Abstract: 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 includes 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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: 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.

Abstract: 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.

Abstract: Methods and apparatus for sensor-based part development are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to translate at least one user-defined material property selection into a desired process observable, the desired process observable including a meltpool property, perform voxel-based autozoning of an input part geometry, the input part geometry based on a computer-generated design, and output a voxelized reference map for the input part geometry based on the desired process observable and the voxel-based autozoning.

Abstract: Methods and apparatus for sensor-based part development are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to identify a reference process observable of a computer-generated part, receive input from at least one sensor during three-dimensional printing to identify an estimated process observable using feature extraction, and adjust at least one three-dimensional printing process parameter to reduce an error identified from a mismatch between the estimated process observable and the reference process observable.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 str

Abstract: The present disclosure provides techniques for implementing self-adapting neutralization against cyber-faults within industrial assets. The disclosed neutralization techniques may include obtaining an input dataset from a plurality of nodes of industrial assets and reconstructing compromised nodes in the plurality of nodes to neutralize cyber-faults detected based on the input dataset. A confidence metric may be computed for the reconstruction of the compromised nodes, e.g., using inductive conformal prediction. Based on the confidence metric and the reconstruction of the compromised nodes, input signals from the reconstruction of the compromised nodes may be transformed, or configuration parameters for a controller of the industrial assets may be tuned.

Abstract: The present disclosure relates to techniques for detecting cyber-faults in industrial assets. Such techniques may include obtaining an input dataset from a plurality of nodes of industrial assets and predicting fault nodes in the plurality of nodes by inputting the input dataset to a one-class classifier. The one-class classifier may be trained on normal operation data obtained during normal operations of the industrial assets. Further, the cyber-fault detection techniques may include computing a confidence level of cyber fault detection for the input dataset using the one-class classifier and adjusting decision thresholds based on the confidence level for categorizing the input dataset as normal or including cyber-faults. The predicted fault nodes and the adjusted decision thresholds may be used for detecting cyber-faults in the plurality of nodes of the industrial assets.

Abstract: 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.