Abstract:
A wire processing system includes a tray having at least one tray surface configured to sequentially receive a first wire and a second wire from a wire feed system of a wire processing machine. The tray surface has a surface feature configured to provide a wire-to-surface coefficient of friction between the tray surface and the first wire higher than a wire-to-wire coefficient of friction between the first wire and the second wire laying on top of the first wire. The wire-to-surface coefficient of friction reduces movement of at least a portion of the first wire relative to the tray surface during movement of the second wire relative to the first wire.
Abstract:
A method 500 of operating an automated machine 100 is provided for inserting wires into grommet cavity locations 110 of an electrical connector 112 to compensate for manufacturing tolerances associated with the electrical connector. The method comprises inserting wires into grommet cavity locations of the electrical connector based upon a plug map 300 having offset values to compensate for manufacturing tolerances associated with the electrical connector. The method may further comprise selecting from a plurality of pre-generated plug maps having offset values the closest matching pre-generated plug map for the electrical connector based upon offset values associated with each of the plurality of pre-generated plugs maps. The selected pre-generated plug map having offset values corresponds to the plug map used to insert wires into grommet cavity locations of the electrical connector.
Abstract:
A system (700) for additively manufacturing a composite part (102) comprises a delivery guide (112), movable relative to a surface (114). The delivery guide (112) is configured to deposit at least a segment (120) of a continuous flexible line (106) along a print path (122). The continuous flexible line (106) comprises a non-resin component (108) and a thermosetting-resin component (110). The thermosetting-resin component (110) comprises a first part (253) and a second part (255). The non-resin component (108) comprises a first element (271) and a second element (273). The system (700) further comprises a first resin-part applicator (236), configured to apply the first part (253) to the first element (271), and a second resin-part applicator (237), configured to apply the second part (255) to the second element (273). The system (700) also comprises a feed mechanism (104), configured to pull the first element (271) through the first resin-part applicator (236), to pull the second element (273) through the second resin-part applicator (237), and to push the continuous flexible line (106) out of the delivery guide (112).
Abstract:
A method (300) of additively manufacturing a composite part (102) is disclosed. The method (300) comprises depositing a segment (120) of a continuous flexible line (106) along a print path (122). The continuous flexible line (106) comprises a non-resin component (108) and a thermosetting resin component (110) that is not fully cured. The method (300) further comprises, while advancing the continuous flexible line (106) toward the print path (122), delivering a predetermined or actively determined amount of curing energy (118) at least to a portion (124) of the segment (120) of the continuous flexible line (106) at a controlled rate after the segment (120) of the continuous flexible line (106) is deposited along the print path (122) to at least partially cure at least the portion (124) of the segment (120) of the continuous flexible line (106).
Abstract:
A system (100) for additively manufacturing a composite part (102) is disclosed. The system (100) comprises a delivery guide (112), movable relative to a surface (114). The delivery guide (112) is configured to deposit at least a segment (120) of a continuous flexible line (106) along a print path (122). The continuous flexible line (106) comprises a non-resin component (108) and a thermosetting resin component (110) that comprises a first part (253) and a second part (255) of a thermosetting resin (252). The print path (122) is stationary relative to the surface (114). The delivery guide (112) comprises a first inlet (170) configured to receive the non-resin component (108), and a second inlet (250) configured to receive at least the first part (253) of the thermosetting resin (252). The delivery guide (112) is further configured to apply the first part (253) and the second part (255) of the thermosetting resin (252) to the non-resin component (108). The system 100 further comprises a feed mechanism (104), configured to push the continuous flexible line (106) out of the delivery guide (112).
Abstract:
A system (100) for additively manufacturing a composite part (102) is disclosed. The system (100) comprises a delivery guide (112), movable relative to a surface (114). The delivery guide (112) is configured to deposit at least a segment (120) of a continuous flexible line (106) along a print path (122). The print path (122) is stationary relative to the surface (114). The continuous flexible line (106) comprises a non-resin component (108) and a thermosetting-epoxy-resin component (110) that is partially cured. The system (100) also comprises a feed mechanism (104), configured to push the continuous flexible line (106) through the delivery guide (112). The system (100) further comprises a cooling system (234), configured to maintain the thermosetting-epoxy-resin component (110) of the continuous flexible line (106) below a threshold temperature prior to depositing the segment (120) of the continuous flexible (106) along the print path (122) via the delivery guide (112).
Abstract:
An airport object location system comprising a number of vehicle location units, a sensor system, and a model generator. The number of vehicle location units is connected to a number of vehicles. The number of vehicle location units generate vehicle location information for the number of vehicles in an area including an operations surface at an airport and vehicle timestamps for the vehicle location information. The sensor system is connected to a reference vehicle. The sensor system is configured to generate sensor data for the area, wherein reference timestamps and reference location information are associated with the sensor data. The model generator is configured to correlate the vehicle location information for the vehicles with the sensor data using the vehicle timestamps, the reference location information, and the reference timestamps to form a dataset.
Abstract:
In an example, a method is described. The method includes causing one or more sensors arranged on an aircraft to acquire, over a window of time, first data associated with a first object that is within an environment of the aircraft, where the one or more sensors include one or more of a light detection and ranging (LIDAR) sensor, a radar sensor, or a camera, causing an array of microphones arranged on the aircraft to acquire, over approximately the same window of time as the first data is acquired, first acoustic data associated with the first object, and training a machine learning model by using the first acoustic data as an input value to the machine learning model and by using an azimuth, a range, an elevation, and a type of the first object identified from the first data as ground truth output labels for the machine learning model.
Abstract:
A method of supporting an aircraft approaching a runway is provided. Example implementations involve receiving a sequence of images, captured by a camera onboard the aircraft. Example implementations involve applying a received image to a machine learning model trained to detect the runway or a runway marking in the image, and to produce a mask that includes a segment of pixels of the image assigned to an object class for the runway or marking. Example implementations may also involve applying the mask to a corner detector to detect interest points on the mask and match the interest points to corresponding points on the runway or the marking that have known runway-framed local coordinates. Example implementations may also involve performing a perspective-n-point estimation to determine a current pose estimate of the aircraft relative to the runway or the marking and outputting the current pose estimate for use during final approach.
Abstract:
Solutions are provided for auto-labeling sensor data for machine learning (ML). An example includes: determining a platform's own position; recording, from a sensor aboard the platform, sensor data comprising a sensor image; receiving position data for at least one intruder object (e.g., a nearby airborne object); based at least on the position data for the intruder object and the platform's position, determining a relative position and a relative velocity of the intruder object; based at least on the relative position and a relative velocity of the intruder object and a field of view of the sensor, determining an expected position of the intruder object in the sensor image; labeling the sensor image, wherein the labeling comprises annotating the sensor image with a region of interest and an object identification; and training an artificial intelligence (AI) model using the labeled sensor image.