WELDING DATA PROCESSING DEVICE AND WELDING DATA PROCESSING METHOD

Provided is a welding data processing device that enables effective use of findings derived from a welding execution record and a result of judgment of whether there is a weld defect. A welding data processing device includes a welding process registration module which registers, for each weld point, a welding condition and a welding execution record in a welding process performed under the welding condition in a welding process database, an inspection process registration module which registers, for each weld point, defect judgment data indicating a result of judgment of whether a weld defect exists at the weld point based on image data of the weld point acquired in a radiographic test process in an inspection process database, and an information processing module which performs predetermined processing based on information registered in the welding process database and the inspection process database.

TECHNICAL FIELD

The present invention relates to a welding data processing device and a welding data processing method.

BACKGROUND ART

Hitherto, a welding state during a welding operation has been monitored and recorded as a welding execution record in a welding machine for the purpose of quality control and traceability of a welding process. For example, in Patent Literature 1, there is disclosed a welding machine that detects a welding state, such as a current, a voltage, and a weld time, by a welding state detecting device and records the detected welding state as welding information (a welding execution record) in a recording medium during a welding operation.

CITATION LIST

Patent Literature

SUMMARY OF INVENTION

Technical Problem

In a construction site for a plant facility, a ship, or the like, a plurality of weld points are provided. After a welding process using a welding machine is performed, a radiographic test process is performed. In the radiographic test process, it is judged whether there is a weld defect at each weld point based on image data obtained by applying radiation to the weld point.

However, the welding execution record acquired as the welding information in the welding machine as disclosed in Patent Literature 1 is not associated with defect judgment data showing the result of judgment of whether there is a weld defect in the radiographic test process, and the welding execution record and the defect judgment data are not managed in an integrated manner. Therefore, findings derived by performing various types of processing, such as analysis, prediction, and machine learning, through use of both the welding execution record and the defect judgment data for each weld point have not been used effectively.

The present invention has been made in view of the above-mentioned problem, and it is an object of the present invention to provide a welding data processing device and a welding data processing method that enable effective use of findings derived from a welding execution record and a result of judgment of whether there is a weld defect.

Solution to Problem

In order to achieve the above-mentioned object, according to one aspect of the present invention, there is provided a welding data processing device including: a welding process registration module configured to register, for each weld point, a welding condition and a welding execution record in a welding process performed under the welding condition in a welding process database; an inspection process registration module configured to register, for the each weld point, defect judgment data in an inspection process database, the defect judgment data indicating a result of judgment of whether a weld defect exists at the each weld point based on image data of the each weld point acquired in a radiographic test process; and an information processing module configured to perform predetermined processing based on information registered in the welding process database and the inspection process database.

Advantageous Effects of Invention

According to the welding data processing device of the one aspect of the present invention, the information processing module performs the predetermined processing based on the welding execution record registered in the welding process database and the defect judgment data registered in the inspection process database. Therefore, findings derived from the welding execution record and the defect judgment data can be used effectively.

Problems, configurations, and effects other than those described above become apparent in the “Description of Embodiments” section described later.

DESCRIPTION OF EMBODIMENTS

Embodiments for carrying out the present invention are described below with reference to the drawings. In the following description, a range required for the description for achieving the object of the present invention is schematically shown, and a range required for the description of a portion corresponding to the present invention is mainly described. A portion of which the description is omitted is based on a known technology.

FIG.1is an overall view of an example of a welding integrated management system1.FIG.2is a spool diagram for showing an example of weld points11provided in a welding site10. The welding integrated management system1is a system for managing a welding process and a radiographic test process performed at each of the weld points11provided at various locations in the welding site10in an integrated manner.

The welding integrated management system1includes, as main components, a welding data processing device2, a construction progress management device3, a manager's terminal4, a welder's terminal5A, a welding manager's terminal5B, an imaging worker's terminal5C, a judge's terminal5D, and an inspection manager's terminal5E. In addition, the welding integrated management system1includes a welding machine6, a welding machine monitoring device60, a temperature measurement device61, and a distribution board62as devices used in the welding process and also includes an inspection device7as a device used in the radiographic test process. AlthoughFIG.1shows only one of each device of the welding integrated management system1for the sake of simplicity of the drawing, the number and arrangement of each device are not limited to those ofFIG.1.

The above-mentioned devices of the welding integrated management system1are connected to a wired or wireless network8and configured to be able to transmit and receive various types of data mutually. The form of the network8is not limited to the example ofFIG.1, and may be changed appropriately. For example, a plurality of sub-networks independent of each other, such as a sub-network formed by the welding data processing devices2and a sub-network formed by the construction progress management devices3, may be connected to an integrated network formed by the manager's terminals4, and thus the network8is formed by those devices as a whole.

Examples of the welding site10include plant facilities such as a natural gas plant, a petroleum processing plant, a chemical processing plant, a power plant, and a steel plant, and a ship construction site at which a tanker, a cargo ship, a passenger ship, or the like is constructed. In the welding site10, a welder50A, a welding manager50B, an imaging worker50C, a judge50D, and an inspection manager50E (hereinafter referred to as “workers50A to50E”), who belong to subcontractors undertaking a welding process and a radiographic test process, perform various works associated with the welding process and the radiographic test process under process management by a site manager40of a prime contractor. Therefore, the welding integrated management system1is used by, for example, the prime contractor to manage the welding process and the radiographic test process performed by the subcontractors.

The welding site10is divided into a plurality of welding areas and managed by welding area. For example, the welding site10is managed by dividing the above-mentioned building into a plurality of welding areas in accordance with a division criterion such as hierarchy, section, and purpose of use. The welding site10is provided not only when the above-mentioned building is newly constructed but also when the building is renovated. The welding site10is not limited to the above-mentioned examples of building, and may be any building as long as the weld points11are provided at various locations therein.

The weld point11is represented in a spool diagram drawn as a part of design drawings of a plant, a ship, or the like that is the welding site10, as illustrated inFIG.2. The weld point11is a point in the welding site10, at which pipings allowing any fluid to flow therein are welded to each other, a piping and a piping joint member (for example, a flange, an elbow, or a tee) are welded to each other, or piping joint members are welded to each other. The weld point11is not limited to the above-mentioned examples, as long as the weld point11is a point at which members are welded to each other.

The welding process is a work process performed by the welder50A using the welding machine6under a welding condition determined for each weld point11. In this embodiment, the welding process is described focusing on the case of using arc welding. However, any welding method, for example, gas welding, laser welding, electron beam welding, and resistance pressure welding may be used.

As the welding condition in the welding process, for example, diameter, thickness, material (base material), joint shape, welding material, preheating, post-weld heat treatment, shielding gas, electrical characteristics (welding current, welding voltage, and the like), and welding method (welding speed, welding direction, welding angle, arc time) for the weld point11are determined. The welding condition is determined for each weld point11by, for example, operation instructions for the welding process, and information as a part of the welding condition is represented in a spool diagram (seeFIG.2). In addition, welding machine operation data in which the operating state of the welding machine6is recorded and preheating data in which the preheating state of the weld point11is recorded, for example, are acquired as a welding execution record in the welding process that has been performed under the welding condition.

The radiographic test process includes an imaging process in which the imaging worker50C applies radiation (X-ray, y-ray, or the like) to the weld point11for which the welding process has been performed through use of the inspection machine7to acquire the intensity of radiation transmitted through the weld point11as image data, and an image judgment process in which the judge50D judges whether there is a weld defect at that weld point11based on the image data through use of the judge's terminal5D. Weld defects are classified into defect types such as a blowhole, a pit, a crack, an undercut, and an overlap. When the size, the depth, or the shape of a weld defect exceeds an allowable range, the weld point11is judged to be “defective” (fail), and when the size, the depth, or the shape of a weld defect does not exceed the allowable range, the weld point11is determined to be “non-defective” (pass). In the image judgment process, defect judgment data which indicates the result of the judgment of whether there is a weld defect based on the image data of the weld point11is acquired.

The welding data processing device2is configured by, for example, a general-purpose or dedicated computer (seeFIG.8described later). The welding data processing device2manages various types of data acquired in the welding process and the radiographic test process. The details of the welding data processing device2are described later.

The construction progress management device3is configured by, for example, a general-purpose or dedicated computer (seeFIG.8described later). The construction progress management device3manages design data that includes, for example, design drawings, spool diagrams, and operation instructions of a building as the welding site10and progress management data that includes work schedules and the state of progress of an entire construction process including the welding process and the radiographic test process. The design data and the progress management data are displayed on display screens of the manager's terminal4and worker's terminals5A to5E. Further, the site manager40and the workers50A to50E are notified based on the progress management data via the manager's terminal4and the worker's terminals5A to5E, respectively.

The construction progress management device3is connected to beacon receiving devices30installed at various locations in the welding site10. The beacon receiving device30receives a beacon signal transmitted from a beacon card31, to thereby acquire card information included in the beacon signal and acquire the location of the beacon card31that is a transmission source of that beacon signal.

The beacon card31is held by each of the workers50A to50E. The beacon receiving device30regularly acquires the card information (for example, identification information (ID) for identifying the workers50A to50E) and the current location of the beacon card31held by each of the workers50A to50E, and thus the construction progress management device3acquires location history data of each of the workers50A to50E. Further, the beacon card31is attached to each of the welding machine6and the inspection machine7. The beacon receiving device30regularly acquires the card information (for example, identification information (ID) for identifying the welding machine6and the inspection machine7) and the current location of the beacon card31attached to each of the welding machine6and the inspection machine7, and thus the construction progress management device3acquires location history data of each of the welding machine6and the inspection machine7.

The manager's terminal4is configured by, for example, a general-purpose or dedicated computer (seeFIG.8described later) and is used by the site manager40. The manager's terminal4includes a card reader41that reads the card information of the beacon card31. Further, the manager's terminal4receives various input operations via its input screen and displays various types of information via a display screen of an application, a browser, or the like.

The welder's terminal5A, the welding manager's terminal5B, the imaging worker's terminal5C, the judge's terminal5D, and the inspection manager's terminal5E (hereinafter referred to as “worker's terminal5A to5E”) are each configured by, for example, a general-purpose or dedicated computer (seeFIG.8described later) and are used by the workers50A to50E, respectively. The worker's terminals5A to5E each include a card reader51that reads the card information of the beacon card31. Further, each of the worker's terminal5A to5E receives various input operations via its input screen and displays various types of information via a display screen of an application, a browser, or the like, as with the manager's terminal4.

The welder's terminal5A, the welding manager's terminal5B, the imaging worker's terminal5C, and the inspection manager's terminal5E are used when the welder50A, the welding manager50B, the imaging worker50C, and the inspection manager50E move to each weld point11and perform work there. Therefore, those terminals are preferably configured by portable computers. The judge's terminal5D receives image data acquired in the imaging process in the radiographic test process, for example, via the network8(including the Internet) and displays an image, thereby being used when the judge50D performs work at a location different from the weld point11. Therefore, the judge's terminal5D may be configured by any of a portable computer and a stationary computer.

FIG.3is a configuration diagram of an example of the welding machine6. The welding machine6includes a welding torch63to be grasped by the welder50A, a power supply circuit64that receives electric power supplied from the distribution board62and outputs a predetermined welding current to the welding torch63, and an operation control panel65that receives various input operations (for example, a setting operation of setting the electrical characteristics in the welding condition) and controls each part of the welding machine6. The welding torch63includes a discharge electrode630, a nozzle631that supplies shielding gas from a gas tank66, and an on-off switch632.

For example, the welder50A causes the welder's terminal5A to display a spool diagram and/or operation instructions for the weld point11as a work target to confirm the welding condition for the weld point11, and sets the welding condition for that weld point11on the operation control panel65. Then, when the welder50A brings the welding torch63close to the weld point11and turns on the switch632, an arc is generated between the welding torch63to which a predetermined welding voltage is applied by the power supply circuit64and the weld point11, and thus welding of the weld point11is executed.

The welding machine monitoring device60is connected to the power supply circuit64and generates welding machine operation data12in which the operating state of the welding machine6is recorded in accordance with execution of welding. For example, in the welding machine operation data12, time-series data of a welding current, a welding voltage, a welding speed, a welding direction, and a welding angle, the average value, the maximum value, and the minimum value for each of those values, and an arc time are recorded as detailed data representing the operating state of the welding machine6.

The temperature measurement device61is configured by, for example, a non-contact thermometer and generates preheating data13in which the preheating state of the weld point11is recorded before welding is performed. For example, in the preheating data13, time-series data of a surface temperature of a piping, the average value, the lowest temperature, and the highest temperature of a preheating temperature, and a preheating time are recorded as detailed data representing the preheating state of the weld point11.

The distribution board62includes a switch620to which a power line for supplying electric power to the welding machine6is connected. When receiving a switch control instruction from the welding data processing device2, the distribution board62switches the switch620from an energized state to a cut-off state.

FIG.4is a configuration diagram of an example of the inspection machine7. The inspection machine7includes a radiation irradiation unit70that applies radiation to the weld point11, a detector71arranged to be opposed to the radiation irradiation unit70, a guide72mounted to the outer circumference of the weld point11to be detachable, a driving unit73that moves the radiation irradiation unit70and the detector71along the guide72in the circumferential direction, and an operation control panel74that receives various input operations and controls each part of the inspection machine7. The detector71is configured by, for example, a flat panel detector (FPD). The above-mentioned configuration of the inspection machine7is merely an example. For example, the inspection machine7may include the detector71configured by a computed radiography (CR).

For example, when the imaging worker50C mounts the guide72to the weld point11as a work target and performs an imaging start operation on the operation control panel74, the operation control panel74moves the radiation irradiation unit70and the detector71along the guide72in the circumferential direction by the driving unit73and causes the radiation irradiation unit70and the detector71to operate. Accordingly, the radiation irradiation unit70applies radiation to the weld point11, and the detector71detects the intensity distribution of radiation transmitted through the weld point11, and thus the operation control panel74generates image data14in which the weld point11is recorded over the entire circumference.

(Configuration of Welding Data Processing Device2)

FIG.5is a block diagram of an example of the welding data processing device2. The welding data processing device2includes a control unit20configured by a processor or the like, a memory unit21configured by a hard disk drive (HDD), a solid state drive (SSD), a memory, or the like, a communication unit22that is an interface for communication to and from the network8, an input unit23configured by a keyboard and/or a mouse, for example, and a display unit24configured by a display or the like. The input unit23and the display unit24may be omitted.

The memory unit21stores therein a welding data processing program210, a welding process database211, and an inspection process database212.

FIG.6is a data structure diagram of an example of the welding process database211. The welding process database211includes a weld point table211A, a welding machine table211B, a welder table211C, and a welding manager table211D.

In the weld point table211A, welding condition, required qualification, and welding execution record are registered for each weld point11identified by a welding site ID, a welding area ID, a drawing number, and a weld point number. The welding condition includes, as its fields, diameter, thickness, material (base material), joint shape, welding material, preheating, post-weld heat treatment, shielding gas, welding current, welding voltage, welding speed, welding direction, welding angle, and arc time. The welding execution record includes, as its fields, execution start date and time, execution end date and time, executing welder ID, executing welding machine ID, welder qualification judgment data, preheating data13, welding machine operation data12, preheating deviation judgment data, and welding deviation judgment data.

In the welding machine table211B, manufacturer name, model name, and specification are registered for each welding machine6identified by a welding machine ID. In the welder table211C, department ID, name, holding qualification, and welding manager ID are registered for each welder50A identified by a welder ID. In the welding manager table211D, department ID, name, and holding qualification are registered for each welding manager50B identified by a welding manager ID.

FIG.7is a data structure diagram of an example of the inspection process database212. The inspection process database212includes a weld point table212A, an inspection machine table212B, an imaging worker table212C, a judge table212D, and an inspection manager table212E.

In the weld point table212A, imaging worker required qualification, judge required qualification, inspection execution record, defect prediction data, and defect inference data are registered for each weld point11identified by a welding site ID, a welding area ID, a drawing number, and a weld point number. The inspection execution record includes, as its fields, imaging date and time, executing imaging worker ID, executing inspection machine ID, imaging worker qualification judgment data, image data14, image judgment date and time, executing judge ID, judge qualification judgment data, and defect judgment data.

The image data14includes one or a plurality of images for one weld point11. The defect judgment data includes the presence or absence of a weld defect (“defective” or “non-defective”) and further includes a defect detail status indicating a detailed status of the defect in the case of “defective.” The defect detail status includes, as its fields, defect location indicating the location on the image data14, defect type, size, and depth, for example. In the case in which a plurality of defects are captured in one image data14, the defect judgment data includes the defect detail status for each defect.

In the inspection machine table212B, manufacturer name, model name, and specification are registered for each inspection machine7identified by an inspection machine ID. In the imaging worker table212C, department ID, name, holding qualification, and inspection manager ID are registered for each imaging worker50C identified by an imaging worker ID. In the judge table212D, department ID, name, holding qualification, and inspection manager ID are registered for each judge50D identified by a judge ID. In the inspection manager table212E, department ID, name, and holding qualification are registered for each inspection manager50E identified by an inspection manager ID.

The data structures of the welding process database211and the inspection process database212are not limited to the above-mentioned example, and may be changed as appropriate. For example, a part of the above-mentioned fields may be omitted or a field other than the above-mentioned fields may be added. Further, the welding process database211and the inspection process database212may be stored in an external database management device in place of the memory unit21of the welding data processing device2. In this case, it suffices that the welding data processing device2accesses the database management device via the communication unit22.

The control unit20executes the welding data processing program210stored in the memory unit21, to thereby function as a welding process registration module200, an inspection process registration module201, an information processing module202, a welding process support module203, an inspection process support module204, and an output processing module205, as illustrated inFIG.5.

The welding process registration module200registers a welding condition and a welding execution record in a welding process performed under the welding condition in the welding process database211for each weld point11, as its basic function.

The inspection process registration module201registers, for each weld point11, an inspection execution record that includes at least defect judgment data indicating a result of judgment of whether there is a weld defect at the weld point11based on image data14of the weld point11acquired in a radiographic test process, in the inspection process database212, as its basic function.

The information processing module202performs predetermined processing based on information registered in the welding process database211and the inspection process database212. The information processing module202includes, as components which perform the predetermined processing, a qualification judgment module202A, a deviation judgment module202B, a defect analysis module202C, a qualification ineligibility analysis module202D, a deviation analysis module202E, a deviation judgment criterion setting module202F, a welding assist module202G, a defect prediction module202H, a machine learning module2021, and a defect inference module202J. The details of each of the modules202A to202J of the information processing module202are described later.

The welding process support module203performs various types of processing for supporting execution of the welding process in cooperation with the information processing module202. The inspection process support module204performs various types of processing for supporting execution of the inspection process in cooperation with the information processing module202.

The output processing module205generates display data for causing a result of processing by the information processing module202to be displayed on, for example, a display screen of an application or a browser running on the manager's terminal4and the worker's terminals5A to5E, and transmits (outputs) the generated display data to the manager's terminal4and the worker's terminals5A to5E. The output processing module205may generate print data for causing the result of processing by the information processing module202to be printed or output by a printer or the like, or may store the processing result in the memory unit21.

The functions of the respective units included in the control unit20may be distributed into a plurality of devices and implemented by those devices, instead of being implemented by a single device (the welding data processing device2in this embodiment).

FIG.8is a hardware configuration diagram of an example of a computer900configuring each device of the welding integrated management system1.

Each of the welding data processing device2, the construction progress management device3, the manager's terminal4, the worker's terminals5A to5E, the operation control panel65of the welding machine6, and the operation control panel74of the inspection machine7is configured by the general-purpose or dedicated computer900. The computer900includes, as its main components, a bus910, a processor912, a memory914, an input device916, an output device917, a display device918, a storage device920, a communication interface (I/F) unit922, an external device I/F unit924, an input/output (I/O) device I/F unit926, and a media input/output unit928, as illustrated inFIG.8. The above-mentioned components may be omitted as appropriate depending on the application in which the computer900is used.

The processor912is configured by one or a plurality of arithmetic processing devices (central processing unit (CPU), micro-processing unit (MPU), digital signal processor (DSP), graphics processing unit (GPU), and the like) and operates as a control unit that controls the overall computer900. The memory914stores therein various types of data and a program930, and is configured by, for example, a volatile memory (such as a DRAM and an SRAM) serving as a main memory, a non-volatile memory (a ROM), a flash memory, and the like.

The input device916is configured by, for example, a keyboard, a mouse, a numeric keypad, and/or an electronic pen and serves as an input unit. The output device917is configured by, for example, a sound (audio) output device or a vibration device and serves as an output unit. The display device918is configured by, for example, a liquid crystal display, an organic EL display, an electronic paper display, or a projector and serves as an output unit. The input device916and the display device918may be configured integrally, like a touch panel display. The storage device920is configured by, for example, an HDD and/or an SSD and serves as a memory unit. The storage device920stores therein various types of data required for execution of an operating system and the program930.

The communication I/F unit922is connected to a network940, such as the Internet and an intranet, (which may be the same as the network8ofFIG.1) in a wired or wireless manner and serves as a communication unit that transmits and receives data to and from another computer in accordance with a predetermined communication standard. The external device I/F unit924is connected to an external device950, such as a camera, a printer, a scanner, and a reader/writer, in a wired or wireless manner and serves as a communication unit that transmits and receives data to and from the external device950in accordance with a predetermined communication standard. The I/O device I/F unit926is connected to an I/O device960, such as various sensors and an actuator, and serves as a communication unit that transmits and receives various signals, such as a detection signal of a sensor and a control signal to an actuator, and data to and from the I/O device960. The media input/output unit928is configured by a drive device, such as a digital versatile disc (DVD) drive and a compact disc (CD) drive, and writes and reads data to and from a medium (a non-transitory storage medium)970, such as a DVD and a CD.

In the computer900having the above-mentioned configuration, the processor912calls the program930stored in the storage device920, executes the program930in the memory914, and controls each part of the computer900via the bus910. The program930may be stored in the memory914in place of the storage device920. The program930may be recorded in the medium970in an installable file format or an executable file format and be provided to the computer900via the media input/output unit928. The program930may be provided to the computer900by being downloaded through the network940via the communication I/F unit922. Moreover, the computer900may be configured in such a manner that various functions implemented by execution of the program930by the processor912are implemented by hardware, such as a field-programmable gate array (FPGA) and an application specific integrated circuit (ASIC).

The computer900is configured by, for example, a stationary computer or a portable computer, and is an electronic device of any form. The computer900may be a client computer, a server computer, or a cloud computer.

Each module of the welding data processing device2(each step of a welding data processing method executed by the welding data processing method) and a series of operations by the welding integrated management system1are described below with reference toFIG.9toFIG.25.

(1) Advance Registration in Welding Process Database and Inspection Process Database

The welding process registration module200and the inspection process registration module201perform, before a welding process and an inspection process are performed, advance registration processing of registering in advance various types of information (except for a welding execution record and an inspection execution record) in the welding process database211and the inspection process database212.

First, the site manager40operates the manager's terminal4to provide design data managed by the construction progress management device3to the welding data processing device2. The welding process registration module200adds a plurality of records corresponding to each of a plurality of weld points11provided in the welding site10to the weld point table211A of the welding process database211, based on the design data provided from the construction progress management device3.

At this time, the welding process registration module200receives, for example, a welding site ID determined based on the name of a building or the like from the manager's terminal4operated by the site manager40, to thereby register the common welding site ID for each weld point11. The welding process registration module200receives welding area IDs respectively assigned to a plurality of welding areas into which the welding site10is divided in accordance with a predetermined dividing criterion, to thereby register for each weld point11a welding area ID for identifying a welding area including the weld point11. The welding process registration module200takes in a drawing number and a weld point number assigned to each weld point11from, for example, a spool diagram of the design data and also takes in a welding condition for each weld point11from the spool diagram of the design data and operation instructions. The welding process registration module200thus registers the drawing number, the weld point number, and the welding condition for each weld point11. The welding process registration module200then identifies a required qualification required of the welder50A based on the welding condition for each weld point11, to thereby register the required qualification required of the welder50A for each weld point11.

The welding process registration module200also receives information on the welding machine6to be used in the welding site10from the manager's terminal4, to thereby assign a welding machine ID for identifying the welding machine6to each welding machine6and register manufacturer name, model name, and specification for each welding machine6in the welding machine table211B.

Further, the welding process registration module200receives information on the welder50A who is to work in the welding site10from the manager's terminal4, to thereby assign a welder ID for identifying the welder50A to each welder50A and register for each welder50A a department ID for identifying a department (for example, a sub-contractor or a team) to which the welder50A belongs, a name, a holding qualification, and a welding manager ID for identifying the welding manager50B managing the welder50A in the welder table211C. The welding process registration module200receives information on the welding manager50B managing the welder50A who is to work in the welding site10from the manager's terminal4, to thereby assign a welding manager ID for identifying the welding manager50B to each welding manager50B and register for each welding manager50B a department ID for identifying a department to which the welding manager50B belongs, a name, and a holding qualification in the welding manager table211D.

The inspection process registration module201adds a plurality of records corresponding to the respective weld points11provided in the welding site10to the weld point table212A of the inspection process database212, based on the design data provided from the construction progress management device3, as with the welding process registration module200. That is, the inspection registration module201registers welding site ID, welding area ID, drawing number, and weld point number for each weld point11. The inspection process registration module201then identifies a required qualification required of each of the imaging worker50C and the judge50D based on the welding condition for each weld point11, to thereby register an imaging worker required qualification required of the imaging worker50C and a judge required qualification required of the judge50D for each weld point11.

The inspection process registration module201receives information on the inspection machine7to be used in the welding site10from the manager's terminal4, to thereby assign an inspection machine ID for identifying the inspection machine7to each inspection machine7and register manufacturer name, model name, and specification in the inspection machine table212B for each inspection machine7.

Further, the inspection process registration module201receives information on the imaging worker50C who is to work in the welding site10from the manager's terminal4, to thereby assign an imaging worker ID for identifying the imaging worker50C to each imaging worker50C and register for each imaging worker50C a department ID for identifying a department (for example, a sub-contractor or a team) to which the imaging worker50C belongs, a name, a holding qualification, and an inspection manager ID for identifying the inspection manager50E who manages the imaging worker50C in the imaging worker table212C. The inspection process registration module201receives information on the judge50D who is to work in the welding site10from the manager's terminal4, to thereby assign a judge ID for identifying the judge50D to each judge50D and register for each judge50D a department ID for identifying a department to which the judge50D belongs, a name, a holding qualification, and an inspection manager ID for identifying the inspection manager50E who manages the judge50D in the judge table212D. The inspection process registration module201receives information on the inspection manager50E managing the imaging worker50C and the judge50D who are to work in the welding site10from the manager's terminal4, to thereby assign an inspection manager ID for identifying the inspection manager50E to each inspection manager50E and register for each inspection manager50E a department ID for identifying a department to which the inspection manager50E belongs, a name, and a holding qualification in the inspection manager table212E.

As described above, information required for execution of a welding process and an inspection process is registered in advance in the welding process database211and the inspection process database212. Note that, a welding execution record and an inspection execution record have not been registered in the stage of advance registration.

(2) Registration of Welding Execution Record in Welding Process

The welding process registration module200performs registration processing of registering a welding execution record in the welding process database211in association with execution of a welding process. In conjunction with this registration processing, the qualification judgment module202A performs qualification judgment processing for a holding qualification of the welder50A, and the deviation judgment module202B performs deviation judgment processing.

FIG.9andFIG.10are flowcharts of an example of the registration processing, the qualification judgment processing, and the deviation judgment processing in a welding process. InFIG.9andFIG.10, an operation after the welder50A who is to perform a welding process moves to a weld point11as a work target and installs the welding machine6to be used at the weld point11as the work target is described.

First, in Step S101, the welder50A inputs a welding site ID, a welding area ID, a drawing number, and a weld point number to the welder's terminal5A as weld point identification information for identifying the weld point11as the work target, and performs a welding process start operation indicating the start of the welding process. At this time, the welder50A holds the beacon card31thereof over the card reader51.

Next, in Step S102, the welder's terminal5A receives the welding process start operation to acquire the weld point identification information. The welder's terminal5A acquires a welder ID for identifying the welder50A by reading card information from the beacon card31of the welder50A with the card reader51. The welder's terminal5A acquires a welding machine ID for identifying the welding machine6by reading card information from the beacon card31attached to the welding machine6to be used with the card reader51. The welder's terminal5A may acquire the welding machine ID from the construction progress management device3.

Next, in Step S103, the welder's terminal5A transmits a welding process start notification including the weld point identification information, the welder ID, and the welding machine ID to the welding data processing device2.

Next, in Step S110, when the welding data processing device2receives the welding process start notification from the welder's terminal5A, the welding process registration module200refers to the welding process database211to identify a record corresponding to the weld point identification information included in the welding process start notification (hereinafter referred to as “welding process target record”) of the weld point table211A. In Step S111, the welding process registration module200then registers reception date and time of the welding process start notification as execution start date and time in the welding process target record. In addition, the welding process registration module200registers the welder ID included in the welding process start notification as an executing welder ID in the welding process target record and also registers the welding machine ID included in the welding process start notification as an executing welding machine ID in the welding process target record.

Next, in Step S120, the qualification judgment module202A refers to the welding process database211and judges eligibility of qualification, that is, whether the welder50A who is to perform the welding process at the weld point11as the work target holds a qualification for performing the welding process under the welding condition for that weld point11. Specifically, the qualification judgment module202A acquires a holding qualification registered in the welder table2110for the welder ID included in the welding process start notification, and also acquires a required qualification registered in the welding process target record of the weld point table211A. The qualification judgment module202A then compares the holding qualification of the welder50A and the required qualification for the weld point11with each other to judge eligibility of qualification described above.

As a result, when the qualification judgment module202A judges that the holding qualification of the welder50A is eligible with respect to the required qualification for the weld part11(Step S120: eligible), the welding process registration module200registers welder qualification judgment data indicating that the qualification is “eligible” in the welding process target record in Step S121, and the process proceeds to Step S130.

Meanwhile, when the qualification judgment module202A judges that the holding qualification of the welder50A is ineligible with respect to the required qualification for the weld point11(Step S120: ineligible), the welding process registration module200registers welder qualification judgment data indicating that the qualification is “ineligible” in the welding process target record in Step S122. Then, in Step S123, the welding process support module203performs qualification ineligibility processing to be performed when the holding qualification of the welder50A is ineligible. Specifically, the welding process support module203transmits a notification that the holding qualification of the welder50A is “ineligible” to, for example, the welder's terminal5A of the welder50A and the welding manager's terminal5B of the welding manager50B who manages that welder50A. Further, the welding process support module203transmits a switch control instruction to place the switch620connected to the welding machine6to be used in a cut-off state to the distribution board62. When receiving that notification, the welder50A and the welding manager50B reconfirm the holding qualification, and the switch620is turned off, and thus use of the welding machine6is prohibited. Therefore, execution of the welding process by an unqualified person can be prevented.

Next, in Step S130, the welding process support module203transmits the welding condition registered in the welding process target record of the weld point table211A to the welder's terminal5A.

Next, in Step S131, when receiving the welding condition from the welding data processing device2, the welder's terminal5A displays a preheating support screen based on the received welding condition. In Step S132, the welder50A measures the temperature of the weld point11through use of the temperature measurement device61, while preheating the weld point11through use of a welding preheating device (not shown) in accordance with the display content (for example, preheating in the welding condition) displayed on the preheating support screen. Then, in Step S133, the temperature measurement device61transmits preheating data13in which the measured temperature is recorded as the preheating state of the weld point11to the welding data processing device2.

Next, in Step S140, when the welding data processing device2receives the preheating data13from the temperature measurement device61, the welding process registration module200registers the preheating data13in the welding process target record.

Next, in Step S141, the deviation judgment module202B refers to the welding process database211and judges whether the preheating data13received from the temperature measurement device61deviates beyond a preheating deviation judgment criterion with respect to the welding condition (preheating) registered in the welding process target record of the weld point table211A. In the case in which, for example, a preheating temperature or a preheating time is determined as preheating in the welding condition, the deviation judgment module202B compares, for example, a preheating temperature or a preheating time indicated by the preheating data13with the preheating temperature or the preheating time in the welding condition and, when the difference between the indicated preheating temperature or preheating time and the preheating temperature or the preheating time in the welding condition exceeds the preheating deviation judgment criterion (a predetermined threshold value), judges that the preheating data13deviates.

When the deviation judgment module202B judges that the preheating data13does not deviate (Step S141: no deviation), the welding process registration module200registers preheating deviation judgment data indicating “no deviation” in the welding process target record in Step S142, and the process proceeds to Step S150.

Meanwhile, when the deviation judgment module202B judges that the preheating data13deviates (Step S141: deviation found), the welding process registration module200registers preheating deviation judgment data indicating “deviation found” in the welding process target record in Step S143. In Step S144, the welding process support module203then performs preheating deviation processing to be performed when the preheating data13deviates. Specifically, the welding process support module203transmits a notification of “deviation found” to, for example, the welder's terminal5A of the welder50A and the welding manager's terminal5B of the welding manager50B who manages that welder50A. Further, the welding process support module203transmits a switch control instruction to place the switch620connected to the welding machine6to be used in a cut-off state to the distribution board62. The welder50A and the welding manager50B perform preheating again when receiving that notification, the switch620is turned off, and use of the welding machine6is prohibited, and hence the weld quality can be improved.

The preheating deviation judgment data may include not only the presence or absence of deviation but also, as detailed data of the deviation, the amount of deviation of the preheating data13with respect to the welding condition. The amount of deviation includes, for example, the amount of deviation of preheating temperature (for example, a difference value or a ratio) with respect to the preheating temperature in the welding condition as a reference.

Next, in Step S150, the welding process support module203transmits the welding condition registered in the welding process target record of the weld point table211A to the welder's terminal5A.

Next, in Step S151, when receiving the welding condition from the welding data processing device2, the welder's terminal5A displays a welding support screen based on the welding condition. In Step S152, the welder50A operates the operation control panel65of the welding machine6in accordance with the display content (for example, electrical characteristics and a welding method in the welding condition) displayed on the welding support screen. Then, when the welder50A brings the welding torch63close to the weld point11and turns on the switch632, welding of the weld point11is performed. At this time, in Step S153, the welding machine monitoring device60records the operating state of the welding machine6and transmits welding machine operation data12in which the operating state is recorded to the welding data processing device2.

Next, in Step S160, when the welding data processing device2receives the welding machine operation data12from the welding machine monitoring device60, the welding process registration module200registers the received welding machine operation data12in the welding process target record. At this time, the welding process registration module200registers the date and time when the operating state of the welding machine6ends in the welding machine operation data12as execution end date and time in the welding process target record.

Next, in Step S161, the deviation judgment module202B refers to the welding process database211and judges whether the welding machine operation data12received from the welding machine monitoring device60deviates beyond a welding deviation judgment criterion with respect to the welding condition (at least one of a welding current, a welding voltage, a welding speed, a welding direction, or a welding angle) registered in the welding process target record of the weld point table211A. The deviation judgment module202B compares, for example, a welding current, a welding voltage, a welding speed, a welding direction, and a welding angle indicated by the welding machine operation data12with the welding current, the welding voltage, the welding speed, the welding direction, and the welding angle in the welding condition, respectively, and, when at least one of the differences between those values exceeds the welding deviation judgment criterion (a predetermined threshold value), judges that the welding machine operation data12deviates.

When the deviation judgment module202B judges that the welding machine operation data12does not deviate (Step S161: no deviation), the welding process registration module200registers welding deviation judgment data indicating “no deviation” in the welding process target record in Step S162, and the series of steps of processing is ended.

Meanwhile, when the deviation judgment module202B judges that the welding machine operation data12deviates (Step S161: deviation found), the welding process registration module200registers welding deviation judgment data indicating “deviation found” in the welding process target record in Step S163. In Step S164, the welding process support module203then performs welding deviation processing to be performed when the welding machine operation data12deviates. Specifically, the welding process support module203transmits a notification of “deviation found” to, for example, the welder's terminal5A of the welder50A and the welding manager's terminal5B of the welding manager50B who manages the welder50A. The welder50A and the welding manager50B determine necessity of repair welding or re-welding, priority of a radiographic test, or the like when receiving the notification, and hence it is possible to speed up decision-making and improve the weld quality.

The welding deviation judgment data may include not only the presence or absence of deviation but also, as detailed data of the deviation, the amount of deviation of the welding machine operation data12with respect to the welding condition. The amount of deviation includes, for example, the amount of deviation (a difference value, a ratio, or the like) of a welding voltage with respect to a welding voltage in the welding condition as a reference, the amount of deviation (a difference value, a ratio, or the like) of a welding current with respect to a welding current in the welding condition as a reference, the amount of deviation (a difference value, a ratio, or the like) of a welding speed with respect to a welding speed in the welding condition as a reference, the amount of deviation (a difference value, a ratio, or the like) of a welding direction with respect to a welding direction in the welding condition as a reference, or the amount of deviation (a difference value, a ratio, or the like) of a welding angle with respect to a welding angle in the welding condition as a reference.

The welding process is performed at each weld point11in the above-mentioned manner, and thus the welding execution record including the welding machine operation data12and the preheating data13is recorded in the welding process database211(specifically, in each welding process target record corresponding to the weld point11) at any time and accumulated.

(3) Registration of Inspection Execution Record in Imaging Process

The inspection process registration module201performs registration processing of registering an inspection execution record in the inspection process database212in association with execution of an imaging process included in a radiographic test process. In conjunction with this registration processing, the qualification judgment module202A performs qualification judgment processing for a holding qualification of the imaging worker50C.

FIG.11is a flowchart of an example of the registration processing and the qualification judgment processing in the imaging process included in the radiographic test process. InFIG.11, an operation after the imaging worker50C, who is to perform the imaging process, moves to a weld point11as a work target and installs the inspection machine7to be used at the weld point11as the work target is described.

First, in Step S201, the imaging worker50C inputs a welding site ID, a welding area ID, a drawing number, and a weld point number to the imaging worker's terminal5C as weld point identification information for identifying the weld point11as the work target, and performs an imaging process start operation indicating the start of the imaging process. At this time, the imaging worker50C holds the beacon card31thereof over the card reader51.

Next, in Step S202, the imaging worker's terminal5C receives the imaging process start operation to acquire the weld point identification information. The imaging worker's terminal5C acquires an imaging worker ID for identifying the imaging worker50C by reading card information from the beacon card31of the imaging worker50C with the card reader51. The imaging worker's terminal5C acquires an inspection machine ID for identifying the inspection machine7by reading card information from the beacon card31attached to the inspection machine7to be used with the card reader51. The imaging worker's terminal5C may acquire the inspection machine ID from the construction progress management device3.

Next, in Step S203, the imaging worker's terminal5C transmits an imaging process start notification including the weld point identification information, the imaging worker ID, and the inspection machine ID to the welding data processing device2.

Next, in Step S210, when the welding data processing device2receives the imaging process start notification from the imaging worker's terminal5C, the inspection process registration module201refers to the inspection process database212to identify a record corresponding to the weld point identification information included in the imaging process start notification (hereinafter referred to as “imaging process target record”) in the weld point table212A. In Step S211, the inspection process registration module201then registers reception date and time of the imaging process start notification as imaging date and time in the imaging process target record. In addition, the inspection process registration module201registers the imaging worker ID included in the imaging process start notification as an executing imaging worker ID in the imaging process target record and also registers the inspection machine ID included in the imaging process start notification as an executing inspection machine ID in the imaging process target record.

Next, in Step S220, the qualification judgment module202A refers to the inspection process database212and judges eligibility of qualification, that is, whether the imaging worker50C who is to perform the imaging process at the weld point11as the work target holds a qualification for performing the imaging process for the weld point11. Specifically, the qualification judgment module202A acquires a holding qualification registered in the imaging worker table212C for the imaging worker ID included in the imaging process start notification, and also acquires an imaging worker required qualification registered in the imaging process target record of the weld point table212A. The qualification judgment module202A then compares the holding qualification of the imaging worker50C and the imaging worker required qualification for the weld point11with each other to judge eligibility of qualification described above.

As a result, when the qualification judgment module202A judges that the holding qualification of the imaging worker50C is eligible with respect to the imaging worker required qualification for the weld part11(Step S220: eligible), the inspection process registration module201registers imaging worker qualification judgment data indicating that the qualification is “eligible” in the imaging process target record in Step S211, and the process proceeds to Step S230.

Meanwhile, when the qualification judgment module202A judges that the holding qualification of the imaging worker50C is ineligible with respect to the imaging worker required qualification for the weld point11(Step S220: ineligible), the inspection process registration module201registers imaging worker qualification judgment data indicating that the qualification is “ineligible” in the imaging process target record in Step S222. In Step S223, the inspection process support module204then performs qualification ineligibility processing to be performed when the holding qualification of the imaging worker50C is ineligible. Specifically, the inspection process support module204transmits a notification indicating that the qualification is “ineligible” to, for example, the imaging worker's terminal5C of the imaging worker50C and the inspection manager's terminal5E of the inspection manager50E who manages the imaging worker50C. The imaging worker50C and the inspection manager50E reconfirm the holding qualification when receiving that notification, and hence execution of the imaging process by an unqualified person can be prevented.

Next, in Step S230, the inspection process support module204refers to the welding process database211, identifies a record corresponding to the weld point identification information included in the imaging process start notification (hereinafter referred to as “welding process reference record”) of the weld point table211A, and acquires a welding condition registered in the identified welding process reference record. The inspection process support module204then transmits the acquired welding condition to the imaging worker's terminal5C.

Next, in Step S231, when receiving the welding condition from the welding data processing device2, the imaging worker's terminal5C displays an imaging process support screen based on the welding condition. In Step S232, the imaging worker50C operates the operation control panel74of the inspection machine7while referring to the display content (for example, diameter, thickness, material, joint shape, and welding material in the welding condition) displayed on the imaging process support screen, and sets operation parameters of the radiation irradiation unit70, the detector71, and the driving unit73. Then, when the imaging worker50C performs an imaging start operation on the operation control panel74, the inspection machine7captures an image of the weld point11over the entire circumference to generate image data14in Step S233. The inspection machine7then transmits the image data14to the welding data processing device2.

Next, in Step S240, when the welding data processing device2receives the image data14from the inspection machine7, the inspection process registration module201registers the image data14in the imaging process target record, and the series of steps of processing is ended.

The imaging process is performed at each weld point11in the above-mentioned manner, and thus the inspection execution record including the image data14is registered in the inspection process database212(specifically, in the imaging process target records corresponding to the respective weld points11) at any time and accumulated.

(4) Registration of Inspection Execution Record in Image Judgment Process

The inspection process registration module201performs registration processing of registering the inspection execution record in the inspection process database212in association with execution of the image judgment process included in the radiographic test process. In conjunction with this registration processing, the qualification judgment module202A performs qualification judgment processing for a holding qualification of the judge50D.

FIG.12is a flowchart of an example of the registration processing and the qualification judgment processing in the image judgment process included in the radiographic test process. InFIG.12, an operation in the case in which the judge50D, who is to perform the image judgment process, performs work at a location different from the weld point11is described.

First, in Step S301, the judge50D inputs a welding site ID, a welding area ID, a drawing number, and a weld point number to the judge's terminal5D as weld point identification information for identifying the weld point11as a work target, and performs an image judgment process start operation indicating the start of the judgment process. At this time, the judge50D holds the beacon card31thereof over the card reader51.

Next, in Step S302, the judge's terminal5D receives the image judgment process start operation to acquire the weld point identification information. The judge's terminal5D acquires a judge ID for identifying the judge50D by reading card information from the beacon card31of the judge50D with the card reader51.

Next, in Step S303, the judge's terminal5D transmits an image judgment process start notification including the weld point identification information and the judge ID to the welding data processing device2.

Next, in Step S310, when the welding data processing device2receives the image judgment process start notification from the judge's terminal5D, the inspection process registration module201refers to the inspection process database212and identifies a record corresponding to the weld point identification information included in the image judgment process start notification (hereinafter referred to as “image judgment process target record”) in the weld point table212A. Moreover, in Step S311, the inspection process registration module201registers reception date and time of the image judgment start notification as image judgment date and time in the image judgment process target record and also registers the judge ID included in the image judgment process start notification as an executing judge ID in the image judgment process target record.

Next, in Step S320, the qualification judgment module202A refers to the inspection process database212and judges eligibility of qualification, that is, whether the judge50D who performs the image judgment process at the weld point11as the work target holds a qualification for performing the image judgment process at that weld point11. Specifically, the qualification judgment module202A acquires the holding qualification registered in the judge table212D for the judge ID included in the image judgment process start notification and also acquires a judge required qualification registered in the image judgment process target record of the weld point table212A. The qualification judgment module202A then compares the holding qualification of the judge50D and the judge required qualification for the weld point11with each other to judge eligibility of qualification described above.

As a result, when the qualification judgment module202A judges that the holding qualification of the judge50D is eligible with respect to the judge required qualification for the weld point11(Step S320: eligible), the inspection process registration module201registers judge qualification judgment data indicating that the qualification is “eligible” in the image judgment process target record in Step S321, and the process proceeds to Step S330.

Meanwhile, when the qualification judgment module202A judges that the holding qualification of the judge50D is ineligible with respect to the judge required qualification for the weld point11(Step S320: ineligible), the inspection process registration module201registers judge qualification judgment data indicating that the qualification is “ineligible” in the image judgment process target record in Step S322. In Step S323, the inspection process support module204then performs qualification ineligibility processing to be performed when the holding qualification of the judge50D is ineligible. Specifically, the inspection process support module204transmits a notification indicating that the qualification is “ineligible” to, for example, the judge's terminal5D of the judge50D and the inspection manager's terminal5E of the inspection manager50E who manages the judge50D. The judge50D and the inspection manager50E reconfirm the holding qualification when receiving that notification, and hence execution of the image judgment process by an unqualified person can be prevented.

Next, in Step S330, the inspection process support module204acquires the image data14registered in the image judgment process target record of the weld point table212A. Further, the inspection process support module204refers to the welding process database211, identifies a record corresponding to the weld point identification information included in the image judgment process start notification (hereinafter referred to as “welding process reference record”) of the weld point table211A, and acquires the welding condition and the welding execution record registered in the identified welding process reference record. The inspection process support module204then transmits judgment target data including the image data14, the welding condition, and the welding execution record thus acquired to the judge's terminal5D.

Next, in Step S331, when receiving the judgment target data from the welding data processing device2, the judge's terminal5D displays an image judgment process support screen based on the judgment target data. In Step S332, the judge50D checks the image data14displayed on the image judgment process support screen with eyes to judge whether there is a weld defect at the weld point11. At this time, the judge50D can refer to the contents of the welding condition and the welding execution record displayed on the image judgment process support screen. Then, when the judge50D performs a defect judgment result input operation of inputting the result of judgment of whether there is a weld defect on the image judgment process support screen in Step S333, the judge's terminal5D generates defect judgment data based on the defect judgment result input operation and transmits the defect judgment data to the welding data processing device2in Step S334.

Next, in Step S340, when the welding data processing device2receives the defect judgment data from the judge's terminal5D, the inspection process registration module201registers the defect judgment data in the image judgment process target record, and the series of steps of processing is ended.

The image judgment process is performed at each weld point11in the above-mentioned manner, and thus the inspection execution record including the defect judgment data is registered in the inspection process database212(specifically, in image judgment process target records corresponding to the respective weld points11) at any time and accumulated.

(5) Analysis of Defect Occurrence Tendency

The defect analysis module202C performs defect analysis processing of analyzing a defect occurrence tendency based on information in each of records registered in the welding process database211and the inspection process database212.

The defect analysis module202C analyzes the defect occurrence tendency based on at least one of a welding condition or a welding execution record and defect judgment data, as the information in each record. At this time, the defect analysis module202C receives a predetermined analysis condition and performs statistical processing in accordance with the analysis condition, to thereby analyze the defect occurrence tendency.

The defect occurrence tendency shows a tendency at the time when a weld defect has occurred at a weld point11and is represented by a statistical index value such as a mean value, a median value, a maximum value, a minimum value, a variance, a standard deviation, a ratio, and a frequency. The defect occurrence tendency is obtained by a statistical method such as grand total, cross tabulation, frequency distribution, Pareto analysis, matrix analysis, regression analysis, and factor analysis.

As the analysis condition, an analysis target range and an analysis axis, for example, are specified in addition to the statistical method and the statistical index value described above.

As for the analysis target range, a range of records to be analyzed by the defect analysis module202C is specified among records registered in the welding process database211and the inspection process database212. The analysis target range is specified by, for example, location, period, workers50A to50E, work time, department, welding condition, welding execution record, presence or absence of a weld defect, defect detail status, welding machine6, or inspection machine7, or a combination thereof. The analysis target range is specified by, for example, a variable/variables in one or a plurality of fields included in each of the tables211A to221D of the welding process database211and the tables212A to222E of the inspection process database212. Specifying the analysis target range may be omitted.

For example, when at least one of a welding site ID or a welding area ID of the weld point table211A is specified as the analysis target range, the defect analysis module202C extracts a record matching the welding site10and the welding area thus specified as the analysis target. When a period (for example, past one week, one month, or one year) is specified as the analysis target range, the defect analysis module202C extracts a record matching the specified period as the analysis target. When at least one of an executing welder ID of the weld point table211A, a welding manager ID of the welding manager table211D, or a department ID of the welding manager table211D is specified as the analysis target range, the defect analysis module202C extracts a record matching the at least one of the executing welder ID, the welding manager ID, or the department ID thus specified as the analysis target. When a work time based on execution start date and time and execution end date and time (for example, a real work time (a time from the execution start date and time to the execution end date and time), a waiting time before work (a time from the execution end date and time of previous welding to the execution start date and time of current welding), and a work time zone (after meal, after rest, shift zone, or the like)) is specified as the analysis target range, the defect analysis module202C extracts a record matching the specified period as the analysis target. When at least one of fields (diameter, thickness, material (base material), joint shape, welding material, preheating, post-weld heat treatment, shielding gas, welding current, welding voltage, welding speed, welding direction, welding angle, and arc time) included in a welding condition of the weld point table211A and a variable for the at least one field are specified as the analysis target range, the defect analysis module202C extracts a record matching the specified variable for the field as the analysis target. When at least one of fields (preheating data13, welding machine operation data12, preheating deviation judgment data, and welding deviation judgment data) included in a welding execution record of the weld point table211A and a variable for the at least one field are specified as the analysis target range, the defect analysis module202C extracts a record matching the specified variable for the field as the analysis target. In this case, the analysis target range may be specified by detailed data included in any of the preheating data13, the welding machine operation data12, the preheating deviation judgment data, and the welding deviation judgment data.

The analysis axis specifies a field to be used when the defect analysis module202C analyzes a plurality of records included in the analysis target range by a predetermined statistical method. The analysis axis is specified by, for example, location, period, workers50A to50E, work time, department, welding condition, welding execution record, presence or absence of a weld defect, defect detail status, welding machine6, or inspection machine7, or a combination thereof. The analysis axis is specified by, for example, one or a plurality of fields included in each of the tables211A to221D of the welding process database211and the tables212A to222E of the inspection process database212. Specifying the analysis axis may be omitted.

For example, when at least one of a welding site ID or a welding area ID of the weld point table211A is specified as the analysis axis, the defect analysis module202C analyzes a defect occurrence tendency with respect to at least one of the welding site10or the welding area thus specified as the analysis axis. When a period (for example, on daily basis, on weekly basis, or on monthly basis) is specified as the analysis axis, the defect analysis module202C analyzes the defect occurrence tendency with respect to the specified period as the analysis axis. When at least one of an executing welder ID of the weld point table211A or a welding manager ID and a department ID of the manager table211D is specified as the analysis axis, the defect analysis module202C analyzes the defect occurrence tendency with respect to at least one of the welder50A, the welding manager50B, or the department thus specified as the analysis axis. When a work time based on the execution start date and time and the execution end date and time (for example, a real work time, a waiting time before work, or a work time zone) is specified as the analysis axis, the defect analysis module202C analyzes the defect occurrence tendency with respect to the specified work time as the analysis axis. When at least one of fields (diameter, thickness, material (base material), joint shape, welding material, preheating, post-weld heat treatment, shielding gas, welding current, welding voltage, welding speed, welding direction, welding angle, and arc time) included in a welding condition of the weld point table211A is specified as the analysis axis, the defect analysis module202C analyzes the defect occurrence tendency with respect to the at least one field thus specified as the analysis axis. When at least one of fields (preheating data13, welding machine operation data12, preheating deviation judgment data, and welding deviation judgment data) included in a welding execution record of the weld point table211A is specified as the analysis axis, the defect analysis module202C analyzes the defect occurrence tendency with respect to the at least one field thus specified as the analysis axis. At this time, the analysis axis may be specified by detailed data included in any of the preheating data13, the welding machine operation data12, the preheating deviation judgment data, and the welding deviation judgment data.

As the analysis condition, an analysis frequency at which the defect occurrence tendency is regularly analyzed and the analysis result is regularly notified, or a notification condition for notifying the result of analysis of the defect occurrence tendency, such as when a defect occurrence ratio is higher than a predetermined value, may be specified.

FIG.13is a flowchart of an example of the defect analysis processing.FIG.13shows an operation in the case in which the defect analysis module202C receives an analysis condition in the defect analysis processing from the manager's terminal4and analyzes the defect occurrence tendency in accordance with the analysis condition.

First, in Step S401, when receiving an operation for requesting the defect analysis processing from the site manager40, the manager's terminal4displays an analysis condition input screen42. When the site manager40performs an analysis condition input operation of inputting an analysis condition on the analysis condition input screen42in Step S402, the manager's terminal4receives the analysis condition and transmits the received analysis condition to the welding data processing device2in Step S403.

FIG.14is a diagram of an example of the analysis condition input screen42. The analysis condition input screen42includes input fields420to423for specifying the analysis condition to be used when the defect analysis module202C analyzes the defect occurrence tendency.

In the analysis condition input screen42illustrated inFIG.14, “cross tabulation” is specified in the statistical method input field420; “ratio” is specified in the statistical index value input field421; location “weld site AAA,” period “past three months,” and weld condition “diameter: 3 inches or more” are specified in the analysis target range input field422; and period “on monthly basis,” worker “by welder,” and defect detail status “defect type” are specified in the analysis axis input field423.

Returning toFIG.13, when the welding data processing device2receives the analysis condition from the manager's terminal4, in Step S410, the defect analysis module202C analyzes the defect judgment tendency in accordance with the received analysis condition. Specifically, the defect analysis module202C extracts a record matching the analysis target range specified as the analysis condition from the weld point table211A of the welding process database211and the weld point table212A of the inspection process database212.

In the case in which the analysis condition is specified as illustrated inFIG.14, a record in which the welding site ID is “welding site AAA,” the execution start date and time is within “past three months,” and the diameter in the welding condition is “3 inches or more” are extracted in the weld point table211A.

The defect analysis module202C then performs statistical processing for the extracted records in accordance with the statistical method, the statistical index value, and the analysis axis specified as the analysis condition, to thereby derive the analysis result of the defect judgment tendency. In the case in which the analysis condition is specified as illustrated inFIG.14, “cross tabulation” is performed with regard to “ratio” by using three analysis axes: period “on monthly basis”; worker “by welder”; and defect detail status “defect type”.

Next, in Step S411, the output processing module205transmits the analysis result of the defect judgment tendency to the manager's terminal4. In Step S420, when receiving the analysis result of the defect judgment tendency from the welding data processing device2as a response to the analysis condition, the manager's terminal4displays an analysis result display screen43based on the received analysis result.

FIG.15is a diagram of an example of the analysis result display screen43in the defect analysis processing. The analysis result display screen43includes a display region430for displaying the defect occurrence tendency that is the result of analysis by the defect analysis module202C in a predetermined tabular form or graphical format.

The analysis result display screen43illustrated inFIG.15is a screen to be displayed in the case in which the analysis condition is specified as illustrated inFIG.14. Therefore, the analysis result obtained by performing “cross tabulation” through use of three analysis axes of “by welder,” “on monthly basis,” and “defect type” is shown in a pie chart format in the display region430. In the pie chart, a defect occurrence ratio for each “defect type” is expressed, and the frequency is expressed by the magnitude of the defect occurrence ratio. The analysis result display screen43may be configured to allow the tabular form or the graphical format to be changed. Moreover, the analysis result display screen43may have a button for causing the analysis condition input screen42to be displayed and, when the analysis condition is changed, display the analysis result based on the changed analysis condition.

When the average value, the lowest temperature, or the highest temperature of the preheating temperature or the preheating time included in the preheating data13of the welding execution record, for example, is specified as the analysis axis as another analysis condition, the analysis result of the defect judgment tendency for the values thus specified is derived and displayed. Moreover, when the deviation amount included in the preheating deviation judgment data (the deviation amount of preheating temperature) or the deviation amount included in the welding deviation judgment data (the deviation amount of welding voltage, the deviation amount of welding current, the deviation amount of welding speed, the deviation amount of welding direction, or the deviation amount of welding angle) is specified as the analysis axis, the analysis result of the defect judgment tendency for the deviation amount thus specified is derived and displayed. Further, when a work time based on the execution start date and time and the execution end date and time (a real work time, a waiting time before work, or a work time zone) is specified as the analysis axis, the analysis result of the defect judgment tendency for the work time thus specified is derived and displayed.

As described above, the defect analysis module202C analyzes the defect occurrence tendency based on information registered in the welding process database211and the inspection process database212. While referring to the analysis result of the defect occurrence tendency, the site manager40(who may be any of the workers50A to50E) can investigate the situation and cause of the occurrence of a weld defect, find out the situation and cause at an early stage, and reflect the findings in education and guidance to the workers50A to50E and assignment of the workers50A to50E to each weld point11, for example. Therefore, the weld quality and the productivity can be improved.

(6) Analysis of Qualification Ineligibility Occurrence Tendency

The qualification ineligibility analysis module202D performs qualification ineligibility analysis processing of analyzing a tendency of qualification ineligibility occurrence regarding the holding qualification of each of the workers50A to50E based on information in each record registered in the welding process database211and the inspection process database212. When the qualification held by any of the workers50A to50E is ineligible with respect to the qualification required for the weld point11, the qualification ineligibility processing in Step S123, Step S223, or Step S323is performed, and thus the work by the unqualified one of the workers50A to50E is prevented. Therefore, the qualification ineligibility analysis processing by the qualification ineligibility analysis module202D assumes to analyze a situation in which any of the workers50A to50E has erroneously performed the work although that worker is unqualified.

The qualification ineligibility analysis module202D analyzes the tendency of qualification ineligibility occurrence based on at least one of a welding condition or a welding execution record and welder qualification judgment data, as the information in each record. In this analysis, the qualification ineligibility analysis module202D receives a predetermined analysis condition and performs statistical processing in accordance with the analysis condition, to thereby analyze the tendency of qualification ineligibility occurrence, as with the defect analysis module202C. The tendency of qualification ineligibility occurrence shows an occurrence tendency of ineligibility at the time when the holding qualification of the welder50A and the required qualification for the weld point11are compared with each other, and can be represented by various statistical index values and obtained by various statistical methods, as with the defect occurrence tendency. Further, as for the analysis condition for the tendency of qualification ineligibility occurrence, a statistical method, a statistical index value, an analysis target range, and an analysis axis are specified, as with the analysis condition for the defect occurrence tendency.

The qualification ineligibility analysis module202D may analyze, based on at least one of a welding condition, a welding execution record, or an inspection execution record and imaging worker qualification judgment data, the tendency of qualification ineligibility occurrence showing the occurrence tendency of ineligibility at the time when the holding qualification of the imaging worker50C and the required qualification for the weld point11are compared with each other. Further, the qualification ineligibility analysis module202D may analyze, based on at least one of the welding condition, the welding execution record, or the inspection execution record and judge qualification judgment data, the tendency of qualification ineligibility occurrence showing the occurrence tendency of ineligibility at the time when the holding qualification of the judge50D and the required qualification for the weld point11are compared with each other.

FIG.16is a flowchart of an example of the qualification ineligibility analysis processing.FIG.16shows an operation in the case in which the qualification ineligibility analysis module202D receives an analysis condition in the qualification ineligibility analysis processing from the manager's terminal4and analyzes the tendency of qualification ineligibility occurrence in accordance with the received analysis condition. The basic operation is similar to the defect analysis processing illustrated inFIG.13, and therefore the detailed description thereof is omitted.

FIG.17is a diagram of an example of an analysis result display screen44in the qualification ineligibility analysis processing. The analysis result display screen44includes a display region440for displaying the tendency of qualification ineligibility occurrence, which is the result of analysis by the qualification ineligibility analysis module202D, in a predetermined tabular form or graphical format. In the display region440illustrated in FIG.17, the analysis result obtained by “cross tabulation” for judge qualification judgment data with respect to two analysis axes of “by welding site” and “by department” is shown in a bar chart format.

As described above, the qualification ineligibility analysis module202D analyzes the tendency of qualification ineligibility occurrence based on information registered in the welding process database211and the inspection process database212. While referring to the analysis result of the tendency of qualification ineligibility occurrence, the site manager40(who may be any of the workers50A to50E) can grasp, for example, the welder50A who has erroneously performed the welding process without qualification, the welding manager50B who manages that welder50A, and the department to which that welder50A belongs, and can perform education and guidance. Therefore, the weld quality and the productivity can be improved.

(7) Analysis of Deviation Occurrence Tendency

The deviation analysis module202E performs deviation analysis processing of analyzing a deviation occurrence tendency regarding deviation of a welding execution record with respect to a welding condition based on information in each record registered in the welding process database211and the inspection process database212.

The deviation analysis module202E analyzes the deviation occurrence tendency based on at least one of the welding condition or the welding execution record and at least one of preheating deviation judgment data or welding deviation judgment data, as the information in each record. In this analysis, the deviation analysis module202E receives a predetermined analysis condition and performs statistical processing in accordance with the analysis condition, to thereby analyze the deviation occurrence tendency, as with the defect analysis module202C. The deviation occurrence tendency shows an occurrence tendency of deviation at the time when the welding execution record (the welding machine operation data12or the preheating data13) and the welding condition for the weld point11are compared with each other, and can be represented by various statistical index values and obtained by various statistical methods, as with the defect occurrence tendency. Further, as for the analysis condition for the deviation occurrence tendency, a statistical method, a statistical index value, an analysis target range, and an analysis axis are specified, as with the analysis condition for the defect occurrence tendency.

The deviation judgment criterion setting module202F sets a deviation judgment criterion to be used in judgment by the deviation judgment module202B regarding whether deviation is present, based on the welding condition, the welding execution record, and defect judgment data. When setting a welding deviation judgment criterion, the deviation judgment criterion setting module202F acquires, for example, the welding condition (the welding current and the welding voltage) and the welding machine operation data12that correspond to the weld point11registered as “non-defective” in the defect judgment data in the weld point table212A of the inspection process database212, from the weld point table211A of the welding process database211and sets the welding deviation judgment criterion based on differences between the welding current and the welding voltage in the acquired welding condition and the welding machine operation data12(for example, the maximum value or the confidence interval). When setting a preheating deviation judgment criterion, the deviation judgment criterion setting module202F acquires, for example, the welding condition (preheating) and the preheating data13that correspond to the weld point11registered as “non-defective” in the defect judgment data in the weld point table212A of the inspection process database212, from the weld point table211A of the welding process database211and sets the preheating deviation judgment criterion based on a difference between the preheating in the acquired welding condition and the preheating data13(for example, the maximum value or the confidence interval).

FIG.18is a flowchart of an example of the deviation analysis processing.FIG.18shows an operation in the case in which the deviation analysis module202E receives an analysis condition in the deviation analysis processing from the manager's terminal4and analyzes the deviation occurrence tendency in accordance with the analysis condition. The basic operation is similar to the defect analysis processing illustrated inFIG.13, and therefore the detailed description thereof is omitted.

FIG.19is a diagram of an example of an analysis result display screen45in the deviation analysis processing. The analysis result display screen45includes a display region450for displaying the deviation occurrence tendency, which is the result of analysis by the deviation analysis module202E, in a predetermined tabular form or graphical format. In the display region450illustrated inFIG.19, the analysis result obtained by “cross tabulation” for preheating deviation judgment data and welding deviation judgment data with respect to two analysis axes of “by welding manager” and “by welder” is shown in a bar chart format.

As described above, the deviation analysis module202E analyzes the deviation occurrence tendency based on information registered in the welding process database211and the inspection process database212. While referring to the analysis result of the deviation occurrence tendency, the site manager40(who may be any of the workers50A to50E) can grasp, for example, the welder50A who has performed the welding process under a state in which deviation occurs with respect to the welding condition for the weld point11, the welding manager50B who manages the welder50A, and the department to which that welder50A belongs, and can perform education and guidance. Therefore, the weld quality and the productivity can be improved.

(8) Welding Assist Function Before Welding Process, Using Defect Occurrence Tendency

The welding assist module202G performs welding assist processing of generating assist information including a feature of the welding execution record for which a weld defect is predicted to occur at a weld point11as an assist target, based on the welding condition at the weld point11and the defect occurrence tendency obtained by analysis of information registered in the welding process database211and the inspection process database212performed by the defect analysis module202C. The weld point11as the assist target may be any weld point11corresponding to a record in which a welding condition is registered in the weld point table211A of the welding process database211. Therefore, the welding assist processing is performed at any timing before a welding process is performed at the weld point as the assist target.

FIG.20is a flowchart of an example of the welding assist processing.FIG.20shows an operation in the case in which the welding assist module202G transmits assist information by the welding assist processing in Step S130ofFIG.9or Step S150ofFIG.10and the assist information is displayed in Step S131or Step S151.

First, in Step S701, the welding assist module202G receives the weld point11identified by the welding process target record as the weld point11that is the assist target, to thereby acquire the welding condition registered in the welding process target record of the weld point table211A.

Next, in Step S702, the welding assist module202G generates an analysis condition for the defect analysis processing to be performed by the defect analysis module202C, based on the welding condition at the weld point11as the assist target. As for the analysis condition here, for example, the minimum value exhibited when a weld defect has occurred by grand total is specified as a statistical method and a statistical index value; the same or predetermined similar range as/to the welding condition at the weld point11as the assist target is specified as an analysis target range; and the deviation amount included in the preheating deviation judgment data or the deviation amount included in the welding deviation judgment data is specified as an analysis axis.

Next, in Step S703, the defect analysis module202C analyzes the defect occurrence tendency under the analysis condition generated by the welding assist module202G to derive the analysis result. In the example of the above-mentioned analysis condition, the defect analysis module202C extracts a record with a welding condition that is the same as or within a predetermined similar range to the welding condition at the weld point11as the assist target, from the weld point table211A of the welding process database211and the weld point table212A of the inspection process database212. The defect analysis module202C then refers to the deviation amount in the welding execution record of a record with the defect judgment data indicating “defective” among the extracted records, and derives the minimum value of the deviation amount (the result of analysis of the defect occurrence tendency). Then, in Step S704, the welding assist module202G generates assist information based on the result of analysis of the defect occurrence tendency derived by the defect analysis module202C.

Next, in Step S710(Step S130or Step S150), the output processing module205transmits the welding condition and the assist information by the welding assist module202G to the welder's terminal5A of the welder50A. In Step S711(Step S131or Step S151), when receiving the welding condition and the assist information from the welding data processing device2, the welder's terminal5A displays the preheating support screen or the welding support screen based on the welding condition and the assist information. The welder50A can grasp in advance how much deviation causes a weld defect by checking the assist information (for example, the minimum value of the deviation amount) displayed on the preheating support screen or the welding support screen. The welding assist module202G may generate assist information including a defect occurrence ratio exhibited when the welder50A who is to perform the welding process has performed the welding process in the past under the same welding condition as that for the weld point11as the assist target. The welder50A grasps the defect occurrence ratio thereof in advance and, when the defect occurrence ratio is high, is promoted to perform the welding process with caution, and hence the weld quality can be improved.

As described above, the welding assist module202G generates assist information including a feature of the welding execution record for which a weld defect is predicted to occur at a weld point11as an assist target based on information registered in the welding process database211and the inspection process database212. The welder50A can perform the welding process while referring to the assist information provided before the welding process is performed, and hence the weld quality and the productivity can be improved.

(9) Defect Prediction Advice Function after Welding Process, Using Defect Occurrence Tendency

The defect prediction module202H performs defect prediction processing of predicting whether there is a weld defect at a weld point11as a prediction target and generating advice information including the prediction result, based on the welding condition and the welding execution record at the weld point11and the defect occurrence tendency obtained by analysis of information registered in the welding process database211and the inspection process database212performed by the defect analysis module202C. The weld point11as the prediction target may be any weld point11corresponding to a record in which a welding condition and a welding execution record are registered in the weld point table211A of the welding process database211. Therefore, the defect prediction processing is performed at any timing after a welding process is performed at the weld point11as the prediction target.

FIG.21is a flowchart of an example of the defect prediction processing.FIG.21shows an operation in the case in which the defect prediction module202H performs the defect prediction processing after Step S162or Step S164ofFIG.10.

First, in Step S801, the defect prediction module202H receives the weld point11identified by the welding process target record as the weld point11as the prediction target, to thereby acquire the welding condition and the welding execution record registered in the welding process target record of the weld point table211A.

Next, in Step S802, the defect prediction module202H generates an analysis condition for the defect analysis processing to be performed by the defect analysis module202C, based on the welding condition and the welding execution record at the weld point11as the prediction target. As for the analysis condition here, for example, a weld defect occurrence ratio by grand total is specified as a statistical method and a statistical index value, and the same or predetermined similar range as/to the welding condition and the welding execution record at the weld point11as the prediction target are specified as an analysis target range. Specifying an analysis axis is omitted.

Next, in Step S803, the defect analysis module202C analyzes the defect occurrence tendency under the analysis condition generated by the defect prediction module202H to derive the analysis result. In the example of the above-mentioned analysis condition, the defect analysis module202C extracts a record with a welding condition and a welding execution record that are the same as or within a predetermined similar range to the welding condition and the welding execution record at the weld point11as the prediction target, from the weld point table211A of the welding process database211and the weld point table212A of the inspection process database212. The defect analysis module202C then derives a ratio in which the total number of the extracted records is a denominator and the total number of records with the defect judgment data indicating “defective” among the extracted records is a numerator, that is, a weld defect occurrence ratio (the result of analysis of the defect occurrence tendency).

Next, in Step S804, the defect prediction module202H predicts whether there is a weld defect based on the result of analysis of the defect occurrence tendency derived by the defect analysis module202C. In the example of the above-mentioned analysis condition, the defect prediction module202H gives the prediction result indicating “defective” when the weld defect occurrence ratio is a predetermined reference value or more, and gives the prediction result indicating “non-defective” when the weld defect occurrence ratio is less than the predetermined reference value. The defect prediction module202H may use the weld defect occurrence ratio as the result of prediction of whether there is a weld defect.

Next, in Step S810, the defect prediction module202H refers to the welding process database211and identifies a record corresponding to the weld point11as the prediction target in the weld point table212A. The defect prediction module202H then registers defect prediction data indicating the result of prediction of whether there is a weld defect in the identified record.

Next, in Step S820, the output processing module205transmits the defect prediction data obtained by the defect prediction module202H to the welder's terminal5A of the welder50A and the welding manager's terminal5B of the welding manager50B who manages the welder50A.

Then, in Step S821and Step S822, when receiving the defect prediction data from the welding data processing device2, the welder's terminal5A and the welding manager's terminal5B display a prediction result display screen based on the defect prediction data.

As described above, the defect prediction module202H predicts whether there is a weld defect at the weld point11as the prediction target based on information registered in the welding process database211and the inspection process database212. The welder50A and the welding manager50B (who may be the site manager40or any of the workers50C to50E) determine, for example, necessity of repair welding or re-welding, priority of a radiographic test, or the like in response to display of the prediction result display screen based on the prediction result, and hence it is possible to speed up decision-making and improve the weld quality.

(10) Defect Inference Advice Function after Welding Process, Using Learning Model

FIG.22is a schematic diagram of an example of machine learning processing and defect inference processing.

The machine learning module2021performs machine learning processing that inputs a plurality of learning data pairs to a learning model25to cause the learning model25to learn a correlation between input data and training data by machine learning. Each learning data pair is configured by associating, for a common weld point11, a welding condition and a welding execution record registered in the weld point table211A as the input data and defect judgment data registered in the weld point table212A as the training data with each other in the welding process database211and the inspection process database212.

The learning model25is configured by, for example, a neural network (including deep learning). A welding condition and a welding execution record (that may be a part of data) are input to an input layer of the learning model25, and an output layer of the learning model25outputs defect inference data for the welding condition and the welding execution record thus input. The learning model25may output the inference result (binary classification) of judgment of whether there is a weld defect as the defect inference data or output the inference result (multi-value classification) of judgment of the type of weld defect together with the presence or absence of a weld defect as the defect inference data. In the case of handling the welding machine operation data12and the preheating data13as time-series data, the learning model25is preferably configured by a recurrent neural network. In addition, the method of machine learning is not limited to machine learning using a neural network, and another machine learning method may be employed. The other machine learning method is, for example, a tree type such as decision tree, ensemble learning, clustering, multivariate analysis, and support vector machine.

The machine learning module2021inputs input data configuring a learning data pair to the input layer of the learning model25, and then adjusts weighting factors associated between the input and output layers such that an evaluation value of an error function that compares defect inference data output from the output layer of the learning model25and training data configuring the learning data pair with each other become smaller, by using the error function. The machine learning module2021repeats the above-mentioned operation through use of a plurality of learning data pairs, and stores weight parameters at the time when a predetermined learning end condition is satisfied as the trained learning model25in the memory unit21, for example.

The defect inference module202J performs defect inference processing of inferring whether there is a weld defect at a weld point11aas an inference target and generating advice information including the inference result by inputting a welding condition and a welding execution record at the weld point11ato the learning model25(with its weight parameters adjusted) as the input data. The weld point11aas the inference target may be any weld point11corresponding to a record with a welding condition and a welding execution record registered in the weld point table211A of the welding process database211. Therefore, the defect inference processing is performed at any timing after a welding process is performed at the weld point11aas the target of inference.

FIG.23is a flowchart of an example of the defect inference processing.FIG.23shows an operation in the case in which the defect inference module202J performs the defect inference processing after Step S162or Step S164ofFIG.10.

First, in Step S901, the defect inference module202J receives the weld point11identified by the welding process target record as the weld point11aas the inference target, to thereby acquire the welding condition and the welding execution record registered in the welding process target record of the weld point table211A.

Next, in Step S902, the defect inference module202J inputs the welding condition and the welding execution record at the weld point11aas the inference target to the input layer of the learning model25, and causes the output layer of the learning model25to output defect inference data.

Next, in Step S910, the defect inference module202J refers to the welding process database211and identifies a record corresponding to the weld point11identified by the welding process target record in the weld point table212A. The defect inference module202J then registers the defect inference data output from the learning model25in the identified record.

Next, in Step S920, the output processing module205transmits the defect inference data output from the learning model25to the welder's terminal5A of the welder50A and the welding manager's terminal5B of the welding manager50B who manages the welder50A.

Then, in Step S921and Step S922, when receiving the defect inference data from the welding data processing device2, the welder's terminal5A and the welding manager50B display an inference result display screen based on the defect inference data.

As described above, the defect inference module202J infers whether there is a weld defect at the weld point11aas the inference target based on information registered in the welding process database211and the inspection process database212. The welder50A and the welding manager50B (who may be the site manager40or any of the workers50C to50E) determine, for example, necessity of repair welding or re-welding, priority of a radiographic test, or the like in response to display of the inference result display screen based on the inference result, and hence it is possible to speed up decision-making and improve the weld quality.

OTHER EMBODIMENTS

The present invention is not limited to the above-mentioned embodiment, and various modifications can be made without departing from the gist of the present invention. Further, all of the embodiments thus obtained are included in the technical idea of the present invention.

For example, the information processing module202has been described as including the modules202A to202J in the above-mentioned embodiment, as illustrated inFIG.5. However, the information processing module202may include only a part of the modules202A to202J. In addition, in the flowcharts for illustrating the processing executed by the modules202A to202J of the information processing module202, the execution order of steps may be appropriately changed, or a part of the steps may be omitted.

Moreover, the image data14has been described as being generated by the inspection machine7in the above-mentioned embodiment. Alternatively, the image data14may be generated by scanning a film on which radiation transmitted through a weld point is imaged with an image reading device such as a scanner.

Further, the input data of the learning model25in the machine learning module2021and the defect inference module202J has been described as the welding condition and the welding execution record at the weld point11in the above-mentioned embodiment. However, the input data of the learning model may be the image data14at the weld point11. In this case, the information processing module202may further include a machine learning module and a defect inference module separately from the machine learning module2021and the defect inference module202J in such a manner that the machining learning module causes a learning model to learn a correlation between input data and training data by machine learning based on a learning data pair configured by the image data14as the input data and the defect judgment data as the training data, and the defect inference module outputs defect inference data at the welt point11aas the inference target by inputting the image data14at the welding point11aas the input data to the learning model that has been trained.

REFERENCE SIGNS LIST