This method for acquiring weld pass information pertaining to execution conditions for a weld pass for welding two workpieces, which are to be welded by the welding robot, includes: a step in which a weld pass for welding the two workpieces is extracted from 3D CAD data; a step in which a wall determination model having a predetermined 3D shape is prepared; a step in which the wall determination model is positioned in the direction extending towards the outside of the weld pass, the end of welding which is the starting point or ending point of the weld pass serving as a reference; and a step in which, for the positioned wall determination model, a determination is made as to whether there is interference from a wall surface demarcated by another member different from the two workpieces.

A welding position is provided which can prevent surface pressure drop in a seal bead in a pair of metal gaskets is determined. Joint surfaces of a pair of metal gaskets are joined to each other by welding. The pair of metal gaskets include a seal bead that encloses opening and a plurality of structures that bulge from the joint surfaces. A pressure-sensitive medium is sandwiched between the seal bead and a member to be engaged so that the pair of joined metal gaskets and the member to be engaged are stacked on one another, and the pair of joined metal gaskets are tightly engaged with the member to be engaged. The tight engagement is released, and a surface-pressure-drop location in the seal bead is detected based on a mark which remains on the pressure-sensitive medium after the release of the tight engagement. An additional welding position is determined in a rectangular area which is defined by four sides contacting a first structure closest to the surface-pressure-drop location and a second structure closest to the first structure to surround the first structure and the second structure.

A vapor chamber having a housing that includes a first metal sheet and a second metal sheet which face each other and respective outer edges thereof are joined to each other to form a welded portion; a bead portion in a region of at least one of the first metal sheet and the second metal sheet surrounded by the welded portion in a plan view of the vapor chamber, the bead portion comprising melted and solidified metal from the at least one of the first metal sheet and the second metal sheet; a working fluid encapsulated in the housing; and a wick in or on an inner wall surface of the first metal sheet or the second metal sheet.

In electric resistance welding for energizing a workpiece formed by superimposing plural metal plates, an energization resistance reduction amount between a pair of electrodes pressurizing the workpiece is detected at a specified time interval during each welding process under a specified welding condition, frequency distribution of the energization resistance reduction amount under the welding condition is calculated on the basis of data relating to the energization resistance reduction amount, the frequency distribution is fitted with a Gaussian function, and occurrence of an expulsion under the welding condition is determined on the basis of whether the fitting is statistically significant.

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.

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 repair welding segment detection method includes generating, based on a result of the inspection determination, shape mismatch data obtained by extracting a shape mismatch portion of the weld bead, dividing the shape mismatch data into N. where N is an integer of 2 or more, equally divided windows in a direction perpendicular to a welding direction of the weld bead, setting a shift region formed by i, where i: an integer of 1 or more, continuous windows among the N windows, separately calculating volumes of (N-i+1) shift regions obtained by shifting one by one the i windows forming the shift region in the welding direction, and determining that a shift region having a volume of a predetermined value or more among the calculated volumes of the (N-i+1) respective shift regions is a defective segment of the weld bead.

A precipitation-strengthened cast product welding repair method is a method of repairing a damaged portion of a precipitation-strengthened cast product. The method includes: forming a first weld layer on a surface of the damaged portion by micro tungsten inert gas (TIG) welding using a solid-solution-strengthened welding material having higher toughness than the cast product; and forming a second weld layer on the first weld layer by building up the second weld layer by laser welding using a dual multi-phase nanostructure intermetallic compound as a welding material and being higher in heat supply rate than the micro TIG welding.

A precipitation-strengthened cast product welding repair method is a method of repairing a damaged portion of a precipitation-strengthened cast product. The method includes: forming a first weld layer on a surface of the damaged portion by micro tungsten inert gas (TIG) welding using a solid-solution-strengthened welding material having higher toughness than the cast product; and forming a second weld layer on the first weld layer by building up the second weld layer by laser welding using a dual multi-phase nanostructure intermetallic compound as a welding material and being higher in heat supply rate than the micro TIG welding.

To improve quality control of welding, there is included in resistance welding: a magnetic field measuring unit (205) disposed around a welded part and configured to measure a local current at the welded part; a high-speed camera (202) configured to capture an image for measuring local temperature at the welded part from variation of luminance of emission by capturing light emission state of the welded part; a comparison determination unit (106) configured to determine whether or not at least one of current information and temperature information has an abnormal value by comparing the current information calculated based on magnetic field information acquired from the magnetic field measuring unit with past current information and comparing the temperature information measured from an image of the high-speed camera (202) with past temperature information.