Provided are a damage diagram creation method, a damage diagram creation device, a damage diagram creation system, and a recording medium capable of detecting damage with high accuracy based on a plurality of images acquired by subjecting a subject to split imaging. In a damage diagram creation method, damage of a subject is detected from each image (each image in a state of being not composed) constituting a plurality of images (a plurality of images acquired by subjecting the subject to split imaging), and thus, damage detection performance is not deteriorated due to deterioration of image quality in an overlapping area. Therefore, it is possible to detect damage with high accuracy based on a plurality of images acquired by subjecting the subject to split imaging. Detection results for the respective images can be composed using a composition parameter calculated based on correspondence points between the images.

A device to detect defects in a finished surface by analyzing images thereof taken under lighting of different colors includes a supporting mechanism, a transmitting mechanism, a detecting mechanism, and a processor. The transmitting mechanism carries and transmits the product. The detecting mechanism includes a detecting frame, and a light source assembly. The processor connects to a camera assembly, and preprocesses images obtained of the long side surfaces, of the width side surfaces, and of the chamfered side surfaces of the product to detect any defects of these surfaces of the product.

A system classifies materials utilizing a vision system that implements an artificial intelligence system in order to identify or classify and then remove automotive airbag modules from a scrap stream, which may have been produced from a shredding of end-of-life vehicles. The sorting process may be designed so that live airbag modules are not activated, which may cause damage to equipment or persons.

An ultrasonic quality control as disclosed can inspect a quality of a piece and classify the piece automatically. The piece can be scanned, and an image formed from the scanning. A reference piece is also scanned, and a reference image is formed. A negative image of the reference image is formed, and an indication image is created by utilizing the image and the negative image. The indication image is filtered by utilizing several image filters, each image filter filtering all data of the indication image except an image filter specific indication level data. Further several indication levels data are provided from the image filter specific indication level data, and the piece can be classified utilizing the several indication levels data.

The invention belongs to the technical field of quantitative statistical distribution analysis for micro-structures of metal materials, and relates to a method for automatic quantitative statistical distribution characterization of dendrite structures in a full view field of metal materials. According to the method based on deep learning in the present invention, dendrite structure feature maps are marked and trained to obtain a corresponding object detection model, so as to carry out automatic identification and marking of dendrite structure centers in a full view field; and in combination with an image processing method, feature parameters in the full view field such as morphology, position, number and spacing of all dendrite structures within a large range are obtained quickly, thereby achieving quantitative statistical distribution characterization of dendrite structures in the metal material. The method is accurate, automatic and efficient, involves a large amount of quantitative statistical distribution information, and is statistically more representative as compared with the traditional measurement of feature sizes of dendrite structures in a single view field.

A manufacturing history management system according to an aspect of the present disclosure includes a data generation unit, a data storage unit, and a data extraction unit. The data generation unit generates manufacturing history data by associating internal void information specific to a product and manufacturing history information of the product with a product identification code. The data storage unit stores the manufacturing history data relating to a plurality of the products generated by the data generation unit. The data extraction unit checks the internal void information of the manufacturing history data of the plurality of products stored in the data storage unit against the internal void information of a predetermined product and extracts the manufacturing history data that matches the internal void information of the predetermined product from the manufacturing history data of the plurality of products stored in the data storage unit.

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 three-dimensional shape registration method includes a step of acquiring three-dimensional CT data of a subject and at least three positioning members, which are acquired by performing X-ray CT imaging on the positioning members together with the subject, a step of measuring relative positions between the subject and each of the positioning members, and a step of performing registration between the subject in the CT data and the subject in three-dimensional design data of the subject based on the CT data, the design data, and information on the relative positions.

A process includes receiving a target quality value, receiving a measured quality value, and sending an inspection control instruction. The inspection control instruction is based at least in part on the target quality value and the measured quality value. The measured quality value is generated by a product inspector configured to inspect a sample before being sorted and may also include data generated by an output product inspector configured to inspect a sample after passing a sorting phase. The target quality value indicates a desired quality value of an output group of samples. The inspection control instruction controls the method of inspection performed by the product inspector. The measured quality values are received by a computing device, which in turn outputs the inspection control instruction to a product inspector.

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.