A method, apparatus, and system are provided to monitor and characterize the dynamics of a phase change region (PCR) created during laser welding, specifically keyhole welding, and other material modification processes, using low-coherence interferometry. By directing a measurement beam to multiple locations within and overlapping with the PCR, the system, apparatus, and method are used to determine, in real time, spatial and temporal characteristics of the weld such as keyhole depth, length, width, shape and whether the keyhole is unstable, closes or collapses. This information is important in determining the quality and material properties of a completed finished weld. It can also be used with feedback to modify the material modification process in real time.

A system for monitoring a laser machining process on a workpiece is disclosed. The system includes: a neuromorphic image sensor configured to generate image data of the laser machining process, and a computing unit configured to determine input data based on the image data, and to determine output data based on the input data by means of a transfer function, the output data containing information about the laser machining process. Further, a method for monitoring a laser machining process on a workpiece is disclosed.

The present invention relates to a weld bead inspection apparatus and, more specifically, to a weld bead inspection apparatus capable of precisely inspecting a weld bead. The present invention relates to the weld bead inspection apparatus for inspecting whether a weld bead (20) on a pipe (10) is defective, and disclosed is the weld bead inspection apparatus comprising: a housing (100) encompassing the pipe (10) so as to cover the weld bead (20), and forming a closed inspection space (S) therein; an inspection means (200) provided in the housing (100) so as to confirm the state of the weld bead (20) in the inspection space (S); and a defect determination unit for determining whether the weld bead (20) is defective on the basis of the state of the weld bead (20), having been confirmed through the inspection means (200).

A welding quality processing method and device, and a circuit board. The method includes: obtaining warpage data of each circuit board layer in a multi-layer circuit board under a preset welding temperature change curve; performing simulation according to a stacked state of the multi-layer circuit board and the warpage data to generate a warpage level of each region in the multi-layer circuit board in the stacked state; and processing the multi-layer circuit board according to the warpage level.

The present application relates to a method, device, and system for detecting welding spot quality abnormalities based on deep learning. The method includes: acquiring a dynamic welding parameter in a welding process corresponding to any target welding spot; inputting the dynamic welding parameter into a pre-trained dynamic welding parameter simulation model for simulation, and acquiring a welding simulation parameter output by the dynamic welding parameter simulation model; determining a deviation of the dynamic welding parameter from the welding simulation parameter, and determining that the target welding spot is an abnormal welding spot when the deviation is greater than a preset threshold. The solution of the present application can reduce the frequency of manual tearing down and batches for abnormality detection, which has a faster abnormality detection speed and may cover all welding spots.

A method for determining the cooling rate of a weld, comprising locating at least one non-consumable thermal sensor at a predetermined location from a weld pool generated during an active welding process, wherein the thermal sensor is located within the same plane of travel as a welding device creating the weld pool; determining the travel speed of the welding device; using the at least one non-consumable thermal sensor to gather temperature data from the weld pool; and determining the time interval between when the weld pool has a first measured temperature and when the weld pool has a second measured temperature that is less than the first measured temperature, wherein the determined time interval represents the cooling rate of the weld.

An object of the present invention is to appropriately perform a welding quality control. Therefore, a welding work data accumulation device (100) includes: a measurement unit (4, 5, 7, 9, 11, and 16) that measures a welding motion and a welding phenomenon when a welding operator (1) grips a welding torch (2) and performs welding on a welded body (3); a data analysis unit (14) that extracts an appropriate combination of a welding motion feature amount (Tw, Ht, and Sp) and a welding phenomenon feature amount (Iw, S, and Ssym) in correction with time or coordinates based on data acquired by the measurement unit (4, 5, 7, 9, 11, and 16); and a data accumulation unit (15) that creates a database (70) based on an extraction result of the data analysis unit (14).

An electric resistance welded steel pipe or tube comprises: a base metal being a steel sheet having a specific chemical composition and an electric resistance weld portion having a bond width of 40×10.sup.−6 m or more and 120×10.sup.−6 m or less, wherein C.sub.0-C.sub.1 is 0.05 mass % or less, where C.sub.0-C.sub.1 is a difference between C.sub.1 in mass % which is a minimum C content of the electric resistance weld portion and Co in mass % which is a C content of the steel sheet, and a depth of a total decarburized layer in each of an inner surface layer and an outer surface layer of the electric resistance welded steel pipe or tube is 50×10.sup.−6 m or less.

An electric resistance welded steel pipe or tube comprises: a base metal being a steel sheet having a specific chemical composition and an electric resistance weld portion having a bond width of 40×10.sup.−6 m or more and 120×10.sup.−6 m or less, wherein C.sub.0-C.sub.1 is 0.05 mass % or less, where C.sub.0-C.sub.1 is a difference between C.sub.1 in mass % which is a minimum C content of the electric resistance weld portion and Co in mass % which is a C content of the steel sheet, and a depth of a total decarburized layer in each of an inner surface layer and an outer surface layer of the electric resistance welded steel pipe or tube is 50×10.sup.−6 m or less.

A weld production knowledge system for processing welding data collected from one of a plurality of welding systems, the weld production knowledge system comprising a communication interface communicatively coupled with a plurality of welding systems situated at one or more physical locations. The communication interface may be configured to receive, from one of said plurality of welding systems, welding data associated with a weld. The weld production knowledge system may comprise an analytics computing platform operatively coupled with the communication interface and a weld data store. The weld data store employs a dataset comprising (1) welding process data associated with said one or more physical locations, and/or (2) weld quality data associated with said one or more physical locations. The analytics computing platform may employ a weld production knowledge machine learning algorithm to analyze the welding data vis-à-vis the weld data store to identify a defect in said weld.