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 method for optimizing a welding process to produce a weld joint having a predetermined strength includes measuring a plurality of melt layer thicknesses of weld joints for a plurality of sample assemblies formed by the welding process, measuring a plurality failure loads of weld joints for the plurality of sample assemblies, each of the measured plurality of failures loads being associated with one of the measured plurality of melt layer thicknesses, selecting a first failure load from the plurality of measured failure loads responsive to determining that the first failure load corresponds to a predetermined weld strength, and selecting a first melt layer thickness from the plurality of measured melt layer thicknesses that is associated with the selected first measured failure load.

A weld system includes: a robot control module configured to actuate a robot and move a welder along a joint of metal workpieces during welding, the welder being attached to the robot; a weld control module configured to, during the welding, apply power to the welder, supply a shield gas, and supply electrode material; a vision sensor configured to, during the welding, optically measure distances between the vision sensor and locations, respectively, on an outer surface of a weld bead created along the joint by the welder; and a weld module configured to: determine a strength of the weld bead at a location based on: the distances at the location along the joint; and at least one parameter from at least one of the robot control module during the welding, the weld control module during the welding, and a sensor configured to capture data of the welding during the welding.

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.

A welding system includes one or more sensing devices used to detect and/or determine a position and/or orientation of a welding tool (e.g., a welding torch). The welding tool may be tracked to determine (and/or calibrate) a shape of a welding joint and/or a path of the welding joint. The welding system may determine a virtual line representative of the welding joint. The welding tool may be tracked (and/or the virtual line used) to determine (and/or provide feedback relating to) various welding parameters, including at least a work angle of the welding tool, and/or a travel angle of the welding tool.

An opposing electrode determination method includes a step of inserting a pair of opposing electrodes into corresponding receiving portions, respectively, by holding, between the pair of opposing electrodes, a held member formed with the receiving portions on both sides; a step of measuring the inter-electrode distance between the pair of opposing electrodes; and a step of determining, based on the inter-electrode distance, whether a combination of the pair of opposing electrodes is correct or incorrect. The receiving portions are configured to have different insertion allowances for the opposing electrodes, respectively, according to the tip shape of each of the pair of opposing electrodes.

This disclosure describes various methods and apparatus for characterizing an additive manufacturing process. A method for characterizing the additive manufacturing process can include generating scans of an energy source across a build plane; measuring an amount of energy radiated from the build plane during each of the scans using an optical sensing system that monitors two discrete wavelengths associated with a blackbody radiation curve of the layer of powder; determining temperature variations for an area of the build plane traversed by the scans based upon a ratio of sensor readings taken at the two discrete wavelengths; determining that the temperature variations are outside a threshold range of values; and thereafter, adjusting subsequent scans of the energy source across or proximate the area of the build plane.

A laser welding device includes: a laser oscillator to which an incidence end of a fiber is connectable; a welding head connectable to an emission end of the fiber and configured to perform laser welding while condensing laser light emitted from the laser oscillator via the emission end and irradiating a workpiece with the laser light; a detector configured to detect presence or absence of a defect in the laser welding; and a controller including a processor and a memory storing instructions that, when executed by the processor, cause the laser welding device to perform operations. The operations include: performing, in response to receiving, from the detector, an output signal indicating a defect at a welding point on the workpiece during the laser welding of the workpiece, control such that supplementary laser welding is performed at a predetermined position in a vicinity of the welding point.

A method for monitoring an attachment area during laser welding of bent bar-type conductors containing copper, includes the steps of arranging a first bar-type conductor relative to a second bar-type conductor in partially overlapping fashion and welding the first and second bar-type conductors to one another using a processing laser beam, the welding including forming a weld bead interconnecting the bar-type conductors to one another. After the welding, at least one measurement variable is measured on at least one portion of the weld bead, wherein the at least one measurement variable changes with the temperature of the weld bead as a function of the time during a cooling down of the weld bead. A parameter depending on a heat capacity of the weld bead is determined from the at least one measured measurement variable, and the attachment area qualitatively or quantitatively determined from the parameter.