An automatic determination process device 1 comprises image acquisition units 14, 147, 247, an automatic determination unit 15, and determination result output units 14, 148, 248. An error detection verification device 2 displays a defect candidate image on a display device. The error detection verification device 2 acquires, via an input device, secondary inspection determination result information about the defect candidate image. The image acquisition units 14, 147, 247 read the defect candidate image displayed by the error detection verification device 2 from a medium for displaying the same on the display device. The automatic determination unit 15 executes a determination process on the basis of the content of the defect candidate image read by the image acquisition units 14, 147, 247 and outputs a determination result. The determination result output units 14, 148, 248 generate a signal in accordance with the determination result. Said signal is equivalent to pressing, via the input device connected to the error detection verification device 2, either a “defect” button or a “good” button included on a screen displayed by the error detection verification device 2.

Provided is a substrate working machine including a holding device configured to hold a substrate in which multiple through-holes are formed, an inserting device configured to insert multiple terminals of a component into the multiple through-holes of the substrate held by the holding device, and an imaging device configured to simultaneously image a shape of a pair of pins and a pair of through-holes of the multiple through-holes, in which positions of the pair of through-holes and the shape of the pair of pins are calculated based on image data captured by the imaging device.

A processing method includes obtaining a processing image of a apparatus and performing a second processing on the processing image to generate a target image to analyze the target image according to a target defect detection method to realize defect detection of the apparatus. The processing image is obtained by performing a first processing on an initial image of the apparatus. The first processing includes performing scale processing on the initial image according to defect parameters corresponding to the initial image.

An automatic model reconstruction method and an automatic model reconstruction system for a component recognition model are provided. The automatic model reconstruction method includes the following steps. A first component image of a plurality of circuit boards is sequentially captured at a first position. The component recognition model sequentially recognizes component categories of the first component images, and a number of recognition probability values are output. According to the recognition probability values, a number of exponentially weighted moving averages (EWMA) are obtained. The first component images corresponding to the exponentially weighted moving averages lower than a first set value are collected until one of the exponentially weighted moving averages is lower than or equal to a second set value. The collected first component images are regarded as abnormal component images. The component recognition model is reconstructed according to the abnormal component images.

An apparatus for inspecting components may include a processor for: determining exterior information of a first component mounted on a first printed circuit board; inspecting a mounting state of the first component by using inspection information of a second component having a first similarity value, when the first similarity value is higher than or equal to a preset reference similarity value, and updating the inspection information of the plurality of components by using the inspection information of the first component matched with the inspection information of the second component, when it is determined that the mounting state of the first component is good.

A method is disclosed for inspecting electrical components within a housing. The method may be useful for identifying dust, grime, corrosion, tree-like structures, edges and/or slots on electrical components. The method includes capturing two optical images of an electrical component and comparing the images. Variations in pixels between the two images may be used to generate warnings.

A printed wiring board includes a resin insulating layer including resin and particles, and a conductor layer formed on a surface of the resin insulating layer. The particles in the resin insulating layer include first particles and second particles such that the first particles are partially embedded in the resin and the second particles are completely embedded in the resin, and the resin insulating layer is formed such that the first particles has exposed surfaces exposed from the resin and covered surfaces covered by the resin, respectively, the surface of the resin insulating layer includes the first exposed surfaces, and a ratio of a second area to a first area is in a range of 0.1 to 0.25 where the first area is an area of the surface of the resin insulating layer, and the second area is obtained by summing areas of the exposed surfaces of the first particles.

One variation of a method for monitoring manufacture of assembly units includes: receiving selection of a target location hypothesized by a user to contain an origin of a defect in assembly units of an assembly type; accessing a feature map linking non-visual manufacturing features to physical locations within the assembly type; for each assembly unit, accessing an inspection image of the assembly unit recorded by an optical inspection station during production of the assembly unit, projecting the target location onto the inspection image, detecting visual features proximal the target location within the inspection image, and aggregating non-visual manufacturing features associated with locations proximal the target location and representing manufacturing inputs into the assembly unit based on the feature map; and calculating correlations between visual and non-visual manufacturing features associated with locations proximal the target location and the defect for the set of assembly units.

An optical monitoring device includes a housing including a plurality of walls and an interior space disposed therebetween, an opening disposed in a wall of the plurality of walls, and a movable panel, the opening forms a window through the wall, and the movable panel is configured to be moved to access the interior space; a lamp assembly disposed within the interior space and positioned to project light at the window; a camera including an image receiving lens, the camera positioned within the interior space with the image receiving lens is in optical communication with the window; a memory storage device for storing image data; and a controller in electrical communication with the camera and the memory storage device, wherein the controller is configured to compare instant image data from the camera to image data stored on the memory storage device.

A system and a method for assessing reliability of an electronic component. The method may include training a machine earning (ML) algorithm and/or a classification network to classify electronic components based on one or more features, attributes or characteristics related to reliability of the electronic components, e.g., related to a level of solderability of the components lead or balls or features indicating of tampering of the electronic component. By receiving an image of a test electronic component and extracting a feature related to reliability of the test electronic component from the image received, embodiments of the invention may enable classifying the test electronic component to a class indicating a reliability of the test electronic component by using the machine learning algorithm and/or the classification network.