The present disclosure relates to a portable production machine, a mobile terminal, a production system having a portable production machine and a mobile terminal, and a method for capturing data. According to aspects of the disclosure, on the production machine - which can be, for example, a welding machine or cutting machine - production data is automatically captured, organized into data sets, and converted into data set images, which are displayed on a display device. The displayed data set images are captured with a mobile terminal and transmitted to a central server. As a result, a plurality of production machines can be incorporated into a data system without the need for any changes to the hardware in the production machines.

A weld-line generating apparatus includes a point-cloud-data acquiring unit that acquires 3D point cloud data of workpieces to be welded that are arranged in a predetermined space, an edge extracting unit that extracts 3D point cloud data of edges from the 3D point cloud data acquired by the point-cloud-data acquiring unit, a workpiece point-cloud-data generating unit that generates a 3D point cloud data component of each of the workpiece based on 3D point cloud data that is obtained by removing the 3D point cloud data of edges extracted by the edge extracting unit from the 3D point cloud data acquired by the point-cloud-data acquiring unit, and a weld-line generating unit 24 that generates weld lines for the workpieces based on the 3D point cloud data components of the workpieces generated by the workpiece point-cloud-data generating unit.

A soldering quality inspection method and a soldering quality inspection apparatus are provided. The soldering quality inspection method includes: acquiring an inspection image; calculating, by a processing device, a dyed area percentage of an area of a part of a soldering region in the inspection image that is dyed by a dye ink relative to an area of the soldering region, and determining whether the dyed area percentage is greater than a predetermined dyed percentage. When the dyed area percentage is determined to be equal to or less than the predetermined dyed percentage, a position under inspection is determined to be of good soldering quality, and a corresponding inspection result information is generated. When the dyed area percentage is determined to be greater than the predetermined dyed percentage, the position under inspection is determined to be of poor soldering quality, and the corresponding inspection result information is generated.

A solder printing inspection device includes: an illumination device that irradiates, with a predetermined light, a printed circuit board on which a solder paste is printed; an imaging device that takes an image of the printed circuit board irradiated with the predetermined light and obtains image data; and a control device that: based on the image data, obtain three-dimensional measurement data of the solder paste printed on the printed circuit board, based on the three-dimensional measurement data, extracts upper portion shape data of an upper portion of the solder paste, the upper portion having a height equal to or higher than a predetermined height, and compares the upper portion shape data with a predetermined criterion and determines whether a quality of a three-dimensional shape of the upper portion of the solder paste is good or poor.

A secondary detection system for integrating automated optical inspection and neural network and a method thereof are disclosed. In the secondary detection system, an automated optical inspection apparatus performs automated optical inspection for pin solder joints on circuit board, and when a detection result indicates abnormal condition, the secondary detection device calculates a detection image probability value based on the component image feature and the template image feature, and calculate pin solder joint image probability values based on the component pin solder joint image feature and the template pin solder joint image feature through siamese neural network, to obtain a minimum probability value among the detection image probability value and pin solder joint image probability values. The minimum probability value is used to determine whether to change the detection result, thereby providing accurate detection result of automated optical inspection and increasing a first pass yield.

Provided is image processing unit that causes computer to perform: a spatter candidate region detection step of performing, for each of input images obtained by capturing workpiece during arc welding, detection of a spatter candidate region based on a pixel value indicating brightness of a pixel included in the input images; a reflected light region identification step of identifying, in the spatter candidate region detected in the spatter candidate region detection step, a reflected light region in which reflected light of arc light is shown, based on color information of a reference pixel included in the spatter candidate region; and a spatter number identification step of identifying, as the number of spatters, the number of spatter candidate regions obtained by removing the reflected light region identified in the reflected light region identification step in the spatter candidate region detected in the spatter candidate region detection step.

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.

Various attachments for additive textile manufacturing machines are disclosed. In one example, an apparatus is provided which comprises a connector component and a liquid deposition component. The connector component is configured to attach to an additive textile manufacturing machine that produces a textile product. The liquid deposition component is configured to deposit a liquid on one or more materials of the textile product.

Disclosed is a quaternion multi-degree-of-freedom neuron-based multispectral welding image recognition method, comprising: using three cameras having different wavebands to obtain multispectral weld pool images, and respectively performing pre-processing and edge extraction on the weld pool images having the different wavebands obtained at a same moment by the three cameras; establishing a quaternion-based multispectral weld pool image edge model; extracting low-frequency features after a quaternion discrete cosine transform; using a quaternion-based multi-degree-of-freedom neuron network to perform classification, training and recognition on edge features of the multispectral weld pool images. Compared to traditional means, the present invention has multiple recognition information sources, strong anti-interference capabilities and high recognition accuracy.

A non-destructive system for detecting anomalies in weldment of a pipeline is provided including an imaging apparatus, an anomaly detection unit, and a computing device. The imaging apparatus produces image segments corresponding to segments of the circumferential area of the weldment. The anomaly detection unit includes an artificial intelligence platform that processes and analyzes the image segments to identify at least one of a type, size, and location of a welding anomaly within the weldment using a database of truth data. The computing device includes a graphical user interface that displays the image segments with an overlay of information relating to at least one of the type, size, and location of the welding anomaly to the user.