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 intrinsic process signal-based online expulsion detection method for resistance spot welding process, which comprises: acquiring the intrinsic process signal and current signal output by sensors installed at two electrodes in real-time during the welding process and establishing a relationship graph; performing expulsion judgement based on the relationship graph to obtain expulsion frequency, single intrinsic process signal feature and an accumulated feature; calculating expulsion metal volume according to the accumulated feature and electrode profile features to obtain a prediction expulsion metal amount. The method performs online prediction of the expulsion metal amount according to the intrinsic process signal for resistance spot welding process, thereby achieving online quantitative estimation of the expulsion intensity, overcoming the defect of the traditional technology which relies on manual detection, and improving detection efficiency and accuracy.

When a material to be welded is irradiated with a laser beam emitted by a processing head, a beam receiving units receive a radiated beam including a reflected beam of the laser beam and a monitoring beam that is caused by thermal radiation and is a beam having a wavelength different from that of the reflected beam. A spectrometer unit spectrally separates the reflected beam and the monitoring beam, and converts the monitoring beam into a first electric signal. A trigger unit converts the reflected beam into a second electric signal, and outputs a trigger signal when a level of the second electric signal is a predetermined threshold value or higher. When the trigger signal is input, a laser welding monitor starts, based on the first electric signal, a determination of whether or not laser welding of the material to be welded is normally performed.

A control device that executes machining to cut a workpiece into a part and a remaining material; a detection unit that determines, as a time-series signal, a result of observing in time series a state of the workpiece; a machining state evaluation unit that determines, as evaluation information, a result of evaluation on a state of the machining for each of sections obtained by dividing the machining path, based on the time-series signal; an evaluation information storage unit that stores contour line evaluation information in which a contour line is associated with the evaluation information; and a sorting operation determination unit that determines a sorting control command for controlling sorting operation in which the part is taken out from a position where the workpiece is machined and moved to a target position, based on the contour line evaluation information; are provided.

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.

This disclosure describes an additive manufacturing method that includes monitoring a temperature of a portion of a build plane during an additive manufacturing operation using a temperature sensor as a heat source passes through the portion of the build plane; detecting a peak temperature associated with one or more passes of the heat source through the portion of the build plane; determining a threshold temperature by reducing the peak temperature by a predetermined amount; identifying a time interval during which the monitored temperature exceeds the threshold temperature; identifying, using the time interval, a change in manufacturing conditions likely to result in a manufacturing defect; and changing a process parameter of the heat source in response to the change in manufacturing conditions.

A welding component is provided for welding to a component via at least one welding region. The welding component includes: at least one welding indicator arrangement provided on the welding component; and, the at least one welding indicator arrangement includes a welding region minimum indicator to be covered by the at least one welding region and a welding region maximum indicator not to be covered by the at least one welding region.

The present invention relates to a method and a device for monitoring the process for a welding seam formed by means of collision welding, in which a first joining partner (1) and a second joining partner (2) are moved toward one another by an introduction of energy and are welded to one another to form the welding seam. A light flash between the first joining partner (1) and the second joining partner (2) is detected during the welding by an optical capture unit (6), which measures in a time-resolved manner, having at least one detector (19, 20, 24) and an actual value of a beginning of the light flash, a duration of the light flash, an intensity of the light flash, and/or an intensity distribution over time of the light flash is determined by an analysis unit (16) and compared to a respective target value of the beginning of the light flash, the duration of the light flash, the intensity of the light flash, and/or the intensity distribution over time of the light flash. The weld seam is only classified as qualitatively adequate if a maximum deviation of the actual value from the target value is maintained.

A device for monitoring, in particular for closed-loop control, of a thermal cutting process carried out on a workpiece. The device includes a focusing unit for focusing a machining beam, in particular a laser beam, onto the workpiece for the formation of a kerf on the workpiece. The device also includes an image acquisition unit to generate at least one image of a region of the workpiece, and an evaluation unit configured to determine, based on the at least one image, at least one measured variable for the course of the gap width of the kerf in a thickness direction of the workpiece. The invention also relates to an associated method for monitoring, in particular for closed-loop control, of a thermal cutting process carried out on a workpiece.

Systems and methods for welding are described. The welding system can include, for example, a welding power source, a welding torch, and a computer. The computer and the welding torch can be operatively coupled to the power source. A first weld is performed and its signature is saved by the computer. It is considered a high quality weld and is selected as a weld reference. A second weld is performed and its signature is saved by the computer. The computer then computes a single weld confidence result for the second weld based on a comparison between the signature data of the second weld and the signature data of the reference weld. A weld fault condition is triggered based on the single weld confidence result which causes the welding system to stop or to modify the welding operation, and/or which causes the welding system to send out communications relating to the triggering of the weld fault condition.