Abstract: A method includes obtaining stamping system data from a stamping system data controller and vibration data from one or more vibration sensors disposed at the conduit; selecting a baseline vibration threshold and a frequency filter from among a plurality of frequency filters based on the stamping system data; generating a plurality of measured vibration values based on the vibration data and the frequency filter, where each measured vibration value from among the plurality of measured vibration values is associated with a given iteration from among a plurality of iterations of a stamping process; determining whether the conduit is in an obstructed state based on a comparison between a set of measured vibration values from among the plurality of measured vibration values and the baseline vibration threshold; and performing a corrective action in response to the conduit being in the obstructed state.

Abstract: A component inspection system and method generate a 3D model based on a point cloud and images of an automotive component captured by an imaging system. It is determined whether an anomaly is present based on artificial intelligence driven training and learning. Upon anomaly detection, a type of anomaly is identified and classified. From the 3D model, a type of the automotive component can be identified. The identification of the automotive component and the anomaly detection involve a controller subject to artificial intelligence driven training and learning. The controller determines presence of anomaly and a location of anomaly if any.

Abstract: A component inspection system and method generate a 3D model based on a point cloud and images of an automotive component captured by an imaging system. It is determined whether an anomaly is present based on artificial intelligence driven training and learning. Upon anomaly detection, a type of anomaly is identified and classified. From the 3D model, a type of the automotive component can be identified. The identification of the automotive component and the anomaly detection involve a controller subject to artificial intelligence driven training and learning. The controller determines presence of anomaly and a location of anomaly if any.

Abstract: A method of monitoring operation of a shredder or health of a shredder component includes: setting a profile of a selected parameter of the shredder, wherein the profile includes a nominal value, a tolerance of the nominal value, and a predetermined range of the selected parameter outside the tolerance; obtaining an actual value of the selected parameter; comparing the actual value with the profile of the selected parameter; determining the operational condition of the shredder based on a comparison between the actual value and the profile of the selected parameter. The profile is obtained based on experimental data, statistics, or machine learning.

Abstract: A method for forming a stamped component from a blank material with an industrial stamping machine during a stamping process includes measuring a plurality of parameters of the stamping process. The parameters are provided as variables of the stamping process. The method further includes analyzing, by a stamping process model, the plurality of parameters to adjust the stamping process for the blank material, defining, by the stamping process model, a control parameter of the industrial stamping machine for the blank material, and stamping the blank material with the industrial stamping machine based on the defined control parameter to form the stamped component.

Abstract: A method includes obtaining stamping system data from a stamping system data controller and vibration data from one or more vibration sensors disposed at the conduit; selecting a baseline vibration threshold and a frequency filter from among a plurality of frequency filters based on the stamping system data; generating a plurality of measured vibration values based on the vibration data and the frequency filter, where each measured vibration value from among the plurality of measured vibration values is associated with a given iteration from among a plurality of iterations of a stamping process; determining whether the conduit is in an obstructed state based on a comparison between a set of measured vibration values from among the plurality of measured vibration values and the baseline vibration threshold; and performing a corrective action in response to the conduit being in the obstructed state.

Abstract: A method of inspecting stamped blanks on a stamping line includes identifying at least one target defect location for a given stamped blank configuration where a unique defect type is associated with each of the at least one target defect locations. One or more images of each of the least one identified target defect locations on blanks stamped per the given stamped blank configuration are acquired with one or more cameras assigned to each of the identified target defect locations. The method includes analyzing the one or more images of each of the least one identified target defect locations and detecting if the unique defect type associated with each of the at least one target defect locations is present. Also, each unique defect type is identified with a corresponding unique defect identification algorithm.

Abstract: A component inspection system and method generate a 3D model based on a point cloud and images of an automotive component captured by an imaging system. It is determined whether an anomaly is present based on artificial intelligence driven training and learning. Upon anomaly detection, a type of anomaly is identified and classified. From the 3D model, a type of the automotive component can be identified. The identification of the automotive component and the anomaly detection involve a controller subject to artificial intelligence driven training and learning. The controller determines presence of anomaly and a location of anomaly if any.

Abstract: A component inspection system and method generate a 3D model based on a point cloud and images of an automotive component captured by an imaging system. It is determined whether an anomaly is present based on artificial intelligence driven training and learning. Upon anomaly detection, a type of anomaly is identified and classified. From the 3D model, a type of the automotive component can be identified. The identification of the automotive component and the anomaly detection involve a controller subject to artificial intelligence driven training and learning. The controller determines presence of anomaly and a location of anomaly if any.

Abstract: A method of monitoring one or more manufacturing components includes clustering vibration data associated with the one or more manufacturing components to generate a plurality of clusters, determining a vibration characteristic of the one or more manufacturing components based on the plurality of clusters, comparing auxiliary data associated with the one or more manufacturing components and an auxiliary data prediction model associated with the one or more manufacturing components, and determining an auxiliary characteristic of the one or more manufacturing components based on a comparison of the auxiliary data associated and the auxiliary data prediction model. The method includes determining a state of the one or more manufacturing components based on the vibration characteristic and the auxiliary characteristic and broadcasting a notification based on the state.

Abstract: An inspection system for automotive components includes one or more cameras, a controller, a conveyor structure, and a row of platforms. The cameras are configured to collect images corresponding to a selected automotive component. The controller is communicably coupled to the cameras. The conveyor structure is operable to transport the selected automotive component. The cameras are synchronized and triggered to capture images of the selected automotive component with the controller. The controller is configured to generate a composite image based on the data set and generate, at a production speed, an output indicative of the selected automotive component with or without anomaly.

Abstract: A component inspection system and method generate a 3D model based on a point cloud and images of an automotive component captured by an imaging system. It is determined whether an anomaly is present based on artificial intelligence driven training and learning. Upon anomaly detection, a type of anomaly is identified and classified. From the 3D model, a type of the automotive component can be identified. The identification of the automotive component and the anomaly detection involve a controller subject to artificial intelligence driven training and learning. The controller determines presence of anomaly and a location of anomaly if any.

Abstract: An inspection system for automotive components includes one or more cameras, a controller, a conveyor structure, and a row of platforms. The cameras are configured to collect images corresponding to a selected automotive component. The controller is communicably coupled to the cameras. The conveyor structure is operable to transport the selected automotive component. The cameras are synchronized and triggered to capture images of the selected automotive component with the controller. The controller is configured to generate a composite image based on the data set and generate, at a production speed, an output indicative of the selected automotive component with or without anomaly.

Abstract: A method of inspecting stamped blanks on a stamping line includes identifying at least one target defect location for a given stamped blank configuration where a unique defect type is associated with each of the at least one target defect locations. One or more images of each of the least one identified target defect locations on blanks stamped per the given stamped blank configuration are acquired with one or more cameras assigned to each of the identified target defect locations. The method includes analyzing the one or more images of each of the least one identified target defect locations and detecting if the unique defect type associated with each of the at least one target defect locations is present. Also, each unique defect type is identified with a corresponding unique defect identification algorithm.

Abstract: A method for forming a stamped component from a blank material with an industrial stamping machine during a stamping process includes measuring a plurality of parameters of the stamping process. The parameters are provided as variables of the stamping process. The method further includes analyzing, by a stamping process model, the plurality of parameters to adjust the stamping process for the blank material, defining, by the stamping process model, a control parameter of the industrial stamping machine for the blank material, and stamping the blank material with the industrial stamping machine based on the defined control parameter to form the stamped component.