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.

A training device and a training method for a neural network model. The training method includes: obtaining a data set; completing, according to the data set, a plurality of artificial intelligence (AI) model trainings to generate a plurality of models corresponding to the plurality of AI model trainings respectively; selecting, according to a first constraint, a first model set from the plurality of models; and selecting, according to a second constraint, the neural network model from the first model set.

In one embodiment, a computing system may access design data of a printed circuit board to be produced by a manufacturing process. The system may determine one or more corrections for the design data of the printed circuit board based on one or more correction rules for correcting one or more parameters associated with the printed circuit board. The system may automatically adjust one or more of the parameters associated with the design data of the printed circuit board based on the one or more corrections. The adjusted parameters may be associated with an impedance of the printed circuit board. The one or more corrections may cause the impendence of the printed circuit board to be independent from layer thickness variations of the printed circuit board to be produced by the manufacturing process.

The present invention relates to a method for inspecting a welding quality of an electrode tab-electrode lead welded portion of a pouch-type lithium secondary battery, and the method includes: recognizing welding traces of the welded portion by a vision inspection device; measuring a size of each of the recognized welding traces; and determining whether the welded portion has been weakly welded, excessively welded or normally welded by comparing the measured size with a reference value.

In one embodiment, an X-ray inspection system may nondestructively inspect a printed circuit board to measure a number of dimensions at a number of pre-determined locations of the printed circuit board. The X-ray inspection system may generate a data set for the printed circuit board based on the measured dimensions. The X-ray inspection system may calculate one or more drilling values based on the data set of the printed circuit board. The X-ray inspection system may provide, to a drilling machine, instructions for drilling a number of plated-through vias based on the calculated drilling values for the printed circuit board.

In various examples, a computer-implemented method of generating instructions for a welding robot. The computer-implemented method comprises identifying an expected position of a candidate seam on a part to be welded based on a Computer Aided Design (CAD) model of the part, scanning a workspace containing the part to produce a representation of the part, identifying the candidate seam on the part based on the representation of the part and the expected position of the candidate seam, determining an actual position of the candidate seam, and generating welding instructions for the welding robot based at least in part on the actual position of the candidate seam.

A weld quality inspection method, apparatus and system and an electronic device are disclosed. The weld quality inspection method provided by the embodiments of the disclosure includes: acquiring point cloud data of a target weldment, and converting the point cloud data into a height map; determining a weld region for characterizing a target weld from the height map; analyzing the weld region to obtain a feature parameter of the target weld; and obtaining a quality inspection result of the target weld according to the feature parameter, where the target weldment includes a base material, a welding part and the target weld formed by welding the welding part to the base material.

The present invention relates to a weld bead inspection device that inspects the shape of a weld bead, and provides a weld bead inspection device capable of inspecting the welding quality, welding state, and the like by imaging the shape of the weld bead in a pipe. For this purpose, the weld bead inspection device of the present invention includes an image sensor unit that images a shape of a weld bead in a downwardly open inner space formed on a middle inner side of a housing unit and an illumination unit that provides illumination light directly or indirectly to the weld bead. Accordingly, by moving and rotating the weld bead inspection device along an outer peripheral surface of a pipe to be inspected, it is possible to quickly and accurately inspect the weld bead by virtue of a simple inspection operation even in a complex facility structure.

In one embodiment, an automated high-speed X-ray inspection system may generate a first X-ray image of an inspected sample at a first direction substantially orthogonal to a plane of the inspected sample. The first X-ray image may be a high-resolution grayscale image. The system may identify one or more elements of interest of the inspected sample based on the first X-ray image. The first X-ray image may include interfering elements that interfere with the one or more elements of interest in the first X-ray image. The system may determine one or more first features associated with respective elements of interest based on variations of grayscale values in the first X-ray images. The system may determine whether one or more defects are associated with the respective elements of interest based on the one or more first features associated with the element of interest.

An auxiliary prediction system is provided to predict reliability of an object after a specific operation is applied to the target object. The auxiliary prediction system includes an image correction module and an analysis module. The image correction module performs an image correction procedure to convert an original image of the target object into a first correction image. The analysis module performs a feature analysis on the first correction image through an artificial intelligence model that has been trained, so as to predict whether the target object has a defect or not after the specific operation.