Similar SKU Treads were getting mislabeled before shipment. Some SKUs have very similar tread patterns but vary in width by only 1/2 inch. With the product being black rubber, imaging the product can be quite difficult, and with 100s of different SKUs, pretraining all the SKUs is not feasible.
An auto-guide interacts with the PLC to get the current SKU and opens/closes it to the appropriate width, allowing the tread to be centered over a combination of 3D laser displacement cameras. The tread is imaged as it passes over an encoder / grooved conveyor rollers. The image is converted into a 2D image and contrast-enhanced.
A Setup Wizard allowed the local QA staff to easily train a new SKU in less than 10 minutes. Vision Scripts are stored on a central repository, allowing all three finishing lines to use the same files.
Track and Trace dashboard allows QA to review inspection results and replay images in case of a future customer complaint.
Before applying cement, the smooth side of the tread surface must be inspected to ensure the quality adhesion of the tread to a tire carcass. Although a human inspector can easily find defects like blisters and cracks, small defects can be a challenge, and other surface defect anomalies may require shifting positions to find variation in surface sheen.
Our team shadowed human inspectors while collecting images of the product, annotated images, and then an image-based deep learning model was developed to detect surface anomalies. Through further data collection, annotation, and model iterations, the system gained the ability to classify the surface anomalies into several categories. Traditional vision (rule-based) algorithms were added to established pass/fail criteria based on shape, size, and brightness for each classification, thus reducing the need for 100% human inspection.
The inside of a tire is manually inspected for surface defects and penetration by foreign objects. It can be physically awkward and challenging to see small objects manually, so the vision system handles that task.
For this pilot project, a camera on a linear slide and rotating mirror constrained to a 12" x 3" package were mounted on the end of the linear arm and joysticked into the center of the tire. Laser gauges provide feedback to the operator for their ideal placement. Interior is laser profiled to calculate optimal camera distance such that the entire field of view is within focus. The mirror is centered to the field of view, and the tire is rotated while the camera acquires a series of images. After each series of images, the mirror is adjusted to the next field of view, and the process is repeated until the interior is fully imaged. Images are then presented to the operator on a 34" curve monitor for manual inspection. The operator reviews the images and draws boxes around possible defects. Afterward, this information is archived with an identifier. At a later date, this data can be used to train a Deep Learning system.
A scheduling system dictates the components to be assembled on a diesel engine head. This information is written to an RFID tag that is attached to a carriage. The client needed a way to validate the correct engine head was loaded onto the carriage and get its information (production date, parent, and serial number) married to the RFID tag. (7) different head types will need to be handled by this system.
Multiple Cognex Dataman cameras are oriented in a work cell. As the carriage enters the work cell and stops, an RFID reader gets the part type and passes that to the HMI software, which triggers 1 or 2 cameras. The cameras extract the date, parent, and serial number for a 2D matrix dot pin barcode, and the HMI software writes the data to the RFID tag and an edge database. If the parent number is not valid for the current part type, the error is flagged for an operator to review. In the event of a poor barcode, the operator can enter the information manually.
Car interiors are often customized for their material, trim, and user features. Variations may be subtle and batch runs are small. An end-of-line inspection system is needed to ensure the product that was assembled matches the build ticket.
A camera with a bright field and dark field configuration was mounted to the end of an ABB robot. The operator loads an assembled car door interior onto an open nest and presses the Inspect bottom. The robot would move to 20 different locations on the backside of the car door interior, triggering the camera/lights, and then requested the operator to rotate the fixture. The robot then moved to 6 different locations, triggering the camera/lights for each.
A mock display is presented to the Operator for pass/fail of all zones. The operator had the ability to select a zone to see the image taken and individual pass/fail results for each Poka-Yoke item.
A robotic system applies Loctite to various holes on an engine block. Insufficient application of Loctite can create quality issues.
An additive in the Loctite causes it to fluoresce if illuminated with UV lighting. Cognex InSight camera with a Blue bandpass filter image the Loctite application for presence and bead width and continuity.
Prior to loading the tire for shipment, the highpoint is located and the sidewall of the tire is marked with a red dot, white dot, white P, white N or a sticker. Storage and material handling of the tire can cause the highpoint mark to get rubbed off or faded.
Cognex VisionPro was used to identify and verify the presence/absence of the various types of highpoint marks based on the product SKU.
Tires of similar sizes can be produced with different tread patterns. A color band is applied on the tread surface based on the product SKU to ensure the wrong product is not mixed in with the shipment.
Cognex VisionPro was used to blend the color band together (because of the gap between tread lugs) and a color match is performed to verify it matches with the current SKU.
A fuse block assembly for a car or truck has similar frames, but what goes into them can vary. A component can easily be missed, such as a fuse or diode or the wrong relay model installed.
A GUI was developed to allow an Engineer to train a new component (color, pattern matching, OCR) and add it to a component family. Once populated with components, an Engineer can create a product SKU and create a Region of Interests (ROIs) for each component location on the fuse block. The engineer can choose the allowed orientation of the component for the ROI.
With a fully configured product, an Operator can recall the SKU from a part list and batch inspect multiple fuse blocks. Any component that is missing, in the wrong orientation, pattern score is too low, or OCR is mismatched, then the ROI is highlighted in red. Components that pass are highlighted in green.
Key blanks for major retailers are plated, stamped and channels cut. Non-conformances can include poor plating, poor stamping, misaligned stamping, and misaligned channels.
The system was trained with the average composite image of 10 or more keys. Cognex PatInspect and additional image processing were used to determine the difference image, then blob analysis determined pass/fail result.
Dashboard displays manufactured by automotive suppliers are hand assembled with different Legend Tags (door open, oil temp, washer fluid low, etc.). Before the product is packaged and shipped for assembly into a vehicle, 100% inspection is required to prevent quality chargebacks.
PC-based vision system reads the 2D barcode on the cluster assembly and queries a database. The database provides information on what Legend tags are to be installed in each of the 10 locations. Using pre-trained patterns, the system verifies all 10 locations on the cluster and prompts the Operator of any mismatches.
Dashboard displays manufactured by automotive suppliers must have all the gauges (speedometer, tachometer, engine temp, etc.) calibrated before shipping.
The operator places the product into a test docking station, and the gauge is provided a signal for 3 different values. The PC-based vision system determines the angle of the needle on the gauge and provides the value back to the test docking station. The 3 data points are used to calibrate the gauge, and values are saved to the EPROM of the dashboard display.
An Automotive manufacturer needed to verify the correct roof has been placed on the vehicle prior to robotic welding.
PC-based Vision system identified key features of 6 different possible roof configurations and verified a match based on the VIN barcode.
An Automotive manufacturer needed to verify the correct fender has been placed on the vehicle prior to robotic welding.
PC-based Vision system identified key features of 4 different possible fender configurations and verified a match based on the VIN barcode.
Tier 1 automotive supplier manufactures car frames. Mounts that are bent or incorrectly mounted on the frame need to be detected before additional value is added to the product.
PC-Based vision system located and profiled the mount that was welded to the frame. An alert was raised if the location or deformation was outside of tolerance by more than 1/4 inch.
Molded fenders made from thermoplastic olefins (TPO) require an adhesion promoter (Adpro) prior to the application of automotive paint.
A PC-Based vision system projected a laser line onto the TPO fender. A raw fender would create a diffraction pattern when reflecting the laser line, whereas the application of Adpro creates a diffuse reflection. Image processing analysis detected the signature difference between the two, and the system would stop the line if a non-conformance were detected.
Fenders are welded to a truck bed by welding robots. Misaligned fenders need to be detected and corrected before the truck bed proceeds to the next step in the process.
The PC-Based vision system used structured light to detect and measure fender location on the truck bed. Rejects were flagged, and an alert was raised to the Operator.
Metal truck panels after forming enter into a rust inhibitor dip tank. Panels that are not properly secured can "float" off the carriage into the dip tank. Subsequence carriage entering into the dip tank can have their panels damaged.
A PC-based vision system was used to verify all the locking pins holding panels to the carriage were in the correct position. If an improper position locking pin was detected, the line was stopped, and an alarm was raised.