A method for adaptable machining includes (a) capturing one or more images, with a digital imaging system, of each of a series of work pieces that may or may not be of common design geometry, (b) for each of the work pieces, selectively modifying a preprogrammed cutting tool path with regard to the image of the respective work piece, and (c) for each of the work pieces, performing a machining operation according to the respective selectively modified preprogrammed cutting tool path.

In a teaching method for a transfer mechanism that transfers a substrate to a mounting table, the method includes: transferring an inspection substrate having a plurality of imaging devices on an outer peripheral edge thereof to a transfer position where the substrate is transferred between the transfer mechanism and the mounting table; imaging a part of the mounting table which includes an outer periphery of the mounting table at the transfer position by the imaging devices; calculating a central position of the mounting table based on the image obtained by the imaging devices; and correcting the transfer position based on the central position of the mounting table which is calculated in the calculating and a central position of the inspection substrate at the transfer position.

The disclosure relates to a path correction method for correcting paint tracks when coating a component (e.g., motor vehicle body component) with a coating agent (e.g., paint). The path correction method includes: Defining a reference path, applying a first paint track of the coating agent to the component, where the first paint track and the reference path run adjacent to one another and ideally adjoin one another without a path error at a seam, determining the interfering path error at the seam between the first paint track and the adjacent reference path, and determining path correction values for correcting the course of the first paint track in a subsequent coating operation, the path correction values being determined as a function of the path error.

Furthermore, the disclosure comprises a corresponding coating method and a correspondingly adapted coating system.

A target path is corrected using an imaging result of a workpiece and a moving distance of a control object for each control cycle is kept constant. The PLC generates a connected path for each control cycle so that a length of the corrected path for each control cycle substantially matches a length of a normative path for each control cycle.

In one aspect, the present invention relates to a contour checking device for calculating path deviations from a target path of a cutting head of a laser machine tool. The contour checking device comprises a reference texture interface for reading a reference texture along the target path, which is defined in particular for cutting a contour, a controller, which is intended for controlling the laser machine tool in such a way that the cutting head traverses the target path; at least one camera, wherein the controller for controlling the at least one camera is intended for continuously capturing overlapping frames of the reference texture along the traversed path; a processor, which is intended for executing an image processing algorithm for reconstructing the trajectory traversed by the cutting head from the captured overlapping frames of the reference texture; and wherein the processor is intended for calculating deviations between the reconstruction of the path traversed by the cutting head and the target path. The contour checking device also comprises an output interface, which is intended for outputting the calculated deviations.

A method for object distance detection and focal positioning in relation thereto. The method comprising the steps of: (a) identifying (via a computing device) a desired distance among a plurality of designated sites on an object; (b) adjusting a focus (via an autofocus device) onto the plurality of designated sites; (c) calculating (via an image recognition module) the actual distance among the plurality of designated sites; (d) determining (via the image recognition module) if error exist between the actual distance and the desired distance; and (e) wherein (in no particular order) repeating the steps of (b), (c), and (d) until no substantial error exists between the actual distance and the desired distance.

A positioning unit that performs positioning with respect to a predetermined sample, an imaging unit that obtains an image of the sample, and a processing unit, in which the imaging unit is moved by the positioning unit, and obtains a first image and a second image which is obtained later than the first image, and in which the processing unit obtains a first position shift trend from the first image and the second image, and determines whether or not the positioning is abnormal from a change of the first position shift trend.

The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.