A device includes image generation circuitry and convolutional-neural-network circuitry. The image generation circuitry, in operation, generates a digital image representation of a wafer defect map (WDM). The convolutional-neural-network circuitry, in operation, generates a defect classification associated with the WDM based on: the digital image representation of the WDM and a data-driven model associating WDM images with classes of a defined set of classes of wafer defects and generated using a training data set augmented based on defect pattern orientation types associated with training images.

A method for improving machine performance based on machine application identification can include training an application identification model. The method can also include receiving one or more images from a camera positioned on a machine performing an application, and feeding each of the one or more images into the trained application identification model. The trained application identification model provides a predicted application corresponding to the application being performed by the machine. The model also provides a probability that the predicted application corresponds to the application being performed by the machine. Application optimization parameters, based on the predicted application, are retrieved and distributed to the machine when the probability is greater than a selected confidence threshold.

An arrangement detector for plate-shaped objects and a lord port including the same are provided. The arrangement detector for plate-shaped objects includes a judgement window setting means, a shape determination means for determining a shape matching rate by superimposing the judgement window on the image captured by the imaging means, and an object judgement means for judging the plate-shaped objects do not exist in the judgement window overlaid on the image captured by the imaging means, in case that the shape matching rate determined by the shape determination means is equal to or less than a predetermined value. A first reference line is a continuous straight line in the judgement window, and a second reference line is a collection of discontinuous line segments linearly arranged in the judgement window.

A method for detecting a defect includes: a measurement image including a wafer edge of a wafer to be detected is acquired; an image region to be detected is determined in the measurement image; feature extraction is performed on the image region to be detected to obtain a pixel distribution characteristic of the image region to be detected; and defect detection is performed on the wafer edge based on the pixel distribution characteristic of the image region to be detected.

The present invention provides a measurement program selection assisting apparatus capable of visually confirming whether a selected measurement program is suitable for an object to be measured. One aspect of the present invention is a measurement program selection assisting apparatus comprising: a measurement program database storing a measurement program related to measurement of an object and superimposed display information corresponding to a three-dimensional shape of the object in association with each other; a display unit capable of displaying information defined in a virtual space superimposed on the real space; and a display control unit for acquiring the superimposed display information corresponding to a selected measurement program from the measurement program database and displaying the acquired superimposed display information in a mixed reality on the display unit

An object of the present invention is to provide a tool system allowing a tool to be controlled on a work object basis before the work is started. A tool system includes a portable tool and an identification unit. The tool includes a driving unit to operate with power supplied from a battery pack. The identification unit identifies, by a contactless method, a current work object, to which the tool is set in place, out of a plurality of work objects.

An environment association system (“EAS”) comprising: a processor and a memory; an object recognition process configured to identify objects within images, the objects including one or more of a vehicle, a vehicle lift, a vehicle repair tool, and an alignment fixture; and an EAS interface configured to communicate with a user device, the user device comprising a camera and a display; wherein the processor is configured to: determine, for at least one object in the set of objects, create a virtual overlay for the image based on the position of the at least one object within the image and a virtual marking associated with the at least one object; and provide the virtual overlay to the user device, wherein the virtual overlay is configured to cause the user device to simultaneously display the image and the virtual overlay via the display. The system provides information about the alignment of lifting points of the object with lifting members.

Examples provide a system and method for autonomously placing items into non-rigid containers. An image analysis component analyzes image data generated by one or more cameras associated with picked items ready for bagging and/or a non-rigid container, such as, but not limited to, a bag. The image analysis component generates dynamic placement data identifying how much space is available inside the bag, bag tension, and/or contents of the bag. A dynamic placement component generates a per-item assigned placement for a selected item ready for bagging based on a per-bag placement sequence and the dynamic placement data. Instructions, including the per-item assigned placement designating a location within the interior of the non-rigid container to the selected item and an orientation for the selected item after bagging, is sent to at least one robotic device. The robotic device places the selected item into the non-rigid container in accordance with the instructions.

A system and method that automatically resolves conflicts among sensor information in a sensor fusion robot system. Such methods can accommodate converging ambiguous and divergent sensor information in a manner that can allow continued, and relatively accurate, robotic operations. The processes can include handling sensor conflict via sensor prioritization, including, but not limited, prioritization based on the particular stage or segment of the assembly operation when the conflict occurs, overriding sensor data that exceeds a threshold value, and/or prioritization based on evaluations of recent sensor performance, predictions, system configuration, and/or historical information. The processes can include responding to sensor conflicts through comparisons of the accuracy of workpiece location predictions from different sensors during different assembly stages in connection with arriving at a determination of which sensor(s) is providing accurate and reliable predictions.

A software compensated robotic system makes use of recurrent neural networks and image processing to control operation and/or movement of an end effector. Images are used to compensate for variations in the response of the robotic system to command signals. This compensation allows for the use of components having lower reproducibility, precision and/or accuracy that would otherwise be practical.