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.

An NC device as a numerical control device controls an additive manufacturing apparatus for producing an object by layering, on a workpiece, a material melted by being irradiated with a beam. The NC device includes: a feature quantity extracting unit that extracts, from image data, a feature quantity for determining a welding state that is a state where a molten material is added to the workpiece; and a process map creating unit that creates a process map in which a shape of the object and a layering condition are associated with each other. The layering condition is selected from among a plurality of layering conditions on the basis of a result of determination of the welding state, and includes at least one of beam intensity and a supply amount of a material.

A method is specified for determining a size of a defect occurring in a surface region of a component while a surface modification process is performed on the surface region. The method includes identifying an occurrence of a defect occurring in a surface region of a component on a basis of a set of images and determining a size of the defect in a separate method step from the occurrence of the defect identified. In addition, an apparatus and a computer program are specified for determining a size of a defect occurring in a surface region of a component while a surface modification process is performed on the surface region.

A method and a system for controlling a welding operation is provided by a welding machine controlled by an automatic motion generating mechanism. The method includes the steps of acquiring a set of welding data during the welding operation; computing at least a first part of the set of welding data and at least a second part of the set of welding data providing computed data, wherein the computed data indicate an abnormality; and transferring an abnormality output to a robot controller, which is controlling the welding machine and the automatic motion generating mechanism.

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 system for performing computer-assisted image analysis of welds and related methods is disclosed. Digital images are captured at a worksite and sent to a remote image analysis system of a weld analytics system that analyzes images to determine whether the images conform to weld specifications. The remote image analysis system may be trained by artificial intelligence or machine learning.

A laser machining system includes a state measurement unit that observes an internal state of a machining head or a varying state of a workpiece and outputs a machining state signal; an inference unit that determines a degree of quality of the laser beam machining as an inference result for each of machining defects concerning at least one type of machining defect on the basis of the machining state signal; a machining monitoring unit that monitors the workpiece for presence or absence of the machining defect and outputs a monitoring signal; a machining decision unit that decides whether there is the machining defect and determines a quality of the machining as a decision result; and a machinery safety unit that outputs, on the basis of the inference result and the decision result, a control signal that gives an instruction on whether to stop or continue the laser beam machining.

A laser irradiation apparatus is a laser irradiation apparatus including a laser light source, the laser irradiation apparatus including a failure prediction unit configured to perform failure prediction on a movable part used when a substrate is processed by the laser light source, in which the failure prediction unit acquires a physical quantity when the movable part is movable, and derives a failure time of the movable part based on an acquired physical quantity.

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 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.