A machine learning apparatus able to obtaining an optimal shift amount of an assist gas. The machine learning apparatus comprises a state-observation section configured to observe machining condition data included in a machining program given to the laser machine, and measurement data of a dimension of dross generated at a cutting spot of the workpiece when the machining program is executed, as a state variable representing a current state of an environment in which the workpiece is cut; and a learning section configured to learn the shift amount in association with cutting quality of the workpiece, using the state variable.

To allow compensation of position error of a laser beam from a target position in a laser machine in response to temperature change while the compensation is to be affected intricately by a plurality of optical parts and constituting members. A machine learning device performs machine learning on a laser machine comprising a plurality of galvanometer mirrors for reflection of a laser beam and a plurality of galvanometer motors for driving corresponding ones of the galvanometer mirrors to rotate, and scanning the laser beam over a workpiece. The machine learning device comprises: input data acquisition unit that acquires at least two detected temperatures from the galvanometer mirrors and the galvanometer motors as input data; label acquisition unit that acquires a coefficient as a label for calculating a machining target position from an actual position of machining with the laser beam on the workpiece; and learning unit that performs supervised learning using a set of the label and the input data as training data to construct a mathematical model for calculating the machining target position from the actual machining position on the workpiece on the basis of the at least two detected temperatures.

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.

The present invention relates to a method for monitoring the condition of a laser machining head, said method comprising the steps of: detecting current measurement data by means of at least one sensor unit arranged within the laser machining head, determining an input vector based on the acquired current measurement data; and determining an output vector by applying a model trained by machine learning to the input vector, said output vector containing estimated current condition data of at least two elements of the laser machining head.

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 soldering process method includes steps of: establishing a material component database; establishing a working parameter database; analyzing material and component characteristics required for a new soldering process; comparing the characteristics with information in the material component database; selecting operating parameters corresponding to the material and component characteristics similar to those required for the new soldering process; performing the soldering process using the operating parameters corresponding to the material and component characteristics similar to those required for the new soldering process; measuring and recording the soldering process execution information and the final product information; determining whether the final product of the solder process meets the quality control requirements; using the machine learning method to fit the soldering process execution information and the final product information of the solder process to get the operating parameters for the next soldering process when the final product does not meet the quality control requirements.

A CWT-based method to calculate transmission coefficients of different Lamb waves using individual LEU signals. A neural network was trained to accurately predict WPDs based on the transmission coefficients of selected Lamb waves and the LEU signal energy. The method is capable of inspecting WPDs quickly along welds in thin structures.

A machining condition adjustment device adjusts settings of an ionizer so as to neutralize a charge carried by plasma generated during laser beam machining of a workpiece by a laser beam machining device, calculates an amount of charge per unit time that is to be radiated from the ionizer, based on the amount of charge carried by the plasma generated during the laser beam machining, and sets the ionizer to radiate the calculated amount of charge per unit time.

Quality judgment on a laser beam intensity distribution is performed by taking an observation condition of the laser beam into consideration. A machine learning device includes: a state observing means that acquires data indicating an intensity distribution of a laser beam and data indicating a condition for observing the laser beam, performed to generate the data indicating the intensity distribution as input data; a label acquisition means that acquires an evaluation value related to judgment of the quality of the laser beam as a label; and a learning means that performs supervised learning using a pair of the input data acquired by the state observing means and the label acquired by the label acquisition means as training data to construct a learning model for judging the quality of the laser beam.

Disclosed is a system having a robotic welding system, a controller, a camera, and a processor. The robotic welding system is configured to weld metal sections together in accordance with a plurality of welding variables. The controller is configured to automatically control the robotic welding system. The camera captures sequential images of the welding performed by the robotic welding system. According to an embodiment, the processor is configured to process the sequential images to determine when a selected welding state is to change to a next welding state based on the selected welding state and multiple consistent determinations of the next welding state, and to signal that change to the controller to effect a change in how the welding is performed by the robotic welding system. By considering multiple consistent determinations of the next welding state, there can be a high probability that the next welding state is correct.