METHOD OF IDENTIFYING DENTAL CONSUMABLES EQUIPPED INTO A DENTAL TOOL MACHINE

Abstract

A method of identifying dental consumables including at least one of a dental blank (2) and a dental tool (3) equipped into a dental tool machine (1). The method includes: a step of colliding the dental tool (3) with the dental blank (2) or a dental blank holder of the dental tool machine (1) and a step of detecting a signal indicative of the collision. The method also includes a step of analyzing the detected signal through trained artificial intelligence; and a step of identifying the type and/or condition of at least one of the dental consumables based on the analysis.

Claims

1. A method of identifying dental consumables including at least one of a dental blank and a dental tool equipped into a dental tool machine, the method comprising the steps of colliding the dental tool with the dental blank or a dental blank holder of the dental tool machine: detecting a signal indicative of the collision, analyzing the detected signal through trained artificial intelligence; and identifying the type and/or the condition of at least one of the dental consumables based on the analysis.

2. The method according to claim 1, further comprising the steps of: receiving one or more information of the dental consumables respectively; recognizing the type and/or the condition of each dental consumable based on the received information; and verifying consistency of the recognition with the identification.

3. The method according to claim 2, wherein the information is an information tag that is an RFID tag, a OR code, or a bar code and said information is read in a reading step; and in the reading step an RF transceiver or an optical transceiver/receiver is accordingly used for reading the information tag.

4. The method according to claim 2, further comprising the steps of: receiving said information based on inputting into the dental tool machine said information on one or more dental consumables through a user-interface.

5. The method according to claim 2 further comprising: a step of inhibiting the machining responsive to determining that the verification is inconsistent.

6. The method according to claim 2, further comprising: a step of allowing the machining if the consumables are suitable for the machining and the verification is consistent.

7. The method according to claim 1, further comprising: a step of determining the correctness of the identification, and correcting the identification responsive to determining that a correction is necessary.

8. The method according to claim 7, further comprising: a step of inhibiting the machining responsive to determining that the identification is incorrect.

9. The method according to claim 7, further comprising: a step of allowing the machining responsive to determining that the consumables are suitable for the machining and the identification is correct.

10. The method according to claim 7, further comprising: a step of training the artificial intelligence based on the detected signal and the corrected identification.

11. The method according to claim 2, further comprising: a step of training the artificial intelligence based on the detected signal and the recognition.

12. The method according to claim 1, wherein in the analyzing step a Fourier transformation is applied to the signal to generate a frequency spectrogram comprising the spectrum of frequencies versus time.

13. The method according to claim 1, wherein the trained artificial intelligence is implemented through a computer-implemented algorithm based on a neural network.

14. The method according to claim 13, wherein in the analyzing step a trained convolutional neural network is used.

15. A computer-program comprising computer-readable codes for causing a computer based dental machining system to carry out the method steps according to claim 1.

16. A non-transitory computer-readable data storage medium storing a program which when executed by a computer system, causes the computer system to perform the steps of claim 1.

17. A dental machining system comprising: a dental tool machine which comprises: consumables including at least one of a dental blank and a dental tool; one or more driving units each movably holding at least one dental tool for machining a corresponding side of the dental blank; a dental blank holder for holding at least one dental blank relatively movably with respect to the dental tools; a detection device configured to detect a. signal indicative of a collision of the dental tool with the dental blank or the dental blank holder, means for implementing a trained artificial intelligence that is configured to analyze the signal and identify the type and/or the condition of at least one of the dental consumables based on the analysis; and a control device configured to control the dental machining system according to claim 1.

18. The dental machining system according to claim 17, wherein the detection device is further configured to detect the signal based on the sound of the collision, or the speed, the acceleration, the vibration of the respective dental tool, or the force, the torque acting on the respective dental tool or the supply current to a dental tool motor of the respective dental tool.

19. The dental machining system according to claim 17, further comprising: a reading device configured to read an information tag on the dental consumable, wherein the dental consumable has an RFID tag, a QR code, or a bar code.

20. The dental machining system according to claim 17, further comprising a user-interface for inputting information on a dental consumable.

21. The dental machining system according to claim 17, wherein the control device is further configured to initiate the collision before start of the machining, in a pause in the machining, or after the machining of the dental blank, and the detection device is further configured to detect the signal accordingly.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In the subsequent description, further aspects and advantageous effects of the present invention will be described in more detail by using exemplary embodiments and by reference to the drawings, wherein

[0020] FIG. 1—is a diagram showing the amplitude of the frequency distribution at an arbitrary instant in a signal detected through the method according to an embodiment of the present invention;

[0021] FIG. 2—is a frequency spectrogram showing the spectrum of frequencies with respect to time for a signal detected through the method according to an embodiment of the present invention;

[0022] FIG. 3—is a schematic partial view of a dental tool machine of a dental machining system according to an embodiment of the present invention.

[0023] The reference numbers shown in the drawings denote the elements as listed below and will be referred to in the subsequent description of the exemplary embodiments: [0024] 1. Dental tool machine [0025] 2. Dental blank [0026] 2a. Shaft [0027] 3. Dental tool [0028] 4. Driving unit [0029] 4a. Arm [0030] 4b. Shaft

[0031] FIG. 3 shows a dental tool machine (1) of a dental machining system according to an embodiment. The dental tool machine (1) comprises: consumables including a dental blank (2) and dental tools (3); two driving units (4) each movably holding a dental tool (3) for machining a corresponding side of the dental blank (2); a dental blank holder for holding the dental blank (2) relatively movably with respect to the dental tools (3); and a detection means for detecting a signal indicative of a collision of the dental tool (3) with the dental blank (2) or the dental blank holder. Each driving unit (4) has a shaft (4b) and an arm (4a) radially fixed to the shaft (4b). Each shaft (4b) can be moved in the z axis through a driving mechanism of the respective driving unit (4). Each arm (4a) can be moved around the z axis through the driving mechanism. The dental tools (3) are mounted to one or more tool motors in each arm (4a) respectively. The dental blank (2) is joined to a shaft (2a) which can be moved in the y axis and rotated around the y axis through another driving mechanism. The dental machining system further comprises means for implementing a trained artificial intelligence that is adapted to analyze the detected signal and identify the type and/or the condition of each dental consumable based on the analysis; and a control means adapted to control the dental machining system according to the method of the present invention. The present method can also be applied to dental tool machines which have a different kinematical structure as that illustrated in FIG. 3.

[0032] The method of the present invention serves the purpose of identifying the dental consumables including at least one of a dental blank (2) and a dental tool (3) equipped into the dental tool machine (1). The method comprises: a step of colliding the dental tool (3) with the dental blank (2) or a dental blank holder of the dental tool machine (1); a step of detecting a signal indicative of the collision; a step of analyzing the detected signal through trained artificial intelligence; and a step of identifying the type and/or the condition of at least one of the dental consumables based on the analysis. For the collision, the slowly rotating dental tool (3) is relatively moved against the dental blank (2) or the dental blank holder along the directions x, y, z defined through the axes of freedom as shown in FIG. 3.

[0033] In an embodiment, in the analyzing step a Fourier transformation is applied to the signal to generate a frequency spectrogram comprising the spectrum of frequencies versus time. FIG. 1 shows the amplitudes of the frequency distribution at an arbitrary instant in the signal detected during the collision. FIG. 2 shows a frequency spectrogram i.e., the spectrum of frequencies of the detected signal with respect to time. The frequency spectrogram serves as a fingerprint of the collision to identify the type and the condition of the consumables based on an empirical study for the available consumables on the market. The type of the consumable includes for example the material and the geometrical properties, and preferably the manufacturer. A database is used for the information on the manufacturer and the like. The condition of the consumable includes for example the degree of wear of the dental tool, and the shrinkage of the dental blank. The degree of wear can be shown in percentage. For instance, 100% indicates a new dental tool (3), and 0% indicates a completely worn dental tool (3).

[0034] In an embodiment, the collision noise is recorded with a microphone to generate the signal indicative of the collision. The signal is preferably fed to two (analog) filters connected in parallel: 1× high pass filter, 1× low pass filter. A comparator compares the level behind the low pass filter with the level behind the high pass filter. Depending on the output of the comparator, the material of the dental blank (2) may be classified as ceramic or plastic. Thus, in the step of analyzing the signal, high pass filters and low pass filters may be additionally or separately used in the framework of the trained artificial intelligence.

[0035] In an embodiment, the trained artificial intelligence is implemented through software, namely through a computer-implemented algorithm. The trained artificial intelligence may be alternatively implemented trough a hardware. The trained artificial intelligence is implemented through a trained neural network, preferably a trained convolutional neural network.

[0036] In an embodiment, the control means is further adapted to initiate the collision before start of the machining. Alternatively, or additionally, the control means may initiate the collision in a pause in the machining and/or after the machining of the dental blank. The detection means is further adapted to detect the signal accordingly.

[0037] In an embodiment, the detection means is adapted to detect the signal through an acoustic sensor based on the sound of the collision. Alternatively, the detection means may be adapted to detect the signal based on the speed, the acceleration, the vibration of the respective dental tool (3), or the force, the torque acting on the respective dental tool (3), or the supply current to the tool motor of the dental tool (3) respectively through a speed sensor, an acceleration sensor, a vibration sensor, a force sensor, a torque sensor, or a supply current sensor. The speed is preferably the speed of revolution of the dental tool (3). Herein, the speed, acceleration and the force may be measured along any of the x, y, z directions corresponding the degrees of freedom of the dental tool machine (1). Also reference/threshold values may be used in the detection for pre-processing the signal.

[0038] In an embodiment, the dental machining system may further comprise a reading means adapted to read an information tag on the dental consumable. In this embodiment, the dental consumable may have an RFID tag, a QR code, a bar code or the like. The information tag may include information on the specification of the dental consumable, such as the type, the manufacturer, the production date, material, geometry, current remaining service life, and the like. The reading means may be an RF transceiver, an optical transceiver, a camera or the like. In this embodiment, the method further comprises: a step of reading one or more information tags of the dental consumables; a step of recognizing the type and/or the condition of each dental consumable based on the read information; and a step of verifying consistency of the recognition with the identification. In this embodiment, the identification may be used to secure the recognition through the verification according to a scenario which will be explained later.

[0039] In an embodiment, the dental machining system further comprises a user-interface for inputting information on a dental consumable. The user interface is preferably located on the dental tool machine (1). Alternatively, the user interface may be implemented through a PC connected to the dental tool machine (1). In this embodiment, the method further comprises: a step of inputting into the dental tool machine (1) information on the dental consumables through the user-interface; a step of recognizing the type and/or the condition of each dental consumable based on the inputted information; and a step of verifying consistency of the recognition with the identification. Also, in this embodiment, the identification may be used to secure the recognition through the verification as mentioned above.

[0040] In an embodiment, for the purpose of securing the operation of the dental tool machine, in the framework of the above-mentioned scenario, the method further comprises a step of inhibiting the machining for security if the verification is inconsistent. In this embodiment, the method further comprises a step of allowing the machining if the consumables are suitable for the machining and, additionally, the verification is consistent.

[0041] In an embodiment, the user can check the identification and correct it, if necessary, via the user-interface. In this embodiment, the method further comprises a step of allowing an operator to check the correctness of the identification, and to correct the identification, if necessary.

[0042] In an embodiment, for the purpose of securing the operation of the dental tool machine, in the framework of the above-mentioned scenario, the method further comprises a step of inhibiting the machining for security if the identification is incorrect according to the user-check. In this embodiment, the method further comprises a step of allowing the machining if the consumables are suitable for the machining and, additionally, the identification is correct according to the user-check.

[0043] In an embodiment, the method further comprises a step of training the artificial intelligence based on the detected signal and the recognition. In an alternative embodiment, the method further comprises a step of training the artificial intelligence based on the detected signal and the corrected identification. Thereby, the training phase can be supervised and subjected to the correction through the user or operator. In the training phase, the frequency spectrograms serve as fingerprints of the collisions to identify the type and the condition of the consumables. Also, a database for the identity and specification of the available consumables may be used.