A system for monitoring a laser machining process on a workpiece is disclosed. The system includes: a neuromorphic image sensor configured to generate image data of the laser machining process, and a computing unit configured to determine input data based on the image data, and to determine output data based on the input data by means of a transfer function, the output data containing information about the laser machining process. Further, a method for monitoring a laser machining process on a workpiece is disclosed.

The present application relates to a method, device, and system for detecting welding spot quality abnormalities based on deep learning. The method includes: acquiring a dynamic welding parameter in a welding process corresponding to any target welding spot; inputting the dynamic welding parameter into a pre-trained dynamic welding parameter simulation model for simulation, and acquiring a welding simulation parameter output by the dynamic welding parameter simulation model; determining a deviation of the dynamic welding parameter from the welding simulation parameter, and determining that the target welding spot is an abnormal welding spot when the deviation is greater than a preset threshold. The solution of the present application can reduce the frequency of manual tearing down and batches for abnormality detection, which has a faster abnormality detection speed and may cover all welding spots.

A method for recognizing cutting parameters which are particularly important for specific features of a cut edge. A recording of the cut edge is analyzed by an algorithm having a neural network for determining the cutting parameters. Those recording pixels which play a significant part for ascertaining the cutting parameters are identified by backpropagation of this analysis. An output in the form of a representation of these significant recording pixels, in particular in the form of a heat map, demonstrates to a user of the method which cutting parameters need to be changed in order to improve the cut edge. A computer program product and a device for carrying out the method.

A control device that executes machining to cut a workpiece into a part and a remaining material; a detection unit that determines, as a time-series signal, a result of observing in time series a state of the workpiece; a machining state evaluation unit that determines, as evaluation information, a result of evaluation on a state of the machining for each of sections obtained by dividing the machining path, based on the time-series signal; an evaluation information storage unit that stores contour line evaluation information in which a contour line is associated with the evaluation information; and a sorting operation determination unit that determines a sorting control command for controlling sorting operation in which the part is taken out from a position where the workpiece is machined and moved to a target position, based on the contour line evaluation information; are provided.

A laser machining apparatus includes an output ratio control unit that changes an output ratio between a first laser beam and a second laser beam having different propagation characteristics; a superimposing optical system that multiplexes the first laser beam and the second laser beam; an optical fiber in which beam propagation characteristics of a combined laser beam at an exit varies depending on the output ratio, the combined laser beam being a laser beam obtained by combination of the first laser beam and the second laser beam; and a condensing optical system that performs machining of a workpiece by concentrating, on the workpiece, the beam emitted from the optical fiber.

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 machine learning device performs machine learning on a laser machine including a plurality of galvanometer mirrors for reflection of a laser beam and a plurality of galvanometer motors for driving the galvanometer mirrors to rotate, and scanning the laser beam over a workpiece. The machine learning device includes: 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 based on the at least two detected temperatures.

The present invention relates, in one aspect, to a method for process monitoring of a laser machining process for estimating a machining quality, having the following steps, which are carried out in real time during the machining process: —providing (S2) at least one captured first signal sequence with a first feature from the machining zone; —providing (S3) at least one captured second signal sequence with a second feature from the machining zone; —accessing (S4) a trained neural network with at least the recorded first and second signal sequence in order to calculate (S5) a result for estimating the machining quality.

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.

An automated welding system includes a camera for capturing a camera image of a molten pool and an arc generated in a groove by arc welding; an estimation unit for outputting a probability distribution image based on a camera image by using a learned model; an extraction unit for extracting a region having at least a predetermined probability from the probability distribution image; a selection unit for selecting a representative point corresponding to a feature point of an arc and a representative point corresponding to a feature point of a molten pool, in the region having at least the predetermined probability; and a correction unit for correcting a control parameter of a welding robot based on a positional relationship of the representative point corresponding to the feature point of the arc and the representative point corresponding to the feature point of the molten pool.