Dental machining system for predicting the wear condition of dental tools

Abstract

The present invention relates to a dental machining system for manufacturing dental restorations, comprising a dental tool machine (1), the dental tool machine (1) comprising a dental blank holder for holding at least one dental blank (2) movable relative to one or more dental tools (3), one or more drive units (4) for movably holding each of the at least one dental tool (3) for machining the dental blank (2), and a control unit for controlling the dental blank holder and the drive unit (4) based at least on a time trajectory of the dental tool (3) relative to the dental blank (2) and a spatial amount of material removal from the dental blank (2) along the time trajectory, the control unit being further adapted to execute a primary trained artificial intelligence algorithm adapted to predict mechanical quantities based on the time trajectory and the spatial amount of material removal, where the mechanical quantities correspond to one or more physically measurable quantities associated with each dental tool (3) along the time trajectory, and a secondary trained artificial intelligence algorithm adapted to predict a wear state of the dental tool (3) based on the predicted mechanical quantities.

Description

[Technical field]

[0001] The present invention relates to a dental machining system for producing a dental restoration from a dental blank by using one or more dental tools and a control method thereof, and more particularly to a method for predicting the wear state of a dental tool in a dental machining system. [Background technology]

[0002] Generally, a dental machining system has a dental tool machine for machining a dental blank, typically made of ceramic. The dental tool machine generally has one or more drive units each movably holding at least one dental tool for machining a respective side of the dental blank. The dental tools are each mounted to a tool motor in the drive unit. The dental blank is mounted to a dental blank holder that is movable relative to the dental tools. Generally, CAD / CAM software is used to digitally construct the dental restoration and provide the corresponding machining data to the dental tool machine. The CAD / CAM software typically runs on a processing unit, such as a PC, in the dental machining system. A control unit controls the dental blank holder and the drive units based at least on a time trajectory of the dental tool relative to the dental blank and a spatial amount of material removal from the dental blank along the time trajectory.

[0003] Dental blanks and dental tools are consumables of the dental tool machine. Dental consumables available on the market vary in size, shape and material. The dental tool machine must be equipped by the operator with the correct dental blank and the correct dental tool that matches the specific requirements of the machining. When equipmenting the dental tool machine, the operator usually selects the correct dental consumable from a database via a user interface managed by a CAD / CAM software that acts in part as an aid to recognize the consumables. Thereby, information about the dental consumables equipped to the dental tool machine is also entered in and recognized by the dental tool machine. Alternatively, each dental consumable is recognized by a reading means that reads an information tag of the dental consumable that also acts as an aid to recognize the consumable. The information tag may be, for example, an RFID tag, a QR code, a bar code, or the like. The reading means may be an RF transceiver or an optical transceiver, a camera, or the like.

[0004] Machining can also be performed with a partially worn dental tool rather than a new one. Generally, the wear state of the dental tool is indicated as a percentage. For example, 100% indicates that the dental tool is practically new, 0% indicates that the dental tool is completely worn and therefore at the end of its useful life. When the dental tool machine is equipped with a new dental tool, the wear state of the dental tool is usually predicted by the dental machining system after the machining is finished and managed in a database. The actual wear state of the dental tool can usually be known before the machining starts, through a reference to the database when equipping the dental tool machine, or through any of the auxiliary means mentioned above. Different prediction methods are known. According to a commonly known prediction method, the wear state of the dental tool is predicted based on the number of machined dental restorations. However, this prediction method is very inaccurate, since the restoration type and the restoration geometry may change. Furthermore, this method does not take into account the space load on the dental tool. According to another commonly known prediction method, a dental machining system is provided with sensors to measure the load on the dental tool during machining, and the wear state of the dental tool is predicted based on the load on the dental tool after machining is completed. However, the use of load sensors increases the cost of the dental machining system.

[0005] The dental tool must be new or have a sufficient idle life before the start of machining. Furthermore, the use of the dental tool must be terminated in a timely manner before it is completely worn out. If the use of the dental tool is terminated early, unnecessary maintenance costs will be incurred. If the use of the dental tool is terminated late, the product quality and safe operation of the dental tool cannot be guaranteed. In the worst case, the dental tool may exceed its entire useful life or remaining useful life. In such cases, the dental tool may break and the dental restoration may be damaged. In such cases, the dental tool machine, especially the bearings, may also be damaged. In less serious cases, the machining of the dental restoration is completed with an unacceptably low quality due to the use of a very worn dental tool. In all these cases, user satisfaction may decrease.

[0006] Reference is made to Lei Xiao-Bao et al.: "Design of a two-spindle CNC machine tool applications for dental restoration", Third international conference on measuring technology and mechatronics automation, 2011, Pages 934-937, ISBN 978-1-4244-9010-3. US2019 / 0258222A1 discloses optimal machining parameter selection using a data-driven tool life modeling approach.

[0007] Reference is made to Cao Hongrui et al.: “The concept and progress of intelligent spindles: A review”, International journal of machine tool and manufacture, 2017, Vol. 112, Pages 21-52, ISSN: 0890 / 6955.

[0008] Reference is made to Sara Karam et al., "Online prediction of cutting tool life in turning via cognitive decision making", 48th CIRP-Conference on manufacturing systems, 2016, Vol. 41, Pages 927-932, ISSN: 2212-8271.

[0009] Reference is made to P. Palanisamy et al.: "Prediction of tool wear using regression and ANN models in end-milling operation", International journal of advanced manufacturing technology, 2008, Vol. 7: 1-2, pp. 29-41, ISSN: 1433 / 3015. Summary of the Invention

[0010] An object of the present invention is to overcome the above-mentioned problems of the prior art and to provide a dental machining system and a control method thereof that can accurately predict the wear state of a dental tool without using a load sensor.

[0011] This object is achieved through a dental machining system according to claim 1 and a control method according to claim 10. The subject matter of the other claims relates to further developments.

[0012] The present invention provides a dental machining system for manufacturing dental restorations. The dental machining system comprises a dental tool machine, the dental tool machine comprising a dental blank holder for holding at least one dental blank movable relative to one or more dental tools, one or more drive units for respectively movably holding at least one dental tool for machining the dental blank, and a control unit for controlling the dental blank holder and the drive units based at least on a time trajectory of the dental tool relative to the dental blank and a spatial amount of material removal from the dental blank along the time trajectory. The control unit executes a primary trained artificial intelligence algorithm to predict mechanical quantities based on the time trajectory and the spatial amount of material removal from the dental blank, where the mechanical quantities correspond to one or more physically measurable quantities associated with the respective dental tool along the time trajectory. The control unit further executes a secondary trained artificial intelligence algorithm to predict a wear state of the dental tool based on the predicted mechanical quantities.

[0013] The main advantageous effect of the present invention is that the dental machining system can accurately predict the wear state of the dental tool through the primary and secondary trained artificial intelligence algorithms without using load sensors during the estimation, which can provide customers with a dental tool machine that does not essentially require sensors to detect the load of the dental tool for wear prediction, thus improving the cost-performance ratio.

[0014] According to an embodiment of the present invention, the dental machining system is preferably further provided with a first auxiliary means for recognizing the type of dental blank and the type of dental tool, and a second auxiliary means for recognizing the actual wear state of the dental tool before starting machining. Thereby, the dental machining system can be more flexibly used with a variety of different dental blanks and dental tools, which may be new or partially worn. In this embodiment, the control unit executes a primary trained artificial intelligence algorithm to predict the mechanical quantity based on the time trajectory and the spatial amount of material removal, the type of dental blank, the type of dental tool, and the actual wear state of the dental tool before starting machining. And the control unit executes a secondary trained artificial intelligence algorithm to predict the wear state of the dental tool based on the predicted mechanical quantity and the actual wear state of the dental tool before starting machining. Thereby, the prediction accuracy and adaptability of the dental machining system are further improved.

[0015] According to one embodiment of the present invention, in a dental machining system, the control unit determines before the start of machining whether the predicted wear state of the dental tool will drop to a predefined level before the completion of machining. The predefined level can correspond to a fully worn dental tool. Alternatively, the predefined level may correspond to a partially worn dental tool, which ensures the quality of the finished dental restoration. In this embodiment, it is preferred that the user is informed before the start of machining when the remaining useful life of the dental tool is not sufficient to complete the machining process without replacing the dental tool. Thereby, the risk of exceeding the predefined level can be avoided, thus preventing quality loss or any damage to the dental restoration.

[0016] According to one embodiment of the present invention, the control unit determines a position along the time trajectory for interrupting the machining and terminating the use of the dental tool based on a predicted wear state of the dental tool. Thereby, it is possible to allow the user to safely replace the dental tool when such a position is reached and the machining can be reliably interrupted. In this embodiment, the position along the time trajectory is preferably determined such that the machining process can be easily interrupted to allow the user to replace the dental tool. In this embodiment, the position along the time trajectory is preferably determined such that the wear state of the dental tool is reduced to a predetermined level. Thereby, the use of the dental tool can be terminated at a predetermined level that would be optimal considering costs, quality and speed. Thus, the service life of the dental tool can be optimally utilized and the dental restoration can be finished with optimal quality. In this embodiment, the predetermined level is preferably adjustable by the user via a user interface. Alternatively, the predetermined level may be determined by the dental tool machine according to an optimization.

[0017] According to one embodiment of the present invention, the dental machining system uses a display for displaying configuration fields that allow the user to manually adjust one or more parameters of the next machining. The display is preferably a touch-sensitive display. The parameters relate to the time trajectory and the spatial amount of material removal. This allows the user to configure the machining process in terms of cost, quality and time, i.e. through the process control triangle. In this embodiment, the control unit executes an artificial intelligence algorithm that is secondarily trained to predict the wear state of the dental tool at the end of the machining for each adjustment and displays information indicative of the predicted wear state of the dental tool for the adjustment before the start of the machining. The display is preferably a percentage value between 100% and 0%, with 100% indicating that the dental tool is practically new and 0% indicating that the dental tool is completely worn. Alternatively, a color map or icons may be used. Thereby, the displayed information can be used by the user as a decision aid when adjusting the parameters. Thereby, for example, it also enables the user to know whether the remaining wear state of the dental tool may still be sufficient to complete one or more further machining processes, thus enabling the user to plan ahead and use the dental tool more efficiently.

[0018] According to an embodiment of the present invention, one or more parameters can be adjusted automatically without user adjustment. Thus, in this embodiment, the control unit adjusts the parameters of the machining based on the predicted wear state of the dental tool. The parameters relate to the time trajectory and the spatial amount of material removal, as described above. In this embodiment, for example, a new dental tool can be moved relatively faster and / or with a smaller machining path distance through the material of the dental blank. In contrast thereto, in this embodiment, a worn dental tool can be moved slower and / or with a larger machining path distance through the material of the dental blank. Thereby, the dental tool life, the dental restoration quality and the processing time for machining can be optimized based on the wear prediction. In this embodiment, for example, if the dental tool is already worn, the control unit preferably seeks the possibility of adjusting the parameters so that the dental tool can be used without the need to replace the dental tool before the completion of the machining process, provided that at least a sufficient quality can be ensured for the finished dental restoration. Thereby, the dental tool can be utilized gently without compromising the quality, while the costs of extending the machining are minimal. This allows for more efficient utilization of dental tools.

[0019] According to an embodiment of the present invention, the dental machining system has a training mode and an estimation mode. In the estimation mode, the wear state of the dental tool is predicted based on the primary and secondary trained artificial intelligence algorithms without using sensors to detect loads. Thus, the estimation mode can be performed on the dental machining system without using sensors to detect loads. The training mode is performed on the dental machining system further comprising one or more sensors for measuring mechanical quantities associated with the one or more dental tools as they are driven along a time trajectory with a spatial amount of material removal from the dental blank. In the training mode, the control unit is further adapted to train a primary artificial intelligence algorithm for predicting the mechanical quantities based on the time trajectory, the spatial amount of material removal, and the measured mechanical quantities. The evaluation criteria include a similarity metric between the predicted mechanical quantities and the measured mechanical quantities. In the training mode, the control unit is further adapted to train a secondary artificial intelligence algorithm for predicting the wear state of the dental tool based on the predicted mechanical quantities and the actual wear state of the dental tool. The evaluation criteria include a similarity metric between the predicted wear state and the actual wear state of the dental tool. Alternatively, the secondary artificial intelligence algorithm can be trained on a dental machining system that does not have sensors to detect mechanical quantities, i.e. loads and the like.

[0020] According to an embodiment of the present invention, the dental machining system further comprises a first auxiliary means for recognizing the type of dental blank and the type of dental tool, and a second auxiliary means for recognizing the actual wear state of the dental tool before the start of machining. In this embodiment, the control unit is further adapted to train the primary trained artificial intelligence algorithm to further predict the mechanical quantity based on the time trajectory, the spatial amount of material removal, the measured mechanical quantity, the type of dental blank, the type of dental tool, and the actual wear state of the dental tool. Thereby, the primary and secondary trained artificial intelligence algorithms can be improved in terms of prediction accuracy and adaptability to various dental blanks and dental tools on the market. Of course, the dental machining system may also be operated with a default dental blank, a default dental tool, and it is always necessary to first equip with a new unused dental tool.

[0021] According to an embodiment of the invention, the mechanical quantity corresponds to at least the speed, acceleration, vibration of the respective dental tool, or the force acting on the respective dental tool, the torque, or the supply current to a dental tool motor of the respective dental tool, or the sound generated by the respective dental tool. The sensors used may be speed sensors, acceleration sensors, vibration sensors, force sensors, torque sensors, supply current sensors or acoustic sensors.

[0022] According to an embodiment of the present invention, the dental machining system may also have a CAD / CAM module, preferably including a computer station running CAD / CAM software. The primary and secondary artificial intelligence algorithms are preferably provided as part of the CAD / CAM module. The CAD / CAM module is preferably external to the dental tool machine and accessible through a network or the like. A number of different dental tool machines can use the same primary and secondary trained artificial intelligence algorithms for the estimation. The CAD / CAM module may also be provided as part of the dental tool machine. The present invention also provides CAD / CAM software for implementing the above-mentioned functions of the dental machining system. The CAD / CAM software has computer readable code for causing the computerized dental machining system to perform the functions. The CAD / CAM software is stored in a computer readable storage medium. The storage medium may be portable or integrated. The storage medium may be located external or internal to the dental machining system. The storage medium may be reachable through a network or the like. The present invention may be applicable to dental tool machines having various types of kinematics for moving the dental blanks and dental tools.

[0023] In the following description, further aspects and advantageous effects of the present invention are explained in more detail by using exemplary embodiments and by making reference to the drawings. [Brief description of the drawings]

[0024] [Figure 1] FIG. 1 is a schematic perspective view of a dental tool machine of a dental machining system according to one embodiment of the present invention. [Diagram 2] FIG. 2 is a schematic diagram of a display of a configuration field for adjusting a next machining operation, according to one embodiment of the present invention.

[0025] The reference numbers shown in the drawings indicate the elements listed below and are referenced in the following description of the exemplary embodiments: 1.Dental tool machine 2.Dental blanks 2a. Shaft 3.Dental tools 4. Drive unit 4a. Arm 4b. Shaft 5. Configuration Fields X1, X2, X3, Y1, Y2, Y3: Machining parameters

[0026] FIG. 1 partially shows a dental tool machine (1) of a dental machining system for manufacturing dental restorations. The dental tool machine (1) comprises a dental blank holder for holding a dental blank (2) movable relative to the dental tool (3), two drive units (4) each movably holding a dental tool (3) for machining the dental blank (2), and a control unit adapted to control the dental blank holder and the drive units (4) based at least on a time trajectory of the dental tool (3) relative to the dental blank (2) and on a spatial amount of material removal from the dental blank (2) along the time trajectory. Each drive unit (4) has a shaft (4b). An arm (4a) is radially fixed to each shaft (4b). Each drive unit (4) has a drive mechanism capable of rotating the shaft (4b) about the z-axis and reciprocating along the z-axis. The dental tools (3) are held by tool motors respectively arranged in the arms (4a). The dental blank (2) is joined to a shaft (2a) which is connected to another drive mechanism capable of rotating the shaft (2a) about the y-axis and of reciprocating the shaft (2a) along the y-axis.

[0027] The control unit has a training mode and an estimation mode. In the following description, the estimation mode is described first. The training mode is described later. In the estimation mode, the control unit is adapted to execute a primary trained artificial intelligence algorithm, which is adapted to predict mechanical quantities based on the time trajectory and the spatial amount of material removal. The mechanical quantities correspond to one or more physically measurable quantities associated with each dental tool (3) along the time trajectory. In the estimation mode, the control unit is also adapted to execute a secondary trained artificial intelligence algorithm, which is adapted to predict a wear state of the dental tool (3) based on the predicted mechanical quantities.

[0028] In one embodiment, the mechanical quantity corresponds to at least the velocity, acceleration, vibration of each dental tool (3), or the force acting on each dental tool (3), or the torque, or the current supplied to a dental tool motor of each dental tool (3), or the sound generated by each dental tool (3), where the velocity, acceleration and force can be measured along any of the x, y, z directions corresponding to the degrees of freedom of the dental tool machine (1). The velocity may also include the rotational speed of the dental tool (3).

[0029] In one embodiment, the wear state of the dental tool (3) is predicted as a percentage, with 100% indicating that the dental tool (3) is substantially new and 0% indicating that the dental tool (3) is completely worn down.

[0030] In one embodiment, the dental machining system further comprises a first auxiliary means for recognizing the type of the dental blank (2) and the type of the dental tool (3) and a second auxiliary means for recognizing the actual wear state of the dental tool (3). In this embodiment, the control unit is further adapted to execute a primary trained artificial intelligence algorithm further adapted to predict mechanical quantities based on the temporal trajectory and the spatial quantity of material removal, the type of the dental blank (2), the type of the dental tool (3) and the actual wear state of the dental tool (3) before starting the machining. In this embodiment, the control unit is further adapted to execute a secondary trained artificial intelligence algorithm further adapted to predict the wear state of the dental tool (3) based on the predicted mechanical quantities and the actual wear state of the dental tool (3) before starting the machining. The dental tool machine can also be operated with a default type of dental blank (2), a default type of dental tool (3) and the dental tool machine (1) is initially equipped with a new dental tool (3), so the first auxiliary means and the second auxiliary means are optional.

[0031] In one embodiment, the control unit determines, before the start of machining, whether the predicted wear state of the dental tool (3) will decrease to a pre-defined level before the machining is completed.

[0032] In one embodiment, the control unit determines a position along the time trajectory for interrupting machining and terminating use of the dental tool (3) based on a predicted wear state of the dental tool (3).

[0033] In one embodiment, the dental machining system further comprises a display for displaying a configuration field (5) for allowing the user to manually adjust one or more parameters (X1 to X3, Y1 to Y3) of the next machining. The user may select the manual adjustment through the user interface. Figure 2 shows the configuration field (5) displayed on the display of the user interface of the dental tool machine (1). The parameters (X1 to X3, Y1 to Y3) relate to the time trajectory and the spatial amount of material removal. The parameters (X1 to X3, Y1 to Y3) shown in Figure 2 are discrete and the adjustments can be made by selecting the parameters (X1 to X3, Y1 to Y3). Alternatively, they may be adjusted continuously, for example through a software slider. As shown in Figure 2, when the user selects a set of parameters (X1, Y1), the configuration field (5) correspondingly indicates the predicted wear state of the dental tool (3), i.e. 27% to be achieved at the end of the machining performed with such selected parameters (X1, Y1). The number of parameters, their ranges etc. may be defined according to the application. They may include quality levels, speed levels and the like. In this embodiment, the control unit executes first and second trained artificial intelligence algorithms to predict before the start of machining the wear state of the dental tool (3) achieved at the end of machining for each adjustment and displays on the display information indicative of the predicted wear state of the dental tool (3) for the adjustment before the start of machining. The configuration field (5) is illustrated in the form of a 3x3 matrix. Alternatively, it may be a continuous color map, where the color indicates the wear state of the dental tool (3).

[0034] Alternatively, the user may select automatic adjustment through the user interface. In one embodiment, the control unit automatically adjusts the machining parameters (X1 to X3, Y1 to Y3) based on the predicted wear state of the dental tool (3).

[0035] In the following description, a training mode is described. The training mode is performed on a dental machining system that includes one or more sensors for measuring mechanical quantities associated with one or more dental tools as they are driven along a time trajectory together with the spatial quantities of material removal from the dental blank. In the training mode, the control unit trains a primary artificial intelligence algorithm for predicting mechanical quantities based on the time trajectory, the spatial quantities of material removal, and the measured mechanical quantities. In the training mode, the control unit trains a secondary artificial intelligence algorithm for predicting a wear state of the dental tool (3) based on the predicted mechanical quantities and the actual wear state of the dental tool (3). The secondary artificial intelligence algorithm may instead be trained on a dental machining system that does not have sensors for measuring mechanical quantities. The dental tool machine (1) used to train the primary artificial intelligence algorithm has the same kinematics as the dental tool machine (1) used to train the secondary artificial intelligence algorithm. The estimation mode may be performed on a dental machining system that does not include sensors for measuring mechanical quantities.

[0036] In one embodiment, the control unit trains the primarily trained artificial intelligence algorithm to further predict the mechanical quantities based on the time trajectory, the spatial amount of material removal, the measured mechanical quantities, the type of dental blank (2), the type of dental tool (3), and the actual wear state of the dental tool (3). In this embodiment, the dental machining system has the first auxiliary means and the second auxiliary means as described above.

Claims

1. 1. A dental machining system for manufacturing a dental restoration, comprising: The dental tool machine (1) comprises: a dental blank holder for holding at least one dental blank (2) movable relative to one or more dental tools (3); one or more drive units (4) for movably holding, respectively, at least one dental tool (3) for machining said dental blank (2); a control unit for controlling the dental blank holder and the drive unit (4) based at least on a temporal trajectory of the dental tool (3) relative to the dental blank (2) and on a spatial amount of material removal from the dental blank (2) along the temporal trajectory, The control unit a primary trained artificial intelligence algorithm adapted to predict a mechanical quantity based on the time trajectory and the spatial quantity of material removal; wherein said mechanical quantities correspond to one or more physically measurable quantities associated with each dental tool (3) along said time trajectory; wherein the mechanical quantity corresponds to at least one of a velocity, an acceleration, a vibration of the respective dental tool (3), or a force, a torque acting on the respective dental tool (3), or a current supplied to a dental tool motor of the respective dental tool (3), or a sound generated by the respective dental tool (3), wherein the primary trained artificial intelligence algorithm is trained using the time trajectory, the spatial quantities of material removal, and the measured mechanical quantities; wherein the evaluation criteria comprises a similarity metric between the predicted mechanical quantity and the measured mechanical quantity; a secondary trained artificial intelligence algorithm adapted to predict a wear state of the dental tool (3) based on the predicted mechanical quantities, wherein said secondary trained artificial intelligence algorithm is trained using said predicted mechanical quantities and the actual wear state of the dental tool (3), wherein the evaluation criteria comprises a similarity metric between a predicted and an actual wear state of said dental tool (3), A dental machining system, wherein said control unit is further adapted to control said dental blank holder and said drive unit (4) based on said prediction.

2. a first auxiliary means for recognizing the type of said dental blank (2) and the type of said dental tool (3) and a second auxiliary means for recognizing the actual wear state of said dental tool (3), said control unit is further adapted to execute said primary trained artificial intelligence algorithm, which is further adapted to predict said mechanical quantities based on said temporal trajectory and said spatial quantities of material removal, the type of said dental blank (2), the type of said dental tool (3) and the actual wear state of said dental tool (3) before starting machining, 2. The dental machining system according to claim 1, characterized in that the secondarily trained artificial intelligence algorithm is further adapted to predict the wear state of the dental tool (3) based on predicted mechanical quantities and the actual wear state of the dental tool (3) before starting the machining.

3. 3. The dental machining system according to claim 1 or 2, characterized in that the control unit is further adapted to determine before the start of machining whether the predicted wear state of the dental tool (3) will decrease to a predetermined level before the completion of the machining, and wherein the control unit is further adapted to control the dental blank holder and the drive unit (4) based on said determination.

4. 4. The dental machining system according to claim 1, wherein the control unit is further adapted to determine a position along the time trajectory for interrupting the machining and to terminate use of the dental tool (3) based on the predicted wear state of the dental tool (3).

5. 5. The dental machining system according to claim 1, further comprising a display for displaying configuration fields (5) for allowing a user to adjust one or more parameters (X1 to X3, Y1 to Y3) of a next machining operation, wherein said parameters (X1 to X3, Y1 to Y3) are related to a temporal trajectory and a spatial amount of material removal, and wherein the control unit is further adapted to execute a primary and secondary trained artificial intelligence algorithm to predict a wear state of the dental tool (3) at the end of the machining operation for each adjustment before starting the machining operation, to display on the display information indicative of the predicted wear state of the dental tool (3) for the adjustment before starting the machining operation, and to control the dental blank holder and the drive unit (4) based on user adjustments.

6. 5. The dental machining system according to claim 1, wherein the control unit is adapted to adjust the machining parameters (X1 to X3, Y1 to Y3) based on the predicted wear state of the dental tool (3), wherein the parameters (X1 to X3, Y1 to Y3) are related to a temporal trajectory and a spatial amount of material removal.

7. 7. The dental machining system according to claim 1, further comprising one or more sensors for measuring mechanical quantities associated with one or more dental tools (3) as they are driven along the temporal trajectory together with the spatial amount of material removal from the dental blank (2), wherein the control unit is further adapted to train a primary artificial intelligence algorithm for predicting the mechanical quantities based on the temporal trajectory, the spatial amount of material removal and the measured mechanical quantities, and to train a secondary artificial intelligence algorithm for predicting a wear state of the dental tool (3) based on the predicted mechanical quantities and an actual wear state of the dental tool (3).

8. 8. The dental machining system according to claim 7, characterized in that the control unit is further adapted to train the primarily trained artificial intelligence algorithm to further predict the mechanical quantities based on the temporal trajectory, the spatial amount of material removal, the measured mechanical quantities, the type of the dental blank (2), the type of the dental tool (3) and the actual wear state of the dental tool (3).

9. 9. The dental machining system according to claim 1, wherein the wear state of the dental tool (3) is predicted as a percentage, where 100% indicates that the dental tool (3) is substantially new and 0% indicates that the dental tool (3) is completely worn out.

10. 10. A method for controlling a dental machining system according to any one of claims 1 to 9 for producing a dental restoration, comprising the steps of: The dental machining system comprises a dental tool machine (1), the dental tool machine (1) comprising: a dental blank holder for holding at least one dental blank (2) movable relative to one or more dental tools (3); one or more drive units (4) for movably holding, respectively, at least one dental tool (3) for machining said dental blank (2); a control unit for controlling the dental blank holder and the drive unit (4) based at least on a temporal trajectory of the dental tool (3) relative to the dental blank (2) and on a spatial amount of material removal from the dental blank (2) along the temporal trajectory, The method comprises: causing the control unit to execute a primary trained artificial intelligence algorithm adapted to predict a mechanical quantity based on the time trajectory and the spatial quantity of material removal; wherein said mechanical quantities correspond to one or more physically measurable quantities associated with said respective dental tool (3) along said time trajectory; wherein the mechanical quantity corresponds to at least one of a velocity, an acceleration, a vibration of the respective dental tool (3), or a force, a torque acting on the respective dental tool (3), or a current supplied to a dental tool motor of the respective dental tool (3), or a sound generated by the respective dental tool (3), wherein the primary trained artificial intelligence algorithm is trained using the time trajectory, the spatial quantities of material removal, and the measured mechanical quantities; wherein the evaluation criteria comprises a similarity metric between the predicted mechanical quantity and the measured mechanical quantity; causing the control unit to execute a secondary trained artificial intelligence algorithm adapted to predict a wear state of the dental tool (3) based on the predicted mechanical quantities; wherein said secondary trained artificial intelligence algorithm is trained using predicted mechanical quantities and actual wear conditions of said dental tool (3), wherein said evaluation criterion comprises a similarity metric between a predicted and an actual wear state of said dental tool (3), causing said control unit to control said dental blank holder and said drive unit (4) based on said prediction.

11. A computer readable program comprising computer readable code for causing a dental machining system according to any one of claims 1 to 9 to be controlled according to the method steps according to claim 10.

12. A computer readable storage device containing the computer readable program of claim 11.

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