Embodiments of systems and methods for supporting predictive and preventative maintenance are disclosed. One embodiment includes manufacturing cells within a manufacturing environment, where each manufacturing cell includes a cell controller and welding equipment, cutting equipment, and/or additive manufacturing equipment. A communication network supports data communications between a central controller and the cell controller of each of the manufacturing cells. The central controller collects cell data from the cell controller of each of the manufacturing cells, via the communication network. The cell data is related to the operation, performance, and/or servicing of a same component type of each of the manufacturing cells to form a set of aggregated cell data for the component type. The central controller also analyzes the set of aggregated cell data to generate a predictive model related to future maintenance of the component type.

System for 2D and 3D metal processing with fiber optic laser and plasma, that includes CNC for cutting metal plates with fiber optic laser and plasma and a robot arm for cutting and welding metals with fiber optic laser. The system is characterized because it includes three processes in one single equipment: metal cutting with fiber optic laser, metal cutting with plasma and metal welding with fiber optic laser. The equipment has a computer numerical control (CNC) system and a working area of 1200×3000 mm for cutting metals; it has two cutting heads, one for fiber optic laser and one for plasma as well as one 360° rotating robot arm on which the laser welding head or the laser cutting head can be placed for 3D welding, or cutting circular or rectangular pipes, respectively.

A machine learning device, which performs a task using a plurality of industrial machines and learns task sharing for the plurality of industrial machines, includes a state variable observation unit which observes state variables of the plurality of industrial machines; and a learning unit which learns task sharing for the plurality of industrial machines, on the basis of the state variables observed by the state variable observation unit.

To provide an arc welding robot system that allows for optimal welding each time welding is performed even in a situation in which the impedance and the inductance of a welding circuit vary in each welding. An arc welding robot system includes an arc welding machine, a robot controller, and a parameter keeping device provided in either or both of the robot controller and the arc welding machine. An impedance and an inductance of a welding circuit are acquired in advance for each configuration of welding object and saved in the parameter keeping device. The robot controller performs welding control on each of the welding objects based on the impedance and the inductance saved in the parameter keeping device depending on the configuration of the welding object.

A robot system that performs desired processing on a processing target object using a processing tool. The robot system includes a robot having an arm tip that holds the processing tool, a position detector that detects a position of the arm tip, and a robot controller that controls an operation of the robot based on a position command and a position feedback detected by the position detector. The robot controller includes an adjustment operation creating unit that, during adjustment of operation parameters for controlling the operation of the robot, acquires an application and an operation area of the robot and automatically creates an adjustment operation corresponding to the acquired application and the operation area and a parameter adjustment unit that automatically adjusts the operation parameters during execution of the adjustment operation created by the adjustment operation creating unit so that a performance required for the application is satisfied.

To provide a spot welding system of high reliability which, when an abnormality such as a spot missing in spot welding occurs, is capable of more precisely and accurately detecting this immediately. Provided are a robot-side system having a robot and a robot-side control unit which controls driving of the robot; and a welder-side system having a welding gun mounted to the robot, and a welder-side control unit, in which the robot-side system includes: a storage unit which stores in advance a required welding time needed in a sport welding operation of one location or a plurality of spot welding operations; a welding time measurement unit which measures an actual time from when a welding start command is outputted until receiving a welding completion command; and a comparative determination unit which determines quality by comparing the required welding time stored in the storage unit and the actual welding time measured by the welding time measurement unit.

Presented are intelligent non-autogenous metalworking systems and control logic for automated wire-to-beam alignment, methods for making/using such systems, and robot-borne laser welding/brazing heads with closed-loop control for real-time wire alignment. A method for controlling operation of a non-autogenous workpiece processing system includes a system controller receiving sensor signals from a position sensor indicative of a location of filler wire discharged into a joint region by a wire feeder. Using the received sensor signals, the controller determines a displacement between the wire location and a location of a beam emitted onto the joint region by a beam emitter. If the wire displacement is greater than a threshold wire displacement value, the controller responsively determines a corrective force calculated to reduce wire displacement to below the threshold wire displacement value. The controller then commands the actuator to pivot the processing head to thereby apply the corrective force to the discharging filler wire.

A system and method to correct for deposition errors during a robotic welding additive manufacturing process. The system includes a welding power source to sample instantaneous parameter pairs of welding output current and wire feed speed in real time during a robotic welding additive manufacturing process while creating a current weld layer of a 3D workpiece part. An instantaneous ratio of welding output current and wire feed speed are determined for each instantaneous parameter pair. A short term running average ratio is determined based on the instantaneous ratios. A relative correction factor is generated based on at least the short term running average ratio and is used in real time while creating the current weld layer to compensate for deviations in a deposit level from a desired deposit level for the current weld layer.

A teaching device for a laser machining system which performs laser machining on a workpiece while moving an irradiation position of laser light using a robot includes a graphical user interface processing unit which displays machining periods, in each of which machining is performed by irradiating a corresponding one of a plurality of machining points set for the workpiece with the laser light while the robot moves along a machining path, and non-machining intervals between the machining periods of the machining points arranged in time series in a band-like region in a distinguishable manner.

A controller calculates a correction amount of a position of a robot 1 at a movement point in a first movement path, and drives the robot 1 in a second movement path obtained by correcting the first movement path. The controller includes a second camera configured to detect a shape of a part after a robot apparatus performs a task, and a variable calculating unit configured to calculate, based on an output of the second camera, a quality variable representing quality of a workpiece. When the quality variable deviates from a predetermined determination range, a determination unit of the controller determines that the position or an orientation of the robot 1 needs to be modified based on a correlation between the correction amount of the position in the first movement path and the quality variable.