System and methods for using analytics and algorithms to predict weld quality are provided and include a computer having a processor and memory configured to receive weld parameter data generated during a welding process by a welder to join at least two parts with a weld, input the received weld parameter data to a data analytics model to generate at least one predicted weld quality parameter, compare the predicted weld quality parameter with a weld quality parameter threshold, and generate output indicating at least one of: the at least one predicted weld quality parameter and a result of the comparison between the at least one predicted weld quality parameter and the weld quality parameter threshold

An automatic welding system, an automatic welding method, a welding assistance device, and a program wherein the amount of correction of the welding speed is determined on the basis of the distance between an arc and the tip of a molten pool when the distance between the arc and the tip of the molten pool is within a predetermined range in arc welding performed while alternately weaving a welding torch in the front downward direction and the rear upward direction when the welding progress direction is the frontward direction with respect to a horizontally extending groove formed between two members to be welded aligned in the vertical direction.

A method for process monitoring of a laser machining process for estimating a machining quality is dicloses. The method may include steps, which are carried out in real time during the machining process of providing at least one captured first signal sequence with a first feature form a machining zone, providing at least one captured second signal sequence with a second feature from the machining zone, and accessing a trained neural network with at least the recorded first and second signal sequences in order to calculate a result for estimating the machining quality.

A system and a method is for automating welding processes, in particular welding processes in the heavy industries. One embodiment regards a computer implemented method for automatic detection and/or planning of a welding task in a welding environment, the method including the steps of: obtaining scanning data from a scan of the welding environment, detecting welding object(s) in the scanning data by means of artificial intelligence employing a machine learning algorithm, wherein the machine learning algorithm has been trained on real and simulated 3D data of known welding objects, determining the pose of each detected welding object, and optionally generating a welding path for each detected welding object.

A method for analyzing a laser machining process for machining workpieces includes the steps of acquiring at least one sensor data set for the laser machining process and determining a value of at least one physical property of a machining result of the laser machining process based on the at least one sensor data set using a transfer function. The transfer function is formed by a trained neural network. A system for analyzing a laser machining process and a laser machining system including such a system are also disclosed.

The invention relates to a method for determining the performance of a welding method carried out on a metal workpiece, in particular an electric arc welding or laser welding method, with the following steps: introducing one or more extracts of the initial image each having at least one presumed projection, as input to at least one neural network, in particular a convolutional neural network, so as to classify the presumed projections as confirmed or unconfirmed projections, carrying out a second digital processing operation on the initial image comprising the previously classified projections so as to determine at least one parameter representative of the quantity of confirmed projections chosen from the surface of one or more projections.

A computer-implemented method for operating a laser material processing machine. Process parameters are varied with the aid of Bayesian optimization until a result of the manufacturing, in particular the laser material processing, is sufficiently good. The Bayesian optimization is carried out with the aid of a data-based process model in a first phase, the data-based process model being trained as a function of estimated results. In a second phase, the data-based process model is trained as a function of the ascertained result resulting upon activation of the laser material processing machine.

A computer-implemented method for operating a laser material processing machine. Process parameters are varied with the aid of Bayesian optimization until a result of the laser material processing is sufficiently good. The Bayesian optimization taking place with the aid of a data-based process model, and it being taken into consideration during the variation of the process parameters how probable it is that a variable which characterizes a quality of the result is within predefinable boundaries.

Systems and methods for missing weld identification using machine learning techniques are described. In some examples, a part tracking system uses machine learning techniques to identify whether an operator has missed one or more welds when assembling a part. The part tracking system may additionally identify which specific welds were missed (e.g., the first weld, the third weld, the fifteenth weld, etc.). The part tracking system may be able to identify missing welds after a part has been completed, or in real-time, during assembly of the part. Identification of the particular weld(s) missed during the welding process can help an operator quickly assess and resolve any issues with the part being assembled, saving time and ensuring quality

A welding system includes a welder configured to weld a workpiece at welding points on the workpiece, data acquisition circuitry configured to acquire welding data indicating welding quality at the welding points, and quality evaluation circuitry configured to evaluate the welding quality at each of the welding points based on the welding data according to a determination algorithm which is associated with each of the welding points.