A method for automatic welding of a structural steel assembly includes workpieces such as profiles and/or a sheet material. The method includs using an automated process to receive information from a CAD-CAM program about welds for welding the structural steel assembly, and to post-process the information received from the CAD-CAM program. The information of each single weld received from the CAD-CAM program includes data about e.g a type of a workpiece or of workpieces of the structural steel assembly which bound the weld, a weld type, a position of the respective weld relative to the workpieces of the structural steel assembly that bound the weld, a shape of the weld, a length of the weld, a path of the weld and a width of the weld. The post-processing includes splitting each weld in sections of which the individual welding parameters are predefined.

The repair welding system includes an inspection device and a welding device. The inspection device determines whether there is a defective portion in a welded portion. In a case that there is the defective portion in the welded portion, the inspection device extracts at least defect type information of the defective portion and inspection coordinate system defective portion position information, converts the inspection coordinate system defective portion position information into position information corresponding to a coordinate system of the welding robot and generates welding coordinate system defective portion position information, and transmits the defect type information and the welding coordinate system defective portion position information to the welding device. The welding device instructs execution of a repair welding on the defective portion based on the defect type information and the welding coordinate system defective portion position information.

A repair welding control device includes a memory that stores instructions and a processor that executes the instructions. The instructions cause the processor to perform acquiring information indicating a range of a defective portion in main welding of a workpiece, and determining a repair welding start point indicating a start point of repair welding and a repair welding end point indicating an end point of the repair welding such that a repair welding range includes all the range of the defective portion and a range wider than the range of the defective portion.

Embodiments of systems and methods providing pattern recognition and data analysis in welding and cutting are disclosed. In one embodiment, a system includes a server computer and a data store connected to the server computer. The server computer receives welding data, including core welding data and non-core welding data, over a computer network from welding systems used to generate multiple welds to produce multiple instances of a same type of part. The server computer performs an analysis on the welding data to identify and group same individual welds of the multiple welds without relying on weld profile identification numbers as part of the analysis. A group of the same individual welds corresponds to a same weld location on the multiple instances of the same type of part. The data store receives the welding data from the server computer and digitally stores the welding data as identified and grouped.

In one embodiment, a device comprises interface circuitry and processing circuitry. The processing circuitry receives, via the interface circuitry, a video stream captured by a camera during performance of an industrial process, wherein the video stream comprises a sequence of frames; detects, based on analyzing the sequence of frames, a degree of particle scatter that occurs during performance of the industrial process; and determines, based on the degree of particle scatter, that an anomaly occurs during performance of the industrial process.

A method of correcting angles of a welding torch positioned by a user while training a robot of a robotic welding system is provided. Weldment depth data of a weldment and a corresponding weld seam is acquired and 3D point cloud data is generated. 3D plane and intersection data is generated from the 3D point cloud data, representing the weldment and weld seam. User-placed 3D torch position and orientation data for a recorded weld point along the weld seam is imported. A torch push angle and a torch work angle are calculated for the recorded weld point, with respect to the weldment and weld seam, based on the user-placed torch position and orientation data and the 3D plane and intersection data. The torch push angle and the torch work angle are corrected for the recorded weld point based on pre-stored ideal angles for the weld seam.

A method of programming multiple weld passes in a collaborative robot welding system to perform multi-pass welding is provided. A root pass is programmed for a first weld seam by manually positioning a welding torch and automatically recording root pass position and angle data. Secondary passes for the first weld seam are also programmed. The tip of the welding torch is positioned at a start point and a stop point for each secondary pass. The start and stop position data of the start point and the stop point are automatically recorded for each secondary pass. Numerical position and angle offset data are automatically calculated. The root pass position and angle data and the offset data are stored as a multi-pass template. The template is translated and applied to a weld reference frame of a second weld seam to aid in programming secondary passes for the second weld seam.

Provided is an articulated robot arm capable of laser printing. The articulated robot arm includes: a communicator for receiving beverage order information; a grip part gripping and moving a cup; an articulation part having one side coupled to the grip part and including a plurality of articulation units; a controller controlling operations of the grip part and the articulation part; and a laser beam irradiation unit provided on at least a partial area of the grip part and irradiating a laser beam to print the beverage order information on the cup.

A method inspects weld quality in-situ. The method obtains a plurality of sequenced images of an in-progress welding process and generates a multi-dimensional data input based on the plurality of sequenced images and/or one or more weld process control parameters. The parameters may include: (i) shield gas flow rate, temperature, and pressure; (ii) voltage, amperage, wire feed rate and temperature (if applicable); (iii) part preheat/inter-pass temperature; and (iv) part and weld torch relative velocity). The method generates defect probability and analytics information by applying one or more computer vision techniques on the multi-dimensional data input. The analytics information includes predictive insights on quality features of the in-progress welding process. The method then generates a 3-D visualization of one or more as-welded regions, based on the analytics information, and the plurality of sequenced images. The 3-D visualization displays the quality features for virtual inspection and/or for determining weld quality.

An industrial robot including a movable robot arm for supporting a tool, and a control unit configured to control the movement of the robot. The control unit is provided with an alignment function for aligning the tool with at least one specified axis. The control unit is configured to supervise the movement of the robot, and to automatically adjust the orientation of the tool so that the tool is aligned with the specified axis upon detecting that the movement of the robot has been stopped and the alignment function is activated. Also disclosed is a method for controlling the industrial robot, and to the use of the method for teaching a robot a path including a plurality of target points by lead-through programming.