A substrate inspection apparatus generates, when anomalies of a plurality of second solder pastes among a plurality of first solder pastes printed on a first substrate is detected, at least one image indicating a plurality of second solder pastes with anomalies detected by using an image about a first substrate, applies the at least one image to a machine-learning-based model, acquires a plurality of first values indicating relevance of respective first fault types to the at least one image and a plurality of first images indicating regions associated with one of a plurality of first fault types, determines a plurality of second fault types, which are associated with the plurality of second solder pastes by using the plurality of first values and the plurality of first images, and determines at least one third solder paste, which is associated with the respective second fault types.

A printing solder point quality identification and maintenance suggestion system and a method thereof are disclosed. In the system, when solder paste inspection data, component inspection data or circuit inspection data indicates presence of defect, an analysis and calculation device intercepts the operating data, the equipment data, the raw material data, the process data and the environment data from a time data stream, to generate a data feature portrait. The analysis and calculation device then sets a reliability value based on location association between the solder paste inspection data, the component inspection data, the circuit inspection data and the maintenance data in the location data stream, and performs similarity calculation on the data feature portrait and the comparison data feature portrait to calculate a similarity value, and then calculates a relative reliability value, and compares the relative reliability value with a reliability threshold value to generate maintenance suggestion information.

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.

A computer-implemented method of predicting a quality of a printed circuit board (PCB) assembly includes obtaining production data relating to production of the PCB assembly. The production data is mapped onto a latent vector of a latent space of a trained adaptive algorithm. The trained adaptive algorithm is trained on real X-ray images of PCB assemblies and/or serves for generating X-ray images of PCB assemblies. A subspace of the latent space related to the latent vector is determined. The subspace indicates a quality of the PCB assembly. Alternatively or additionally, an X-ray image of the PCB assembly is generated by the trained adaptive algorithm based on the latent vector in order to determine a quality of the PCB assembly.

Method for inspecting a geometry (10) having a surface (11), which method uses an inspection device (100) having a digital image capturing means (120) and a shape scanning means (130), comprising the steps of

a) orienting the inspection device (100) in a first orientation;
b) depicting the surface (11) to produce a first image;
c) measuring a shape of a first geometry part to produce a first shape;
d) moving the inspection device (100) to a second orientation;
e) depicting the surface (11) to produce a second image;
f) measuring a second part of said geometry (10) to produce a second shape;
g) using digital image processing based on said first and second images, determining a geometric orientation difference between said first and second orientations;
h) determining a geometric relation between the first and second shapes; and
i) producing a data representation of said geometry based on said first shape, said second shape and said geometric relation.

The invention also relates to a device.

A method for inspecting a joint of an assembly, in particular an assembly of a motor vehicle, consisting of two components joined together by a joining process, includes the method steps of: orienting an inspection device with respect to at least one region of the joint to be tested, imaging an actual image of the joint to be tested on a display device; and displaying joint information relating to the joint to be tested of the assembly via the display device.

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.

The welding line detection system includes a photographing unit that photographs an image of an object to be welded, a coordinate system setting unit that sets a user coordinate system based on a marker included in the photographed image, a point-group-data plotting unit that detects a specific position of the marker on the basis of the image, sets the detected specific position on point group data acquired by a distance measurement sensor that measures a distance to the object to be welded, and plots, in the user coordinate system, the point group data to which coordinates in the user coordinate system, using the set specific position as an origin, are given, and a welding line detection unit that detects, on the basis of the point group data plotted in the user coordinate system, a welding line of the object to be welded.

The present disclosure provides an inspection device for use in a mounting system including a mounting device for disposing a component on a board, including a control section configured to extract a mass area included in a captured image resulting from imaging a processing target object where a viscous fluid is formed at a predetermined part, obtain a center of gravity of the mass area so extracted, and determine whether the center of gravity is included in a normal range of the predetermined part as a reference of the captured image to thereby determine whether a bridge has occurred where the viscous fluid is formed over adjacent predetermined parts.

In one embodiment, an X-ray inspection system may access a first set of X-ray images of one or more first samples that are labeled as being non-conforming. The system may adjust a classification algorithm based on the first set of X-ray images. The classification algorithm may classify samples into conforming or non-conforming categories based on an analysis of corresponding X-ray images. The system may analyze a second set of X-ray images of a number of second samples using the adjusted classification algorithm. The second samples may be previously inspected samples that have been classified as conforming by the classification algorithm during a previous analysis before the classification algorithm is adjusted. The system may identify one or more of the second samples from the second set of X-ray images. Each identified second sample may be classified as non-conforming by the adjusted classification algorithm.