A smart acoustic information recognition-based welded weld impact quality determination method and system, comprising: controlling a tip of an ultrasonic impact gun (1) to perform impact treatment on a welded weld with different treatment pressures, treatment speeds, treatment angles and impact frequencies, obtaining acoustic signals during the impact treatment, calculating feature values of the acoustic signals, and constructing an acoustic signal sample set including various stress conditions; marking the acoustic signal sample set according to impact treatment quality assessment results for the welded weld; establishing a multi-weight neural network model, and using the marked acoustic signal sample set to train the multi-weight neural network model; obtaining feature values of welded weld impact treatment acoustic signals to be determined, inputting the feature values into the trained multi-weight neural network model, and outputting determination results for welded weld impact treatment quality to be determined.

This disclosure provides systems, methods, and apparatuses, including computer programs encoded on computer storage media, that provide for welding techniques for manufacturing robots, such multipass welding techniques for welding robots. For example, the welding techniques may enable generation of weld instructions based on a welding fill plan. The instructions may be generated based on a bead model or a table that indicates a wire feed speed, a travel speed, or a voltage. As another example, the techniques may enable generation of weld instructions based on the one or more dimensions of a seam. As another example, the techniques may enable generation of a joint model of a cross-section of a seam to be welded. The joint model may be generated based on a combination of a plurality of feature components to generate the joint model of the seam. Other aspects and features are also claimed and described.

Disclosed is a machine learning device of a cutting condition adjustment apparatus including: a state observation section that observes, as state variables indicating a current state of an environment, cutting condition data indicating a laser cutting condition for a laser cutting and oblique rearward temperature rise data indicating a temperature rise value at an oblique rearward part of a cutting front of a workpiece, a determination data acquisition unit that acquires temperature rise value determination data for determining propriety of the temperature rise value during cutting based on the laser cutting condition for the laser cutting as determination data indicating a propriety determination result of the cutting of the workpiece, and a learning unit that learns the temperature rise value and adjustment of the laser cutting condition for the laser cutting in association with each other using the state variables and the determination data.

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.

In certain embodiments, a portable metal working robot system includes a metal working tool configured to perform a metal working process on one or more metal parts. In addition, the portable metal working robot system includes communication circuitry configured to receive control signals from a control system located remotely from the portable metal working robot system. The portable metal working robot system also includes control circuitry configured to control operational parameters of the portable metal working robot system in accordance with the received control signals.

A machine learning device of a machining condition adjustment apparatus observes, as state variables expressing a current state of an environment, laser machining condition data in laser machining, and gas target deviation data indicating a target deviation of a pressure loss or a flow rate of assist gas. Then the machine learning device acquires determination data for determining quality of a workpiece machined on the basis of the laser machining condition, and learns the target deviation of the pressure loss or the flow rate of the assist gas and adjustment of the laser machining condition in the laser machining in association with each other using the determination data and the observed state variables.

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.

Embodiments of systems and methods to relate a human operator to a welding power source are disclosed. One embodiment is a networked system having a server computer. The server computer is configured to receive first data including at least one of an identity or a location of a welding helmet within a welding environment, and a triggering status indicating a triggering of an arc detection sensor of the welding helmet due to initiation of a welding arc. The server computer is configured to receive second data including at least one of an identity or a location of a welding power source within the welding environment, and an activation status indicating an activation of the welding power source. The server computer is configured to match the welding power source to a human operator using the welding helmet based on at least the first data and the second data.

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.

A method for identifying splatters or weld seam defects during laser processing of a workpiece includes processing the workpiece using a processing laser beam of a laser processing machine, compiling at least one recording of radiation emerging during the processing of the workpiece using an optical sensor that has a plurality of pixels for recording the radiation, and inputting the at least one recording into an evaluation unit. The evaluation unit has a machine learning algorithm configured as a convolutional neural network in a U-Net architecture. The machine learning algorithm has been trained with verified recordings of splatters or weld seam defects. The method further includes identifying one or more splatters or weld seam defects in the processing of the workpiece by running the machine learning algorithm using the at least one recording as input, and outputting an output indicating the identified one or more splatters or weld seam defects.