Classifications

• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B33—ADDITIVE MANUFACTURING TECHNOLOGY
  • B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
  • B33Y10/00—Processes of additive manufacturing
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B22—CASTING; POWDER METALLURGY
  • B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
  • B22F10/00—Additive manufacturing of workpieces or articles from metallic powder
  • B22F10/20—Direct sintering or melting
  • B22F10/28—Powder bed fusion, e.g. selective laser melting [SLM] or electron beam melting [EBM]
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B22—CASTING; POWDER METALLURGY
  • B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
  • B22F10/00—Additive manufacturing of workpieces or articles from metallic powder
  • B22F10/30—Process control
  • B22F10/37—Process control of powder bed aspects, e.g. density
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B22—CASTING; POWDER METALLURGY
  • B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
  • B22F10/00—Additive manufacturing of workpieces or articles from metallic powder
  • B22F10/80—Data acquisition or data processing
  • B22F10/85—Data acquisition or data processing for controlling or regulating additive manufacturing processes
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B22—CASTING; POWDER METALLURGY
  • B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
  • B22F12/00—Apparatus or devices specially adapted for additive manufacturing; Auxiliary means for additive manufacturing; Combinations of additive manufacturing apparatus or devices with other processing apparatus or devices
  • B22F12/60—Planarisation devices; Compression devices
  • B22F12/67—Blades
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B22—CASTING; POWDER METALLURGY
  • B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
  • B22F12/00—Apparatus or devices specially adapted for additive manufacturing; Auxiliary means for additive manufacturing; Combinations of additive manufacturing apparatus or devices with other processing apparatus or devices
  • B22F12/90—Means for process control, e.g. cameras or sensors
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B33—ADDITIVE MANUFACTURING TECHNOLOGY
  • B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
  • B33Y30/00—Apparatus for additive manufacturing; Details thereof or accessories therefor
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B33—ADDITIVE MANUFACTURING TECHNOLOGY
  • B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
  • B33Y50/00—Data acquisition or data processing for additive manufacturing
  • B33Y50/02—Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
  • Y02P10/00—Technologies related to metal processing
  • Y02P10/25—Process efficiency

Definitions

• the present invention relates to the general field of the manufacture of parts by additive manufacturing by selective sintering or selective melting on a powder bed, and more particularly to the detection and localization of anomalies during the spreading of the powder.
• the manufacturing processes by selective melting or selective sintering on a powder bed comprise a step during which is deposited and spread by a scraper, on a production plate, a first layer of powder of a metal, of a metal alloy or of ceramic of controlled thickness; then a step consisting in heating a predefined zone of the powder layer. These steps are then repeated for each additional layer of powder deposited and spread.
• the spreading of the powder by the scraper is an important step in the production. Indeed, the quality of the powder layer applied partly determines the quality of the part resulting from the manufacture. A bad powder spreading can, if it is not corrected, lead to a stop of the machine carrying out the manufacture and / or to the manufacture of an unconfirmed part, or even to material defects not detected in the part thus manufactured.
• SA Schevchik et al. proposed in the article "Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks” to use the sound waves emitted during manufacturing to detect anomalies to quantify the rate of porosity in the material and thus determine the material quality after manufacture.
• L. Scime and J. Beuth have proposed in the article "A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process” to acquire images during manufacturing to detect anomalies when spreading the powder. These images also make it possible to classify the anomalies according to their type: irregular spreading, incomplete spreading, bumps or hollows in the powder, etc.
• the invention relates to a device for manufacturing a part by a method of selective melting or selective sintering on a powder bed comprising a production plate having a work surface, parallel to a first direction and to a second direction, on which the part is intended to be manufactured, a scraper placed on the work surface and able to move and spread the powder in the first direction on the work surface, characterized in that it further comprises:
• a laser range finder oriented in the first direction and able to determine a position of the scraper in the first direction
• a control system capable of detecting an anomaly as a function of said acoustic signal and of determining a position of the anomaly as a function of the acoustic signal detected by said acoustic sensors and of a position signal measured by said laser range finder.
• the device of the invention makes it possible to detect and precisely locate a contact between the scraper and the bed of powder during the spreading of the powder by the scraper, the contact possibly being an anomaly. In fact, during contact between the powder and the scraper, acoustic waves are emitted and propagate in the scraper.
• the acoustic sensors detect this acoustic signal, and the position of this contact in the second direction is determined by the time lag between the detection of the acoustic signal by the first sensor and the detection of the same acoustic signal by the second acoustic sensor. Thanks to the laser range finder, it is possible to determine the actual position of the scraper in the first direction. The control system is then able to precisely locate the contact and therefore the anomaly on the work surface. He is also able to assess whether this contact is indeed an anomaly.
• the device of the invention can also detect anomalies resulting from an irregular spreading of the powder (hollow or hole in the powder bed). In fact, in this case, no acoustic wave will be detected by the acoustic sensors, which means that no contact has taken place between the scraper and the powder. It will then be up to the control system to determine if this lack of contact is normal or if it is the result of poor spreading of the powder.
• the anomaly is more particularly localized in a system with three coordinates in the first, second and third directions.
• the first coordinate which corresponds to the first direction is determined by virtue of the position signal measured by the laser range finder.
• the second coordinate which corresponds to the second direction is determined by the time lag between the detections of the acoustic signal from the two acoustic sensors.
• the third coordinate which corresponds to a third direction is determined by the number of layers of powder spread by the scraper.
• the laser range finder is fixed to the scraper.
• the laser range finder is fixed to the scraper by gluing or by a mechanical connection.
• each acoustic sensor is fixed to the scraper by gluing or by mechanical connection.
• the manufacturing device comprises two acoustic sensors each fixed to one end of the scraper.
• control system is a machine learning system.
• the machine learning system is then able to give the precise localization of the anomalies and thanks to the acoustic signals detected by the acoustic sensors, it is able to learn to classify the anomalies according to their severity according to the characteristics of these signals. These characteristics are for example the amplitude, the duration or the waveform of the signal.
• the machine learning system Thanks to the classification of the anomalies by the machine learning system, it is also possible to know whether the anomaly results in damage to the scraper or not, and whether it is necessary to stop the manufacture of the part to repair or change the scraper. .
• the machine learning system is a neural network, and more particularly is a convolutional neural network.
• the machine learning system is a system of decision trees or a forest of decision trees.
• the machine learning system is based on probabilistic modeling.
• the machine learning system is based on methods using kernels.
• the machine learning system is based on a gradient amplification algorithm.
• Neural networks have the advantage of allowing supervised learning. They will be able to do all the work of extracting the data and description of anomalies. During the training phase, the classification error will be minimized in order to optimize the classification parameters.
• the specific architecture of the neural network makes it possible to extract signatures of the anomaly of different complexities, from the simplest to the most complex.
• the automatic extraction and prioritization of signatures which adapt to the given problem, is one of the strengths of convolutional neural networks.
• Another object of the invention is a method for detecting an anomaly implemented by a device according to the invention comprising the following steps:
• the position of the anomaly being determined in the first direction by the position signal measured by the laser range finder, in the second direction by a time shift of the acoustic signal detected by the at least two acoustic sensors and in a third direction by a number of layers of powder spread by the scraper;
• Determining the position of the anomaly in three directions makes it possible to locate the anomaly in a three-dimensional space, and not only in a two-dimensional space as in the prior art. This may possibly be able to correct the anomaly quickly.
• the classification of the anomaly according to its seriousness also makes it possible to know whether a production stop is necessary or not, if the scraper is damaged or even if the final part will contain acceptable material defects or not.
• Another subject of the invention is a method of manufacturing a part by selective melting or selective sintering on a powder bed comprising a step of detecting anomalies by the detection method according to the invention.
• FIG. 1 represents a device for manufacturing a part by selective melting or selective sintering on a powder bed according to one embodiment of the invention.
• FIG. 2 represents a scraper of the manufacturing device according to one embodiment of the invention.
• FIG. 1 represents a device 100 for manufacturing a part by selective melting or selective sintering on a powder bed comprising a production plate 101 having a work surface 102 on which the part is manufactured.
• the device 100 also comprises a scraper 110 which makes it possible to spread the powder 103 in the X direction by moving on the work surface 102.
• the scraper 110 thus moves in a plane formed by the axes (X, Y) and the build plate 101 as well as the work surface 102 are parallel to this plane.
• the number of layers of powder deposited and spread defines the thickness of the final part along the Z axis.
• areas 160 of the working surface 102 are heated by an energy source, such as a laser beam or an electron beam, so as to sinter or merge powder in these areas 160.
• an energy source such as a laser beam or an electron beam
• the device 100 In order to detect anomalies 150 resulting from contact or from an absence of contact between the powder and the scraper 110, the device 100 also comprises a laser range finder 130 and two acoustic sensors 121 and 122.
• the laser range finder 130 is fixed to one end 170 of the device 100 and is oriented towards the scraper 110 in the X direction.
• the range finder 130 makes it possible to measure the position of the scraper 110 in the X direction, which makes it possible to determine the position of a possible anomaly 150 in the same direction X.
• the two acoustic sensors 121 and 122 are fixed on the scraper 110. They are spaced from each other in the Y direction. When contact takes place between the scraper 110 and the powder 103 spread on the work surface 102, acoustic waves are emitted. and propagate in the scraper 110. By propagating in the scraper, these acoustic waves are detected by the two acoustic sensors 121 and 122. Thanks to the data transmitted by the acoustic sensors 121 and 122, a control system 140 identifies or not a anomaly. These same data also make it possible to determine the position of the anomaly 150 in the direction Y. Indeed, thanks to the time shift between the detections of the acoustic wave by the two sensors 121 and 122, it is possible to go back to the position of the contact. and therefore the anomaly 150 in the Y direction.
• the control system 140 determines the position of the anomaly 150 in the X direction, which is the position of the scraper 110 in this same X direction measured by the laser range finder 130.
• the determination of the position of the anomaly 150 in the Z direction by the control system 140 is given by the number of layers of powder 103 deposited and spread on the production plate 101.
• the laser range finder 130 and the acoustic sensors 121 and 122 can be attached to the device 100 by gluing or by a mechanical connection.
• the acoustic sensors 121 and 122 are placed respectively at 1/3 and 2/3 of the length of the scraper 110 in the direction Y.
• the control system 140 can be a machine learning system.
• the system 140 can thus learn to classify the acoustic waveforms detected by the acoustic sensors 121 and 122 contacts, to determine whether the contact, or the absence of contact is an anomaly 150 and then classify this anomaly 150 according to its gravity.
• an anomaly 150 may be the result of a gas bubble, a hole in the spread powder, an elevation of the powder, an incomplete spread of the powder, traces in the spreading or even irregular spread. These anomalies do not present the same level of severity. Usually, the most serious anomalies result from powder elevation, traces in spreading, and uneven spreading in the (X, Y) plane of the work surface 102. Classification according to the severity of the anomalies allows the control system 140 to give or predict the risk of damage to the scraper, to the final part and to decide whether or not production should be interrupted.
• the classification of the detected anomalies 150 can be done according to the characteristics of the detected waves: waveform, amplitude, duration, etc ...
• the machine learning system is chosen from among a convolutional neural network, a system based on probabilistic modeling, a system based on a kernel algorithm, decision trees, a forest of 'decision trees or a gradient implementation system.
• a convolutional neural network will, for example, make it possible to have an automatic extraction and prioritization of the contacts and non-contacts detected in order to classify the anomalies according to their severity.
• FIG. 2 represents a scraper 210, and more particularly the arrangement of the acoustic sensors 221 and 222 and of the laser range finder 230 according to another embodiment of the invention.
• the two acoustic sensors 221 and 222 are still fixed on the scraper 210 and spaced apart in the Y direction. So as to obtain a significant time shift to determine the position of the anomaly 250 according to the direction Y, the acoustic sensors 221 and 222 are each placed on one end of the scraper 210.
• the laser range finder 230 is fixed on the scraper 210 and is always oriented in the direction X to determine the position of the scraper 210 and of the anomaly 250 in this same direction X.
• the laser range finder 230 is placed in the middle of the scraper 210 between the two acoustic sensors 221 and 222.
• the two acoustic sensors 221 and 222, as well as the laser range finder 230 can be attached to the scraper 210 by gluing or by a mechanical connection.
• acoustic waves propagate in the scraper 210.
• the acoustic sensor 221 detects these waves at the instant t 0 + a, while the acoustic sensor 222 detects these same waves at the instant to + a + b. It is the temporal shift between the two instants of detection b which makes it possible to determine the position of the contact (and of the possible anomaly 250) in the direction Y, the positions of the two acoustic sensors 221 and 222 being known.
• the stopping of the propagation of acoustic waves in the scraper can also be detected and the possible anomaly at the origin of this stopping will also be localized thanks to the time lag between the acoustic sensors.
• the manufacturing device can also include more than three acoustic sensors. Indeed, if the acoustic sensors are too far apart, one of the sensors could not perceive the acoustic wave propagating in the scraper, or could perceive a wave already disturbed by the arrival of a new wave.
• the use of three or four sensors makes it possible to remedy this, because it is thus possible to place the sensors at closer intervals, while maintaining a sufficient spacing between at least two sensors to detect a significant time shift for the determination of the position. of the anomaly.
• the device comprises three acoustic sensors placed respectively at 1/4, 2/4 and 3/4 of the length of the scraper in the Y direction.
• Another object of the invention is a method for detecting an anomaly during the spreading of the powder by the scraper, the method being implemented by one of the devices presented above.
• the method firstly comprises the detection of an acoustic signal by the acoustic sensors, then the determination of the position of the anomaly, and finally the classification of the anomaly according to its severity.
• the position of the anomaly is determined:
• control system which determines whether the detected acoustic wave or the absence of a wave really results from an anomaly, then which determines the position of this anomaly and finally which classifies this anomaly according to its severity.
• Another subject of the invention is a method of manufacturing a part by selective melting or selective sintering on a powder bed comprising a step of detecting anomalies by the detection method described above.

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• Engineering & Computer Science (AREA)
• Chemical & Material Sciences (AREA)
• Materials Engineering (AREA)
• Manufacturing & Machinery (AREA)
• Automation & Control Theory (AREA)
• Analytical Chemistry (AREA)
• Physics & Mathematics (AREA)
• Plasma & Fusion (AREA)
• Powder Metallurgy (AREA)
• Length Measuring Devices Characterised By Use Of Acoustic Means (AREA)
• Lining Or Joining Of Plastics Or The Like (AREA)

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• 2020
  • 2020-06-19 FR FR2006426A patent/FR3111574B1/fr active Active
• 2021
  • 2021-06-09 US US18/002,085 patent/US20230219140A1/en active Pending
  • 2021-06-09 EP EP21736635.0A patent/EP4168196A1/de active Pending
  • 2021-06-09 WO PCT/FR2021/051038 patent/WO2021255367A1/fr unknown

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