Described herein are examples of weld training systems that show (e.g., transparent and/or translucent) “ghost” images of a welding tool on a display screen of a welding headgear to indicate target positions and/or target orientations of an actual welding tool. In some examples, the weld training systems may additionally “reset” the target tool image to a position closer to the actual welding tool if the target tool image gets too far away. The ability to “reset” the target tool image to a position closer to the actual welding tool may help in minimizing a risk that an operator 106 will overcompensate to try to catch up with the target tool image, which can be detrimental to the weld. Additionally, resetting the target tool image to a position closer the welding tool may allow an operator to better perceive and/or understand differences in orientation and/or other technique parameters.

An example weld training system includes: a display device; a processor; and a machine readable storage device comprising machine readable instructions to: collect data describing the welding-type operation; collect, from a set of one or more cameras, images depicting one or more of: a) a posture of an operator or a technique of the operator during the welding-type operation; or b) a welding torch used in the welding-type operation; synchronize the collected data and the collected images with comparative images of a predefined correct performance of the welding-type operation; and display the collected data and the collected images together in a synchronized manner on the display device such that the processor updates the display device to display corresponding synchronized data and images when a portion of the welding-type operation is selected for viewing, the displaying comprising displaying the collected data, the collected images, and the comparative images.

In the context of additive manufacturing processes wherein an object is built by layered accumulations of discrete instantaneous deposits of feedstock material at specific locations according to a three-dimensional digital data model, systems and methods are taught for operating multiple independently-moving depositing devices in a shared build space to build the object. In some embodiments, depositing components perform discrete material depositing actions according to sequential lists of deposit location instructions which are dynamically sortable, enabling a control methodology to alleviate collision risks among depositing components and to improve thermal conditions of a workpiece during construction. Further embodiments provide for dynamic apportionment of discrete deposition actions among the available depositing devices for load balancing and fault tolerance.

A robotic electric arc welding system includes a welding torch, a welding robot configured to manipulate the welding torch during a welding operation, a robot controller operatively connected to the welding robot to control weaving movements of the welding torch along a weld seam and at a weave frequency and weave period, and a welding power supply operatively connected to the welding torch to control a welding waveform, and operatively connected to the robot controller for communication therewith. The welding power supply is configured to sample a plurality of weld parameters during a sampling period of the welding operation and form an analysis packet, and process the analysis packet to generate a weld quality score, wherein the welding power supply obtains the weave frequency or the weave period and automatically adjusts the sampling period for forming the analysis packet based on the weave frequency or the weave period.

An optical manufacturing process sensing and status indication system is taught that is able to utilize optical emissions from a manufacturing process to infer the state of the process. In one case, it is able to use these optical emissions to distinguish thermal phenomena on two timescales and to perform feature extraction and classification so that nominal process conditions may be uniquely distinguished from off-nominal process conditions at a given instant in time or over a sequential series of instants in time occurring over the duration of the manufacturing process. In other case, it is able to utilize these optical emissions to derive corresponding spectra and identify features within those spectra so that nominal process conditions may be uniquely distinguished from off-nominal process conditions at a given instant in time or over a sequential series of instants in time occurring over the duration of the manufacturing process.

Methods and apparatus to determine heat input to a weld are disclosed. An example welding-type power supply includes a power converter to convert input power to output welding-type power, and a controller configured to: during a welding-type operation, calculate average instantaneous power values of the welding-type operation for discrete, non-overlapping time periods by multiplying voltage measurements and corresponding current measurements; identify an end of the welding-type operation; determine a duration of the welding-type operation; calculate a total average instantaneous power of the welding-type operation based on the average instantaneous power values for the time periods and the duration of the welding-type operation; and determine whether an acceptable range of the welding-type operation is exceeded based on the total average instantaneous power and the duration of the welding-type operation.

The present invention discloses a control device and method for formation of a weld seam based on frontal visual sensing of a weld pool. In the present disclosure, structural light is adopted to irradiate the concave surface of the weld pool, and a visual sensor is adopted to acquire corresponding structured light images. The weld pool depression feature is acquired through image processing. The welding current is adjusted in real time to maintain the weld pool depression feature constant, and thus the uniform backside width of the weld seam can be acquired to achieve uniform and consistent penetration of the weld seam. The present disclosure only relies on the structural light information on the topside of the weld pool to achieve the control of formation of the weld seam and can be applied to the filler-wire-free DC gas tungsten arc welding of tight butt joints.

Systems for simulating joining operations, such as welding, are disclosed. In some examples, a system may use a desktop device for conducting welding simulations, such as for purposes of training In some examples, the system may additionally, or alternatively, use modular workpieces. In some examples, the system may additionally, or alternatively, conduct the welding simulation based on one or more selected pieces of welding equipment.

A bead appearance inspection device includes an acquisition unit that acquires input data related to a welding bead, a storage unit that stores a first determination standard and a second determination standard used for an inspection of a defect of the welding bead, a first determination unit that executes a first inspection determination on the welding bead, and k second determination units, where k is an integer of 1 or more, that execute a second inspection determination on the welding bead. An appearance inspection result of the welding bead is created and output by using a determination result indicating whether a first inspection result acquired by the first inspection determination satisfies the first determination standard and a determination result indicating whether a second inspection result satisfies the second determination standard.

A bead appearance inspection device includes an input unit configured to enter input data related to a welding bead of a workpiece produced by welding, a preprocessing unit configured to perform a preprocessing of converting a shape of the welding bead into a predetermined shape on the input data, and k inspection determination units, where k is an integer of 1 or more, that are equipped with k types of artificial intelligence and that are configured to inspect and determine presence or absence of a welding defect of the welding bead based on processings of the k types of artificial intelligence targeting input data on which the preprocessing is performed.