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: 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: 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 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 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: 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: 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: 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: Some aspects are directed to additive manufacturing systems. An example additive manufacturing system controller is configured to receive a build file comprising instructions for controlling the manufacturing hardware to generate the object, receive a material identifier indicating a particular lot of manufacturing media, validate the build file and the material identifier via a distributed ledger to verify at least one of an author of the build file or an origin of the particular lot of manufacturing media, control the manufacturing hardware using the build file to generate the object using the particular lot of manufacturing media, and in response to completion of the generation of the object, generate 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.

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: 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: 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: 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: 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: 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: According to some embodiments, 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, for example, 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, according to some embodiments, via an iterative loop between a build process simulation engine and a topology optimization engine.

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: 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 system and a method automate metrology, measurement, and model correction of a three dimensional (3D) model for 3D printability. Slices of the 3D model are received or generated. The slices represent 2D solids of the 3D model to be printed in corresponding print layers. Medial axis transforms of the slices are calculated. The medial axis transforms represent the slices in terms of corresponding medial axes. A local feature size at any point along a boundary of the slices is determined as the shortest distance from the point to a corresponding medial axis.

Abstract: Some aspects are directed to additive manufacturing systems. An example additive manufacturing system controller is configured to receive a build file comprising instructions for controlling the manufacturing hardware to generate the object, receive a material identifier indicating a particular lot of manufacturing media, validate the build file and the material identifier via a distributed ledger to verify at least one of an author of the build file or an origin of the particular lot of manufacturing media, control the manufacturing hardware using the build file to generate the object using the particular lot of manufacturing media, and in response to completion of the generation of the object, generate 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.