A weld analytics system and method of tracking weld quality for a group of sequential welds. In one example, a weld analytics system receives a welding plan for a plurality of welds being performed by at least one welding machine. The weld analytics system determines an overall weld quality for the plurality of welds, based at least upon weld data from the at least one welding machine; and transmits a signal indicative of the overall weld quality of the plurality of welds to an interactive user terminal.

A method inspects weld quality in-situ. The method obtains a plurality of sequenced images of an in-progress welding process and generates a multi-dimensional data input based on the plurality of sequenced images and/or one or more weld process control parameters. The parameters may include: (i) shield gas flow rate, temperature, and pressure; (ii) voltage, amperage, wire feed rate and temperature (if applicable); (iii) part preheat/inter-pass temperature; and (iv) part and weld torch relative velocity). The method generates defect probability and analytics information by applying one or more computer vision techniques on the multi-dimensional data input. The analytics information includes predictive insights on quality features of the in-progress welding process. The method then generates a 3-D visualization of one or more as-welded regions, based on the analytics information, and the plurality of sequenced images. The 3-D visualization displays the quality features for virtual inspection and/or for determining weld quality.

A computer-implemented method for generating a model of a laser-engraved surface, the method comprising: transforming a first set of values for a first set of parameters associated with a laser-engraving process to a second set of values for a second set of parameters associated with a laser pulse model; modifying the laser pulse model based on the second set of values to produce a modified laser pulse model; and executing the modified laser pulse model to direct a plurality of laser pulses towards a computer-simulated surface, wherein the plurality of laser pulses modify the computer-simulated surface.

Deep learning is performed by using a material of a processing object, a laser beam parameter showing a property of laser beam which the processing object is irradiated with, and pre-processed part data and post-processed part data that respectively reflect laser processing-involved three-dimensional shapes of a processed part before and after irradiation of the processing object with the laser beam. A first relationship of input data that are the material of the processing object, the pre-processed part data, and the laser beam parameter to output data that is the post-processed part data after irradiation with the laser beam in relation to the input data is accordingly obtained as one learning result.

An example additive manufacturing apparatus includes an energy source to melt material to form a component in an additive manufacturing process, a camera aligned with the energy source to obtain image data of the melted material during the additive manufacturing process, and a controller to control the energy source during the additive manufacturing process in response to processing of the image data. The controller adjusts control of the energy source based on a correction determined by: applying an artificial intelligence model to image data captured by a camera during an additive manufacturing process, the image data including an image of a melt pool of the additive manufacturing process; predicting an error in the additive manufacturing process using an output of the artificial intelligence model; and compensating for the error by generating a correction to adjust a configuration of the energy source during the additive manufacturing process.

Disclosed are a method of controlling a size of a molten pool formed during a 3D printing process in real time and a system for the same. A thermal image of the molten pool is taken by a thermal imaging camera. A temperature interface exceeding a melting point of a base metal is specified in the thermal image. A size of the molten pool is obtained by estimating a length, a width, and a depth of the molten pool using the temperature interface. A predicted size of the molten pool is obtained using an artificial neural network model. An actually measured size of the molten pool is derived from a surface temperature of the molten pool. An error between the predicted size and the measured size of the molten pool is calculated to be used for controlling the size of the molten pool in real time.

A welding control device includes an actual position determination part configured to determine an actual position of the position control target on the basis of a weld characteristic amount detected from a captured image captured so as to include at least the position control target, the welding characteristic amount including at least one of a wire position of the weld wire or an electrode position of the electrode; a target position determination part configured to determine a target position being a target of the actual position corresponding to a weld condition for welding the weld target; and a position control part configured to execute a position control of the position control target to bring the actual position to the target position.

The laser machining system includes a laser device configured to output a laser beam, and a machining head configured to emit the laser beam emitted by a laser oscillator of the laser device and propagated through an optical fiber, to a workpiece in order to perform laser machining. The machining head includes at least one wavelength selective mirror having wavelength selectivity with various values of reflectivity and transmittance according to wavelengths, and at least one image capturing device. The laser machining system monitors abnormality in a laser optical system leading from the laser oscillator to the machining head, during the laser machining, by reflecting light propagated from a side of introduction of the laser beam into the machining head by the wavelength selective mirror, making the light incident on an image capturing surface of the image capturing device, and detecting incident light illuminance distribution appearing on the image capturing surface of the image capturing device.

A method for use in additive manufacturing of a three-dimensional workpiece is described. The method includes depositing material onto a substrate to form a shape of the workpiece in accordance with an additive manufacturing process; determining a thermal characteristic of at least a portion of the workpiece during the additive manufacturing process; determining that the thermal characteristic of at least the portion exceeds a threshold associated with the portion; adjusting a cooling parameter of a cooling flow to be applied to the workpiece responsive to determining that the thermal characteristic of at least the portion exceeds the threshold associated with the portion; and applying the cooling flow with the adjusted cooling parameter to at least the portion of the workpiece.

A system managing a polishing state of tips of a welding gun of each welding robot installed in a production line of a vehicle includes: a robot controller storing tip polishing data including the number of polishing of the tips and a polishing amount of the tips generated after each tip dressing of the welding gun; and a server collecting the tip polishing data from the robot controller to store the collected data according to robot identification information of the robot and learning the store data through artificial neural network to generate reference data determining the polishing state of the tips corresponding to the robot identification information. The robot controller sets artificial neural network of the robot based on the reference data and determines whether a polishing state of the tips according to the number of polishing and the polishing amount of the tips is normal.