Patent classifications
G01M11/0278
Swappable optics module for additive fabrication devices
According to some aspects, calibration techniques are provided that allow an optics module of an additive fabrication device to be installed and operated in a stereolithography device by a user. In particular, the calibration techniques enable the optics module to be calibrated in a way that only depends on the characteristics of the optics module, and not upon any other components of the stereolithography device. As a result, the techniques enable a user of a stereolithography device to remove one optics module and replace it with another, without it being necessary to repair or replace the whole device. In some cases, the calibration techniques may include directing light onto one or more fiducial targets within the stereolithography device and measuring light scattered from said targets.
Method and system of measuring toric lens axis angle
A method of measuring an axis angle of a toric contact lens including a posterior toric central zone having a cylindrical axis, and an anterior lens surface forming a ballast that has an axis of orientation offset from the cylindrical axis at a selected rotational angle is disclosed. The method involves (a) providing anterior and posterior mold sections including respective anterior and posterior mold cavity defining surfaces, wherein the posterior mold cavity defining surface includes a toric central zone and the anterior mold cavity defining surface is shaped to provide the ballast, the mold sections being alignable at multiple rotational positions; (b) providing a detectable feature on each of the anterior and posterior mold sections at a predetermined angular location with respect to the tonic and ballast axes thereof, respectively; (c) rotating the detectable feature of the posterior mold section relative to the detectable feature of the anterior mold section, wherein the detectable feature of the anterior mold section is a zero reference; and (d) measuring the axis angle between the detectable feature of the posterior mold section relative to the detectable feature of the anterior mold section after rotational displacement of the mold sections during toric contact lens formation.
Method for detecting a defect in a zone of interest of an optical lens
Method for detecting a defect in a zone of interest of an optical lens, the method including: an image reception step, during which a plurality of images is received, each image includes a view of the zone of interest in front of a plurality of specific patterns, each specific pattern including a bright area and a dark area, and at least one image received is saturated in light intensity; a sampling step, during which each image of the plurality of images are sampled based on a common sampling pattern; a recombination step, during which a recombined image of the zone of interest is determined based on the common sampling pattern; and a defect detection step, during which a defect is detected in the zone of interest of the optical lens based on an analysis of the recombined image.
Apparatus and System for Visual Inspection of Fiber Ends and Image Analysis Tool for Detecting Contamination
A visual inspection device and apparatus is disclosed for inspecting fiber ends of a connector by capturing an image of the connector end face, and implementing an image analysis tool for detecting contamination from the captured image. The visual inspection tool includes components for providing a larger field of view to capture the entire connector end face in a single image, and the image analysis tool is able to accurately and efficiently detect contamination from the captured image.
METHOD FOR MEASURING THE OPTICAL QUALITY OF A GIVEN REGION OF A GLAZING UNIT, ASSOCIATED MEASURING DEVICE
A method for measuring the optical quality of a given region of a glazing of a road or rail vehicle, the region being intended to be positioned in the optical path of an image-acquiring device, the measuring method being implemented by a measuring device including an emitter and a wavefront analyzer, the measuring method including emitting, with the emitter, a beam of light rays in the direction of the given region, analyzing, with the wavefront analyzer, the wavefront of the light rays transmitted by the given region, including generating a wavefront-error map, and determining, on the basis of the wavefront-error map, at least one optical-defect map, of any optical defects present in the region of the glazing.
METHODOLOGY AND APPLICATION OF ACOUSTIC DETECTION OF OPTICAL INTEGRITY
Acoustic optical integrity detection system architectures and methods can be used to detect optical integrity of an optical component by detecting a discontinuity on and/or in the optical component (e.g., on the optical surface and/or within the bulk of the optical component). In some examples, integrity detection can be used to ensure safety compliance of an optical system, optionally including a laser. Acoustic integrity detection can utilize transducers (e.g., piezoelectric transducers) to transmit ultrasonic waves along an optical surface and/or through the thickness of an optical component. A discontinuity of the optical surface can interact with the transmitted wave causing attenuation, redirection and/or reflection of at least a portion of the transmitted wave. Portions of the transmitted wave energy after interaction with the discontinuity can be measured to determine discontinuity location, type, and/or severity.
Lens inspection module
A lens inspection module comprises: a lens insertion station, at least one lens inspection station, and a lens removal station as well as a closed-loop transportation rail, a cuvette transportation shuttle with a plurality of inspection cuvettes, and a self-driving cleaning shuttle for cleaning the rail. Cleaning shuttle comprises a driving unit a cleaning head, a suction unit, and a tube connecting cleaning head and suction unit. Cleaning head is spaced apart from suction unit and driving unit in the transportation direction and is pivotally arranged about a pivot axis. Cleaning head may comprise a distance sensor for detecting the distance between cleaning shuttle and transportation shuttle. Driving unit is configured to change the speed of the self-driving cleaning shuttle when the distance between the cleaning shuttle and the transportation shuttle is shorter than a predetermined threshold distance.
Eye Glasses Lens Inspection Device with Interchangeable Lenses
The disclosure relates to multi-purpose eyeglass lens inspection devices, and more particularly to such a device having a lighted base with a viewing lens assembly, an inspection lens assembly, and a plurality of interchangeable lenses with integral storage for the interchangeable lenses.
EYEGLASSES LENS MEASUREMENT DEVICE AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM
An eyeglasses lens measurement device measures an eyeglasses lens of eyeglasses. The eyeglasses lens measurement device includes a light source that emits a measurement light flux toward the eyeglasses lens, a transmissive display that transmits the measurement light flux from the light source and displays an index pattern formed by arranging a plurality of indexes, a detector that detects the measurement light flux passing through the eyeglasses lens and the transmissive display, and a controller. The controller is configured to control a display of the index pattern, acquire an optical characteristic of the eyeglasses lens, based on a detection result of the detector, and acquire lens information different from the optical characteristic of the eyeglasses lens, based on a detection result of the detector.
SYSTEMS AND METHODS FOR ACQUIRING AND INSPECTING LENS IMAGES OF OPHTHALMIC LENSES
Systems and methods for acquiring and inspecting lens images of ophthalmic lenses using one or more cameras to acquire the images of the lenses in a dry state or a wet state. The images are preprocessed and then inputted into an artificial intelligence network, such as a convolutional neural network (CNN), to analyze and characterize for type of lens defects. The artificial intelligence network identifies defect regions on the images and output defect categories or classifications for each of the images based in part on the defect regions.