life model to a target component that shares characteristics with the test component. The SLM computing device identifies a predictive metric for the target component. The SLM computing device outputs the metric. The SLM computing device directs an operator to modify a maintenance plan for the target component based on the metric.