A system and method of enhanced automated welding of a first workpiece and a second workpiece are provided. The method comprises providing a system for intelligent robot-based welding of the first workpiece and the second workpiece. The method further comprises determining a geometrical location of the first workpiece and the second workpiece to be welded at a welding sequence based a predetermined process variable. The method further comprises adjusting the predetermined process variable based on the geometrical location of the first and second workpieces to define an actual process variable. The method further comprises welding a first portion of the first and second workpieces with the actual process variable to define a first welded portion. The method further comprises determining a weld quality of the first welded portion.

A test procedure system comprising a test component test procedure corresponding to test instructions, a computer model system comprising a test component computer model comprising inspection indicia therein, a head mounted display comprising an alignment system, aligning the test component within a field of view with the computer model to align the inspection indicia and a user interface associated with the head mounted display for entering test results.

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 resistance spot welding control and a method thereof are disclosed.

A system for regulating welding parameters of a spot welding machine joining a plurality of panels according to an aspect of the present disclosure includes an ultrasonic sensor installed inside an electrode of a welding gun, applying a ultrasonic wave to a welding part of the panel, and detecting a reflected ultrasonic signal; an ultrasonic analyzer capable of creating ultrasonic analysis information by analyzing in real time the ultrasonic signal; and a welding controller receiving the ultrasonic analysis information from the ultrasonic analyzer, performing adaptive welding control with the welding parameters set based on information of the panel, and compensating in real time one or more welding parameters according to the received ultrasonic analysis information.

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 method for monitoring a curved solidified weld seam during the welding of a workpiece includes carrying out distance measurements using a measurement beam of an optical coherence tomograph both at one or more pre-measurement points situated upstream of a present welding position, and at one or more post-measurement points situated downstream of the present welding position, and monitoring the curved, solidified weld seam on the basis of the distance measurements. In the case of a plurality of post-measurement points, a post-measurement line is formed from the plurality of post-measurement points and positioned to be offset relative to a pre-measurement line in the direction of the pre-measurement line toward the curved, solidified weld seam and/or is rotated relative to the pre-measurement line. In the case of a single post-measurement point, spacing the single post-measurement point from a line passing through the present welding position in the welding direction.

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 laser processing monitoring device is a monitoring device of a laser processing machine that performs desired laser processing by irradiating a given metal-based workpiece with a laser beam LB and melting the workpiece by means of laser energy, and includes a laser oscillator, a laser power supply, a controller, a guide beam generation unit, delivery optical fibers, a head (an emission unit and a sensor unit, an operation panel, and a monitoring unit. The monitoring unit is a laser monitoring device in the present embodiment, and is configured to mainly include the controller, the operation panel, a sensor signal processing unit, the sensor unit, and the like.

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.