Abstract: A method includes generating one or more nominal images of at least a portion of a digital twin of an environment, defining one or more anomalous characteristics of the one or more components, generating one or more anomalous images of the portion of the digital twin of the environment based on the field of view and the one or more anomalous characteristics, performing a tessellation routine and a texture mapping routine on the one or more nominal images and the one or more anomalous images to generate a plurality of synthetic images, and labeling, for each synthetic image from among the plurality of synthetic images, the synthetic image as one of an anomalous type, a nominal type, or a combination thereof.

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 includes generating one or more nominal images of at least a portion of a digital twin of an environment, defining one or more anomalous characteristics of the one or more components, generating one or more anomalous images of the portion of the digital twin of the environment based on the field of view and the one or more anomalous characteristics, performing a tessellation routine and a texture mapping routine on the one or more nominal images and the one or more anomalous images to generate a plurality of synthetic images, and labeling, for each synthetic image from among the plurality of synthetic images, the synthetic image as one of an anomalous type, a nominal type, or a combination thereof.

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 flexible inspection system includes a robot with a plurality of scanners and a robot controller. The robot controller is configured to receive a vehicle inspection protocol (VIP) for a vehicle being assembled on an assembly line. The VIP includes checkpoints to be scanned on the vehicle and the checkpoints correspond to components installed on the vehicle and connections between components installed on the vehicle. The robot controller commands the robot to move the plurality of scanners per the VIP such that the checkpoints are scanned. A characteristic of each checkpoint is recorded and compared to a reference characteristic such that a pass or no-pass determination of each checkpoint is provided. A vehicle inspection report with the pass/no-pass determinations is provided to an operator such that operator inspections and/or repairs of the checkpoints are made.

Abstract: A method of inspecting an electric motor includes scanning an electric motor stator winding with a 2D or 3D camera, acquiring one or more images of a plurality welds between adjacent electrical wires forming the stator winding using the 2D camera, analyzing the one or more acquired images with at least one neural network such that the neural network determines if at least one of the plurality of welds has a weld defect. The at least one neural network is trained and distinguishes between surface discoloration on a surface of the welds and defect discoloration resulting from contamination during welding. Also, the method inspects over 150 welds per electric motor stator winding moving along an assembly line.

Abstract: A system for providing a thermal interface material on a tray for an electric vehicle battery includes a material dispensing system (MDS) and a vision system. The MDS includes a set of pumps, a robotic dispensing system (RDS), and a controller. The set of pumps includes a first pump to house a first material and a second pump to house a second material different from the first material. The RDS is fluidly coupled to the set of pumps and dispenses a third material on the tray in a defined pattern, where the third material is a mixture of the first and second materials. The vision system is configured to capture a first set of images. The controller is configured to determine a nozzle offset of the RDS based on the first set of images and control a position of the RDS based on the nozzle offset.

Abstract: A method includes generating one or more nominal images of at least a portion of a digital twin of an environment, defining one or more anomalous characteristics of the one or more components, generating one or more anomalous images of the portion of the digital twin of the environment based on the field of view and the one or more anomalous characteristics, performing a tessellation routine and a texture mapping routine on the one or more nominal images and the one or more anomalous images to generate a plurality of synthetic images, and labeling, for each synthetic image from among the plurality of synthetic images, the synthetic image as one of an anomalous type, a nominal type, or a combination thereof.

Abstract: A flexible inspection system includes a robot with a plurality of scanners and a robot controller. The robot controller is configured to receive a vehicle inspection protocol (VIP) for a vehicle being assembled on an assembly line. The VIP includes checkpoints to be scanned on the vehicle and the checkpoints correspond to components installed on the vehicle and connections between components installed on the vehicle. The robot controller commands the robot to move the plurality of scanners per the VIP such that the checkpoints are scanned. A characteristic of each checkpoint is recorded and compared to a reference characteristic such that a pass or no-pass determination of each checkpoint is provided. A vehicle inspection report with the pass/no-pass determinations is provided to an operator such that operator inspections and/or repairs of the checkpoints are made.

Abstract: A system for assembling a vehicle platform includes a robotic assembly system having at least two robotic arms, a vision system capturing images of an assembly frame, and a control system configured to control the robotic assembly system to assemble the vehicle platform based on images from the vision system, force feedback from the at least two robotic arms, and a component location model. The control system is further configured to identify assembly features of a first component and a second component of the vehicle platform from the images, operate the robotic arms to orient the first component and the second component to respective nominal positions based on the images and the component location model, and operate the robotic arms to assemble the first component to the second component based on the force feedback.

Abstract: A system for assembling a vehicle platform includes a robotic assembly system having at least two robotic arms, a vision system capturing images of an assembly frame, and a control system configured to control the robotic assembly system to assemble the vehicle platform based on images from the vision system, force feedback from the at least two robotic arms, and a component location model. The control system is further configured to identify assembly features of a first component and a second component of the vehicle platform from the images, operate the robotic arms to orient the first component and the second component to respective nominal positions based on the images and the component location model, and operate the robotic arms to assemble the first component to the second component based on the force feedback.

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: 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: A method of inspecting an electric motor includes scanning an electric motor stator winding with a 2D or 3D camera, acquiring one or more images of a plurality welds between adjacent electrical wires forming the stator winding using the 2D camera, analyzing the one or more acquired images with at least one neural network such that the neural network determines if at least one of the plurality of welds has a weld defect. The at least one neural network is trained and distinguishes between surface discoloration on a surface of the welds and defect discoloration resulting from contamination during welding. Also, the method inspects over 150 welds per electric motor stator winding moving along an assembly line.

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 flexible inspection system includes a robot with a plurality of scanners and a robot controller. The robot controller is configured to receive a vehicle inspection protocol (VIP) for a vehicle being assembled on an assembly line. The VIP includes checkpoints to be scanned on the vehicle and the checkpoints correspond to components installed on the vehicle and connections between components installed on the vehicle. The robot controller commands the robot to move the plurality of scanners per the VIP such that the checkpoints are scanned. A characteristic of each checkpoint is recorded and compared to a reference characteristic such that a pass or no-pass determination of each checkpoint is provided. A vehicle inspection report with the pass/no-pass determinations is provided to an operator such that operator inspections and/or repairs of the checkpoints are made.

Abstract: The invention relates to a method for producing and checking a thread arranged in a cylinder head to receive a spark plug. However, the invention also relates to a device for checking the thread introduced into the cylinder head. By defining a starting point of the internal thread to be produced specifically in terms of position thereof within the parent hole by using spark plug parameters, the spark plug will always be aligned with its spark gap oriented toward the injector. For the purpose of checking, use is made of a plug gage, the screw-in thread of which has the parameters of the spark plug thread and with which, by using a measuring face, the position of an extension on the threaded body can be established. The extension simulates the ground electrode of the spark plug.

Abstract: The invention relates to a method for producing and checking a thread arranged in a cylinder head to receive a spark plug. However, the invention also relates to a device for checking the thread introduced into the cylinder head. By defining a starting point of the internal thread to be produced specifically in terms of position thereof within the parent hole by using spark plug parameters, the spark plug will always be aligned with its spark gap oriented toward the injector. For the purpose of checking, use is made of a plug gage, the screw-in thread of which has the parameters of the spark plug thread and with which, by using a measuring face, the position of an extension on the threaded body can be established. The extension simulates the ground electrode of the spark plug.