A stress luminescence measurement method according to one aspect includes the steps of: placing a stress luminescent material on a surface of a sample; irradiating the stress luminescent material with excitation light; acquiring a first captured image by imaging the stress luminescent material during irradiation of the excitation light; applying a load to the sample; acquiring a stress luminescence image by imaging stress luminescence of the stress luminescent material; irradiating the stress luminescent material after removal of the load with the excitation light; acquiring a second captured image during irradiation of the excitation light by imaging the stress luminescent material in a state in which the load has been removed; and storing the first captured image and the second captured image in a memory in association with the stress luminescent image.

In a learning phase, a processor of a sample observation device: stores design data on a sample in a storage resource; creates a first learning image as a plurality of input images; creates a second learning image as a target image; and learns a model related to image quality conversion with the first and second learning images. In a sample observation phase, the processor obtains, as an observation image, a second captured image output by inputting a first captured image obtained by imaging the sample with an imaging device to the model. The processor creates at least one of the first and second learning images based on the design data.

Examples of methods for point cloud alignment are described herein. In some examples, a method includes orienting a model point cloud or a scanned point cloud based on a set of initial orientations. In some examples, the method includes determining, using a first portion of a machine learning model, first features of the model point cloud and second features of the scanned point cloud. In some examples, the method includes determining, using a second portion of the machine learning model, correspondence scores between the first features and the second features based on the set of initial orientations. In some examples, the method includes globally aligning the model point cloud and the scanned point cloud based on the correspondence scores.

An image processing method whereby data pertaining to an estimated captured image obtained from reference data of a sample is acquired using an input acceptance unit, an estimation unit, and an output unit. The data is used when comparing the estimated image and an actual image of the sample, wherein the method includes: an input acceptance unit accepting input of the reference data, process information pertaining to the sample, and trained model data; the estimation unit using the reference data, the process information, and the model data to calculate captured image statistics representing a probabilistic distribution of values attained by the data of the captured image; and the output unit outputting the captured image statistics, and generating the estimated captured image from the captured image statistics. This permits reducing the time required for estimation and to perform comparison in real time.

A system for performing computer-assisted image analysis of welds and related methods is disclosed. Digital images are captured at a worksite and sent to a remote image analysis system of a weld analytics system that analyzes images to determine whether the images conform to weld specifications. The remote image analysis system may be trained by artificial intelligence or machine learning.

A system for providing a thermal interface material on a tray for an electric vehicle battery includes a material dispensing system (MDS) and a vision system. The MDS includes a set of pumps, a robotic dispensing system (RDS), and a controller. The set of pumps includes a first pump to house a first material and a second pump to house a second material different from the first material. The RDS is fluidly coupled to the set of pumps and dispenses a third material on the tray in a defined pattern, where the third material is a mixture of the first and second materials. The vision system is configured to capture a first set of images. The controller is configured to determine a nozzle offset of the RDS based on the first set of images and control a position of the RDS based on the nozzle offset.

A method of fabricating a semiconductor device includes generating a mask based on second layout data obtained by applying an OPC model to first layout data and performing a semiconductor process using the mask on a substrate, obtaining a plurality of pattern images by selecting a plurality of sample patterns from the substrate, selecting sample images corresponding to the sample patterns from each of the first layout data, the second layout data, and simulation data obtained by performing a simulation based on the second layout data, generating a plurality of input images corresponding to the sample patterns by blending the sample images corresponding to the sample patterns, respectively, and generating an error prediction model for the OPC model by training a machine learning model using a data set including the input images and the pattern images.

The present invention provides a measurement program selection assisting apparatus capable of visually confirming whether a selected measurement program is suitable for an object to be measured. One aspect of the present invention is a measurement program selection assisting apparatus comprising: a measurement program database storing a measurement program related to measurement of an object and superimposed display information corresponding to a three-dimensional shape of the object in association with each other; a display unit capable of displaying information defined in a virtual space superimposed on the real space; and a display control unit for acquiring the superimposed display information corresponding to a selected measurement program from the measurement program database and displaying the acquired superimposed display information in a mixed reality on the display unit

Disclosed are a method for evaluating a geometric form of a honeycomb product and a system for detecting and evaluating a geometric form of a honeycomb product. The method for evaluating the geometric form includes: acquiring a top-surface image and a side-surface image of the honeycomb product; acquiring vertex coordinates by extracting vertices from the top-surface image; acquiring serial numbers of six vertices of each cell by reconstructing the cells of the top-surface image; computing deviation angles of six interior angles of each cell based on the serial numbers and vertex coordinates of the six vertices of each cell; extracting a top-surface side boundary and a side-surface side boundary of the honeycomb product from the top-surface image and the side-surface image; computing a maximum top-surface deflection and a maximum side-surface deflection of the honeycomb product based on the top-surface side boundary and the side-surface side boundary.

A computer system is provided for processing ultrasonic data of an ultrasonic probe applied to an area of an aircraft component that includes carbon fiber reinforced polymer. C-scan data is obtained and a preliminary mesh is defined over the C-scan data by taking into account the underlying structural or mechanical characteristics of the analyzed component. The mesh is further refined and data gathered for each mesh cell. A heat map is generated based on the mesh.