An inspection device includes a scanning device, a first recognition unit, and a second recognition unit. The scanning device includes a laser sensor that emits laser slit light for measuring a two-dimensional shape, and a movement mechanism that moves the laser sensor in a predetermined direction. The first recognition unit obtains three-dimensional shape data of an inspection object and a workpiece by associating a plurality of two-dimensional shape data obtained by the laser sensor with position data of the laser sensor at the time of measuring the two-dimensional shape. The second recognition unit derives a three-dimensional shape of the workpiece by obtaining a difference between first three-dimensional shape data indicating a three-dimensional shape before the workpiece is laminated on the mold and second three-dimensional shape data indicating a three-dimensional shape after the workpiece is laminated on the mold.

The present disclosure relates to a device for observing and/or inspecting a material web, comprising a first camera unit for capturing first image data of the material web; a first computing unit, connected to the first camera unit, adapted to receive the first image data; and a second computing unit adapted to receive the first image data from the first computing unit. The first and the second computing units are connected via a first USB line. The first image data is transferred to the second computing unit via the first USB line. A first USB interface of the first computing unit and a second USB interface of the second computing unit are connected via the first USB line. Another aspect of the present disclosure relates to a device for processing a material web, comprising the material web and the device for observing and/or inspecting the material web.

An apparatus divides a photographed image of a sheet-like inspection object into blocks each of which has a size of a predetermined number of pixels by a predetermined number of pixels, calculates a longitudinal variance based on pixel values in a longitudinal direction in each block and a lateral variance based on pixel values in a lateral direction in the block, determines whether the block is a defect candidate using the longitudinal variance and the lateral variance as sheet-like inspection object defect determination evaluation values, and determines, based on one of a length and an area of the blocks determined as the defect candidates, whether the sheet-like inspection object has defect.

A system includes at least one common support configured to span at least a width of a web of material in a manufacturing or processing system. The system also includes multiple sensor heads each configured to move independently along at least part of the at least one common support. The sensor heads can be configured to move simultaneously along the at least one common support, and at least one controller can be configured to control movement of the sensor heads so that the sensor heads do not contact one another. Each sensor head could include one or more sensors configured to measure one or more characteristics of the web. Different sensor heads could include different types of sensors. The sensor heads can be configured to move in non-overlapping patterns over or under the web. Different sensor heads can be configured to move at different speeds.

A smart synchronizing method of a web inspection system for monitoring a moving web. A synchronizing device, at least one slave camera, and at least one lighting device illuminate an area of the web to be imaged by the cameras. The method includes transmitting a synchronizing signal to the slave camera. The synchronizing signal includes at least a start pulse and serial data including additional information. All the cameras are synchronized with each other based on synchronization moment when integration of cameras end. A light synchronizing signal is transmitted to the at least one lighting device indicating a switching on and off times of the at least one lighting device, which switching off time corresponds to the synchronization moment, and calculating a starting time of integration based on an individual integration time of a camera and the synchronization moment common for cameras of the web inspection system.

A method for recognizing defects on a homogeneous surface of an object during a movement of the object is provided, wherein an image of the homogeneous surface is recorded and the image is evaluated as to whether it has defects. In this respect, the image is recorded by an event-based image sensor that detects events of a changing intensity with a plurality of pixel elements; the recording takes place over a time interval within which the homogeneous surface moves on over at least some pixel elements of the image sensor; the events are corrected in accordance with a time that has elapsed since a reference point in time and the movement that has taken place during the elapsed time; and the corrected events are collected in an image that is then evaluated with respect to the defects.

A metallic particle detector system includes a particle detection unit with a detector configured to detect, and provide signals as function of, light reflected from a surface of an active material layer on a charge collector backing layer moving on a roll-to-roll coated electrode manufacturing line. The particle detection unit also includes a controller configured to receive the signals from the detector and determine, in-situ and as a function of the signals from the detector, a foreign metallic particle on the active material layer. The controller is also configured to determine a position of the foreign metallic particle on the charge collector backing layer moving on the roll-to-roll coated electrode manufacturing line.

An inspection system includes an inspection device having at least one image capture device. The image capture device captures image data of a sheet part passing through the inspection device. A processing unit of the inspection device provides the image data representative of the sheet part to a plurality of neural networks, where each of the neural networks is trained to identify a corresponding defect in the sheet part and output data indicative of the presence of the corresponding defect. The processing unit determines a quality category of the sheet part based on the data indicative of the presence of the corresponding defect output by each corresponding neural network. The processing unit can further output the quality category of the sheet part to a sorter that can sort the sheet part based on the quality category.

One form of a metallic particle detection system detects automatically, through analysis of image data from a first sensor, a foreign metallic particle in or on an active material layer of an electrode strip moving between a section and a subsequent section on a roll-to-roll coated electrode manufacturing line that manufactures a plate electrode. The system also determines a position of the foreign metallic particle on the electrode strip moving on the roll-to-roll coated electrode manufacturing line. The system also triggers, in response to detection of the foreign metallic particle and based on the position of the foreign metallic particle and a speed at which the electrode strip is moving, the second sensor, the second sensor generating a reflectance spectrum of the foreign metallic particle. The system also analyzes the reflectance spectrum to identify a type of metal of which the foreign metallic particle is composed.