Classifications

• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
  • B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
  • B23K31/00—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by any single one of main groups B23K1/00 - B23K28/00
  • B23K31/12—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by any single one of main groups B23K1/00 - B23K28/00 relating to investigating the properties, e.g. the weldability, of materials
  • B23K31/125—Weld quality monitoring
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
  • B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
  • B23K9/00—Arc welding or cutting
  • B23K9/095—Monitoring or automatic control of welding parameters
  • B23K9/0953—Monitoring or automatic control of welding parameters using computing means
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
  • B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
  • B23K9/00—Arc welding or cutting
  • B23K9/095—Monitoring or automatic control of welding parameters
  • B23K9/0956—Monitoring or automatic control of welding parameters using sensing means, e.g. optical

Definitions

• the invention relates to the field of welding.
• the invention relates to the field of recognition of a conformal weld deposited along a welding path.
• Tube welding, buttering or additive manufacturing are operations that can be carried out automatically using a welding head.
• a welding path may, for example, be formed at a junction between two adjoining tubes or directly at a specific surface, such as at least part of an edge of a part for the purpose of buttering.
• This welding path circular or flat, is filled with molten metal.
• this filling is carried out in several passes, each pass corresponding to a 360° rotation around the components, for example metallic, of the welding electrode.
• Each pass allows molten metal to be deposited corresponding to at least one weld bead.
• each pass allows the weld beads to be deposited and superimposed on each other.
• the weld is analyzed by non-destructive or destructive testing to detect possible defects once the weld is complete, that is to say when all the passes have been made and the weld path has been filled.
• This corrective operation may require grinding the weld to a thickness corresponding to the location of the defect, and then making several passes in order to refill the welding path.
• the invention thus aims to ensure the conformity of the weld bead deposited after each pass.
• a method for learning to recognize a conforming weld deposited along a welding path, the method being implemented by means of a welding installation comprising: – a welding head movable along the welding path and capable of depositing a weld bead in the welding path, – means for acquiring at least one piece of data influencing at least one characteristic of the weld bead, – at least one means of measuring the position of the welding head, – a computer unit in which at least one computer program is implemented, capable of receiving and storing the at least one item of data acquired and associated with the measured position of the welding head, said method comprising: – an operation of moving the welding head along the welding path, – an operation of depositing a weld bead in the welding path, – an operation of acquiring at least one piece of data influencing at least one characteristic of the weld bead, this acquisition operation being carried out simultaneously with the operation of depositing a weld bead, – an operation of measuring the position
• the method further comprises:
• the learning method being characterized in that the preceding steps are repeated to deposit several conforming welds and to constitute a set of data representative of the conforming welds, and in that it further comprises an operation of calibrating the at least one computer program with the set of data representative of the compliant welds inserted into the computer unit to obtain a calibrated computer program, the calibrated computer program being configured to recognize a compliant weld.
• the learning method according to the invention is divided into a learning phase and a monitoring phase.
• the method refines a predictive model based on operational data obtained as described later for a so-called compliant weld (hereinafter referred to as a subset of operational data).
• the learning method calculates an approval score based on a deviation or gap of the operational data from the subset of operational data observed during monitoring compared to a prediction result obtained by the predictive model. Then, from this learning score, it is possible to inform, for example, the operator performing the welding of the status of the weld in progress, and to display associated information.
• the calibration operation corresponding to the learning phase of the method, consists of a calibration operation of the predictive model aimed at predicting the conformity of a weld based on the subset of operational data associated with the corresponding conforming weld.
• the calibration operation is preferably implemented by a computer.
• each conforming weld is associated with a subset of data representative of the corresponding conforming weld.
• Each subset of operational data of a weld is associated with at least two distinct operational data each influencing at least one characteristic of a weld bead of the corresponding conforming weld.
• each of said two operational data is associated with a weighting index, and at least the position of the welding electrode corresponding to the data, thus forming structured operational data.
• each structured data comprises an association of at least the corresponding data, a weighting index associated with said data and the position of the welding electrode corresponding to said data.
• these can form two structured operational data, namely a first structured operational data and a second structured operational data, the first structured operational data associating the first operational data, a weighting index corresponding to the first operational data and the position of the welding electrode corresponding to the first operational data, and the second structured operational data associating the second operational data, a weighting index corresponding to the second operational data and the position of the welding electrode corresponding to the second operational data.
• the weighting index of each of said two operational data may be dependent on a variation in the value of the corresponding data.
• the weighting index of each of said two operational data can be adjusted according to the approval score of the predictive model.
• the prediction error is obtained from the prediction result of the prediction model.
• the prediction error of a cycle is advantageously used to adjust the weighting indices of each of said two operational data for the following cycle.
• the predictive model is trained from said structured operational data corresponding to a compliant weld.
• the method comprises a phase of monitoring a weld in progress, the monitoring phase comprising a step of acquiring in real time said subset of operational data of said weld in progress.
• the monitoring phase comprises determining in real time, from the trained predictive model, the approval score of the current weld based on said subset of operational data of said current weld.
• An approval score obtained within a predetermined value range ensures that the weld in progress is compliant, while an approval score outside this value range identifies a deviation or gap of at least one data item of said subset of operational data of said weld in progress.
• the monitoring phase includes a step of displaying information representative of the deviation or gap.
• the operator supervising the weld in progress can then carry out an operation to check and correct the weld in progress.
• the learning process thus guarantees the success of the conformity of the weld in progress. It follows that no subsequent control operation is necessary. In other words, it will be understood that the weld does not need to be analyzed by control, non-destructive or destructive, to detect possible defects once the weld is finished, unlike known methods.
• a conforming weld is a weld that meets predetermined criteria.
• the predetermined criteria may be determined by a specification, or by one or more specific standards.
• Predetermined criteria include the absence of certain defects such as spheroidal blowholes, blowhole nests, aligned blowholes, metal inclusions, connection defects, fusion gaps, bonding gaps, weld bead thicknesses that are too thin or too thick compared to a reference value.
• conforming welds is meant a plurality of welds each weld of which satisfies predetermined criteria distinct from another weld of that plurality of welds.
• a welding head means a fusible electrode or a refractory electrode used with or without welding wire.
• the welding head may be connected to a welding generator which is typically a power supply generator.
• the at least one piece of data influencing at least one characteristic of the weld bead may take the form of a value or several values which may be intrinsic to the elements used to deposit the weld bead and/or be representative of one or more measured, programmed and/or calculated parameters, and/or linked to the environment.
• characteristics of the weld bead include its thickness, surface condition, volume, position in the welding path, mechanical properties and chemical composition.
• the learning method does not include a step of inserting, into the computer unit, data representative of non-compliant welds, for example welds comprising a weld bead comprising a defect.
• the at least one data item is the voltage and/or current delivered by a welding generator connected to the welding head; – at least one data is the speed of movement of the welding head; – at least one piece of data is the ambient temperature; – at least one piece of data is the ambient humidity; – at least one piece of data is wind speed; – at least one data is the temperature of the weld bead; – at least one data item is the volume or mass flow rate of a shielding gas intended to prevent oxidation of the molten metal by oxygen in the air; – at least one data is the pressure of a shielding gas intended to prevent oxidation of the molten metal by the oxygen in the air; – at least one data item is the temperature of a shielding gas intended to prevent oxidation of the molten metal by the oxygen in the air; – at least one data is the profile of the weld bead and/or the profile of the adjacent areas.
• an automated method for recognizing a conforming weld bead deposited or in the process of being deposited along a welding path this method using the lessons of a prior learning method as previously described, the automated recognition method being implemented by means of a welding installation comprising: – a welding head movable along the welding path and capable of depositing a weld bead in the welding path, – means for acquiring at least one piece of data influencing at least one characteristic of the weld bead, – at least one means of measuring the position of the welding head, – a computer unit in which at least one computer program is implemented and in which the data acquired and associated with the measured positions of the welding head are inserted, the computer program being calibrated by means of said learning method to ensure the conformity of the weld bead being deposited, said automated recognition method comprising: – an operation of moving the welding head along the welding path, – an operation of depositing a weld bead in the welding path, – an operation of acquiring at
• the above automated learning and recognition methods can be implemented in the context of depositing a weld between at least two components, for example metallic, defining the welding path or even in the context of depositing a weld directly on a specific surface and in a predetermined direction defining the welding path, or for example during buttering or during the implementation of additive manufacturing.
• This learning process is described in the context of welding two metal tubes. However, it should be noted that this learning process may be suitable for welding between two metal components of different shapes and/or nature.
• This process uses a welding installation.
• the welding installation comprises a welding head and a device for feeding the metal to be welded.
• the head may be a consumable electrode connected to a welding generator, which is typically a supply current generator, and which makes it possible to create an electric arc intended to melt the metal to be welded.
• the installation includes means for measuring the position of the welding head.
• the position of the welding head is understood to mean its coordinates in a given reference frame.
• the reference frame is materialized by a mark on one of the two tubes to be welded.
• the welding system is used to weld two tubes together. First, two tubes are joined together. The junction between these two tubes forms a circular welding path. To fix these tubes together, molten metal is deposited in the welding path.
• the installation is therefore able to deposit several weld beads in the welding path.
• the installation includes means of acquiring data influencing a characteristic of the weld bead.
• the installation includes a means of measuring the position of the welding head.
• the installation comprises a computer unit. At least one computer program is implemented in the computer unit.
• the computer unit is capable of receiving and storing the data acquired by the acquisition means and associated with the positions measured by a measuring means.
• the computer unit stores in real time the data acquired and associated with the positions of the welding head.
• Data influencing a characteristic of the weld bead is systematically associated with the corresponding position of the welding head.
• the learning phase consists of launching a first welding operation between two tubes.
• the first welding operation includes several passes so as to deposit several weld beads in order to fill the welding path with molten metal.
• the first welding operation consists of making a weld, called a complete weld, between two metal tubes.
• the learning method includes an operation of moving the welding head along the welding path. This operation is carried out by means of the moving device.
• the method comprises an operation of depositing a weld bead in the welding path. At least one weld bead is deposited per pass.
• the operation of depositing a weld bead is carried out as the head moves along the welding path.
• the method comprises an operation of measuring several data influencing at least one characteristic of the weld bead.
• the method comprises at the same time a measurement of the position of the welding head in the reference frame.
• the method comprises an operation of storing the data acquired and associated with the measured positions of the welding head, this storing being carried out in the computer unit.
• a destructive or non-destructive control operation of the weld is carried out.
• an evaluation operation of the controlled weld is carried out in order to establish a compliant weld.
• This operation can be carried out manually.
• An X-ray for example, can be carried out in order to obtain an image of the depth of the weld. Other techniques can be used.
• the X-rays are analyzed by an operator who visually detects welding defects.
• the weld checked and evaluated is considered to be a compliant weld.
• the operator enters into the computer unit the data acquired and associated with the measured positions of the welding head representative of each weld bead deposited.
• the computer unit then associates with each weld bead contained in the conforming weld the data acquired and associated with the measured positions of the welding head.
• the previous steps are repeated to deposit several conforming welds and to constitute a set of data representative of the conforming welds.
• a calibration operation of the at least one computer program with the set of data representative of the compliant welds inserted into the computer unit is carried out.
• This calibration operation aims to obtain a calibrated computer program, the calibrated computer program then being configured to recognize a compliant weld.
• data influencing at least one characteristic of the welding bead are the voltage and the intensity delivered by the welding generator.
• the intensity is measured at the terminals of the generator.
• the voltage is measured on the displacement device.
• the intensity and voltage determine the quality of the weld. More precisely, the weld pool is likely not to melt the surrounding metal or, on the contrary, to collapse.
• a data influencing at least one characteristic of the weld bead is the speed of movement of the welding electrode. This speed is measured by a sensor which continuously measures the speed of movement of the welding electrode.
• Travel speed is an important factor in weld bead deposition. High travel speeds cause significant mechanical stresses that can cause cracks to appear in the weld bead.
• a data influencing at least one characteristic of the weld bead is the ambient temperature.
• This temperature is provided by a sensor capable of measuring the ambient temperature.
• Ambient temperature affects weld quality. When the temperature is below a given temperature, welding defects may occur.
• one piece of data influencing at least one characteristic of the weld bead is the ambient humidity.
• the ambient humidity is provided by a sensor capable of measuring it.
• Ambient humidity has an impact on the quality of the weld. Measuring humidity, together with measuring temperature, allows the dew point to be determined. The dew point is likely to affect the quality of the weld.
• a data influencing at least one characteristic of the welding bead is the wind speed at the welding electrode. This speed is measured by means of a speed sensor.
• Wind speed near the welding electrode can have an impact on the quality of the weld. It is particularly the cause of porosity, oxidation and blowholes in the weld.
• a data influencing at least one characteristic of the weld bead is the temperature of the weld bead. This temperature is measured by means of a temperature sensor.
• the temperature of the weld bead has an impact on the quality of the weld.
• data influencing at least one characteristic of the weld bead is the volume or mass flow rate of a shielding gas intended to prevent oxidation of the molten metal by the oxygen in the air.
• the flow rate of the shielding gas has an impact on the quality of the weld. In particular, it is the cause of porosity, oxidation and blowholes in the weld.
• one piece of data influencing at least one characteristic of the weld bead is the pressure of the shielding gas.
• the pressure of the shielding gas has an impact on the quality of the weld.
• one piece of data influencing at least one characteristic of the weld bead is the temperature of the shielding gas.
• the temperature of the shielding gas has an impact on the quality of the weld.
• a data influencing at least one characteristic of the weld bead is the profile of the weld bead and/or the profile of the adjacent zones.
• Adjacent zones are understood to mean the zones located near the weld bead.
• the profile provides useful information about the quality of the weld.
• the invention relates to a method for automated recognition of a conforming weld bead.
• This method comprises an operation of moving the welding electrode along the welding path.
• the method comprises simultaneously an operation of depositing a weld bead in the welding path.
• the method comprises an operation of measuring at least one piece of data influencing at least one characteristic of the weld bead and simultaneously an operation of measuring the position of the welding electrode in the reference frame.
• the method comprises an operation of detecting a deviation of the at least one acquired data item and associated with its measured position with respect to the data representative of the compliant welds defined by the calibrated computer program.
• the detection operation is therefore notably carried out using the calibrated computer program.
• the automated recognition method further comprises an alert operation for the attention of an operator.
• the operator can suspend the welding operation at a juvenile stage, i.e. before the welding operation is completed and thus remove the thin thickness of weld characteristic at least in part of the non-compliant, or possibly non-compliant, weld bead for which a deviation of the at least one acquired data item and associated with its measured position with respect to the data representative of the compliant welds has been detected.

Landscapes

• Engineering & Computer Science (AREA)
• Mechanical Engineering (AREA)
• Physics & Mathematics (AREA)
• Plasma & Fusion (AREA)
• Theoretical Computer Science (AREA)
• Quality & Reliability (AREA)
• Image Analysis (AREA)
• Butt Welding And Welding Of Specific Article (AREA)

Applications Claiming Priority (2)

Publications (1)

Family

ID=86942297

Family Applications (1)

Country Status (3)

Families Citing this family (1)

Family Cites Families (2)

• 2023
  • 2023-02-15 FR FR2301422A patent/FR3145695B1/fr active Active
• 2024
  • 2024-02-14 WO PCT/EP2024/053735 patent/WO2024170628A1/fr not_active Ceased
  • 2024-02-14 EP EP24704214.6A patent/EP4665529A1/de active Pending

Also Published As

Similar Documents

Legal Events