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: 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.

Abstract: A method, medium, and system to receive an indication of an assembly including a plurality of different components to produce by additive manufacturing (AM), determine based on a forward search process that starts with individual components of the plurality of components and iteratively adds other components of the plurality of components thereto in combinations until an entirety of the assembly is accounted for, determine based on a reverse search process that starts with the entirety of the assembly and iteratively removes components of the plurality of components therefrom in combinations until a two-component set is reached, automatically select an optimal combination of the plurality of components from the combinations determined by the forward search and reverse search processes; and transmit the selection of the optimal combination of the plurality of components to an AM controller.

Abstract: Computer-implemented methods, computer program products, and computer systems for testing a voice assistant device may include one or more processors configured for receiving test data from a database, wherein the test data may include a first set of coding parameters and a first user utterance having an expected device response. Further, the one or more processors may be configured for generating a first modified user utterance by applying the first set of coding parameters to the first user utterance, wherein the first modified user utterance is acoustically different than the first user utterance. The one or more processors may be configured for audibly presenting the first modified user utterance to a voice assistant device, receiving a first device response from the voice assistant device, and determining whether the first voice assistant response is substantially similar to the expected device response.

Abstract: A computer-implemented method for sender activated mobile call overloading (SAMCO) is described that includes processors configured for receiving request call data comprising a data request initiated by a sending mobile device intended for a receiving mobile device, receiving active call data corresponding to a telephone call placed by the sending mobile device to the receiving mobile device, wherein the active call data comprises the data request, determining that the receiving mobile device is subscribed to a data request service configured to execute the data request, determining that the sending mobile device is authorized to access the receiving mobile device, and responsive to determining that the sending mobile device is authorized, the method includes processors configured for transmitting the data request to the receiving mobile device.

Abstract: A computer implemented method manages zero-day vulnerabilities in an application package having a set of components. The computer ingests data about potential vulnerabilities from a plurality of data sources. Using a set of machine learning models, the computer predicts a vulnerability based on of the data that was ingested. The computer performs a code analysis of the set of components to identify a possibility of the vulnerability impacting the application package. The computer generates a recommendation to resolve the vulnerability based on the code analysis and the data that was ingested. The computer manages the recommendation in a private blockchain.

Abstract: A system, method, and computer program product for implementing microservice deployment is provided. The method includes receiving definitions associated with microservices related to implementing hardware and software solutions with respect to hardware and software systems. In response, a definition file and associated code comprising the definitions and associated dependencies associated with the microservices are generated. The microservices are deployed to a container orchestration system cluster on a cloud structure and a service mesh and machine learning module are installed within the container orchestration system cluster. Proxies comprising a proxy for each pair of dependencies are generated and network traffic passing through each proxy is monitored. A malfunction of a hardware or software system is detected and an associated network route passing through an associated proxy is disabled.

Abstract: A method for recycling a used rotor blade of a wind turbine includes processing the used rotor blade into a plurality of material fragments. The method also includes treating the plurality of material fragments to remove at least a portion of the at least one composite material and expose the at least one fiber material of the used rotor blade. Further, the method includes mixing the treated plurality of material fragments with, at least, an alkali activator to form a usable geopolymer concrete.

Abstract: According to some embodiments, systems and methods are provided comprising receiving, via a communication interface of an authorization module comprising a processor, a part file with instructions to manufacture one or more parts with an additive manufacturing machine; generating a shape signature for the part based on the part file; providing a data store storing one or more stored shape signatures, wherein the one or more stored shape signatures are one of an authorized-to-print stored shape signature and an unauthorized-to-print stored shape signature; determining the generated shape signature of the part corresponds to at least one of the authorized-to-print stored shape signatures or at least one of the unauthorized-to-print stored shape signatures; and receiving the determination of whether the generated shape signature of the part corresponds to at least one authorized-to-print stored shape signature or at least one unauthorized-to-print stored shape signature. Numerous other aspects are provided.

Abstract: Embodiments of the present invention provide a computer system a computer program product, and a method that comprises converting the retrieved data to a uniform syntax for data assessment; performing a query on a plurality of external data sources for additional information associated with the converted data; analyzing a plurality of indicative markers associated with the retrieved data and the additional information; generating a plurality of machine learning models associated with the converted data based on the analysis of each indicative markers within the plurality of indicative markers; dynamically selecting at least one generated machine learning model within the plurality of generated machine learning models associated with the retrieved data based on an analysis of the plurality of indicative markers associated with the retrieved data and the additional information; and automatically verifying an accuracy value associated with the at least one selected generated machine learning model.

Abstract: An industrial asset item definition data store may contain at least one electronic record defining the industrial asset item. An automated support structure creation platform may include a support structure optimization computer processor. The automated support structure optimization computer processor may be adapted to automatically create support structure geometry data associated with an additive printing process for the industrial asset item. The creation may be performed via an iterative loop between a build process simulation engine and a topology optimization engine.

Abstract: A method, medium, and system to receive a specification defining a model of a part to be produced by an additive manufacturing (AM) process; execute an AM simulation on the model of the part to determine a prediction of thermal distortions to the part; execute a topology optimization (TO) to create TO supports that counteract the predicted thermal distortions; generate at least one rule-based support based on a geometry of the part to interface with the part at one or more regions other than the TO supports; combining the TO supports and the at least one rule-based support to generate a set of hybrid supports; save a record of the set of hybrid supports; and transmit the record of the set of hybrid supports to an AM controller to control an AM system to generate a support structure for an AM production of the part.

Abstract: Computer-implemented methods, computer program products, and computer systems for testing a voice assistant device may include one or more processors configured for receiving test data from a database, wherein the test data may include a first set of coding parameters and a first user utterance having an expected device response. Further, the one or more processors may be configured for generating a first modified user utterance by applying the first set of coding parameters to the first user utterance, wherein the first modified user utterance is acoustically different than the first user utterance. The one or more processors may be configured for audibly presenting the first modified user utterance to a voice assistant device, receiving a first device response from the voice assistant device, and determining whether the first voice assistant response is substantially similar to the expected device response.

Abstract: 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.

Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

Abstract: A method, medium, and system to execute an additive manufacturing (AM) simulation on a model of a part; determine, based on the AM simulation, a prediction of a temperature and displacement distribution in the part at a particular time in the AM process; apply the predicted temperature and displacement distributions in the part as a boundary conditions on a support design space to determine a temperature distribution throughout the support design space; and execute a thermal-structural topology optimization based on the determined temperature and displacement distributions throughout the support design space to determine a distribution of material in the design space for a thermal support structure to interface with the part that optimally reduces a thermal gradient in the part with a minimum of material and results in the generation of an optimized AM support structure.

Abstract: 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.

Abstract: Automated fixture layout is approached in two distinct stages. First, the spatial locations of clamping points on the work piece are determined to ensure immobility of the fixtured part under any infinitesimal perturbation. Second, spatial locations are matched against a user-specified library of reconfigurable clamps to synthesize a valid fixture layout or configuration that includes clamps that are accessible and collision free. The spatial locations matching during the second stage can be the same spatial locations chosen in the first stage to ensure immobility, or a different set of spatial locations.

Abstract: An exemplary additive manufacturing method includes receiving a build file comprising instructions for controlling the manufacturing hardware to generate an object, receiving a material identifier indicating a particular lot of manufacturing media, validating the build file and the material identifier via a distributed ledger to verify both an author of the build file and an origin of the particular lot of manufacturing media, causing manufacturing hardware to generate the object using the build file and the particular lot of manufacturing media, generating an object manufactured transaction to the distributed ledger indicating a result of the validation of the origin of the at least one of the build file or the material identifier, and certifying the object in response to verifying the author of the build file and the origin of the particular lot of manufacturing media, and wherein the object manufactured transaction indicates that the object is certified.

Abstract: Container orchestration software that controls instantiation, deployment, revision and removal of containers located in more than one cloud (that is, each container is located in a single cloud, but the set of orchestrated containers includes containers in more than one cloud). In some embodiments, the container orchestration software takes the form of a master node. In some embodiments, the container orchestration software takes the form of a Kubernetes type master node.