This welding system comprises: a welding device, various different types of a plurality of sensors which detect an event according to welding performed by a welding device; and an estimation unit which uses a trained model that is pre-generated by machine-learning by taking, as input data, a plurality of pieces of data for learning obtained by detecting events according to welding by means of the same types of sensors as the plurality of sensors, and, as training data, labels representing whether the welding is normal or abnormal, thereby estimating an abnormality of the welding performed by the welding device from a plurality of pieces of detection data generated by the plurality of sensors.

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.

An apparatus for generating arc image-based welding quality inspection model is disclosed. The apparatus includes: a hall sensor for measuring a welding current flowing in a base metal through an arc welding machine; a voltage meter for measuring a welding voltage through a circuit generated between the arc welding machine and the base metal; a camera for capturing an image of a welding target area on which the arc welding machine performs welding; and a model generator configured to: identify a welding state based on the welding current measured using the hall sensor and the welding voltage measured using the voltage meter; obtain an arc image based on the image captured; associate the obtained arc image with a welding quality identified based on the arc image to generate a dataset; and apply the generated dataset to a deep-learning model to generate an arc image-based welding quality inspection model.

Welder's shield with AI includes a frame for mounting on a user's head; a camera mounted on the frame and directed forward toward welding zone, to obtain images of welding zone as video data; a display on an inner side of the frame; an optical device to enable the user to see the display; battery; processor receiving a video data from the camera; the processor running an AI application to process the video data; the processor displaying images on the display based on output of the AI application; the artificial intelligence application receiving the video data and detecting a welding arc in the welding zone using a pattern recognition algorithm; and the AI application modifying the video data to reduce an intensity of the welding arc in the images that are to be displayed on the display, without reducing an intensity of the rest of the images.

A method of inspecting an electric motor includes scanning an electric motor stator winding with a 2D or 3D camera, acquiring one or more images of a plurality welds between adjacent electrical wires forming the stator winding using the 2D camera, analyzing the one or more acquired images with at least one neural network such that the neural network determines if at least one of the plurality of welds has a weld defect. The at least one neural network is trained and distinguishes between surface discoloration on a surface of the welds and defect discoloration resulting from contamination during welding. Also, the method inspects over 150 welds per electric motor stator winding moving along an assembly line.

A repair welding control device includes a processor. The processor is configured to acquire repair portion information indicating a welded portion where repair welding is performed among welded portions in a workpiece welded by a first welding program, and generate a second welding program by correcting the first welding program based on the repair portion information.

Systems and methods for detecting a material composition of a specimen and for cross-sectioning of the specimen. The system includes an imaging system, a femtosecond laser source, and optionally, a synchronized CO.sub.2 injection system. The imaging system is configured to capture image data of a surface of the specimen that has been etched by the laser. A machine learning model is applied to determine a predicted material composition of the specimen based at least in part on the image data. The machine learning model is trained to receive as input the image data and/or one or more quantified surface texture parameters determined from the image data and to produce as output an indication of a predicted material composition. A laser-based milling system is configured to use these material composition detection mechanisms to automatically determine when the laser system has milled through a first layer of a specimen and reached a second layer, and to adjust the operation of the milling system in response. The CO2 injection system can be used to provide fast, clean, high aspect ratio cross-sectioning of microelectronic parts for providing high-precision and high-throughput machining for material removal (e.g., for intrusive inspection of electronic components).

An ultrasonic welding diagnostic method includes: applying a pressing force to an object to be joined so as to generate a surface pressure on a joint surface of the object to be joined; inputting ultrasonic waves to the joint surface; detecting an elastic wave propagating through the object to be joined by at least one sensor at a plurality of different positions; analyzing a signal detected by the sensor to generate an envelope of the signal and calculating information about the envelope; and determining a joint state on the joint surface based on the calculation result of the information.

A data generation device includes a large-scale data acquisition unit that obtains large-scale data that is large-scale learning data used in learning of a first determination model for determining a machining state of a workpiece machined by a first machine tool; an adaptive data acquisition unit that obtains adaptive data for use in generation of learning data for use in learning of a second determination model for determining a machining state of a workpiece machined by a second machine tool; and a learning data generation unit that converts the large-scale data based on the adaptive data to generate adapted large-scale data for use in learning of the second determination model.

Systems and methods for real time feedback and for updating welding instructions for a welding robot in real time is described herein. The data of a workspace that includes a part to be welded can be received via at least one sensor. This data can be transformed into a point cloud data representing a three-dimensional surface of the part. A desired state indicative of a desired position of at least a portion of the welding robot with respect to the part can be identified. An estimated state indicative of an estimated position of at least the portion of the welding robot with respect to the part can be compared to the desired state. The welding instructions can be updated based on the comparison.