Dental machining system for generating machining process parameters

Abstract

The present invention relates to a dental machining system for manufacturing dental restorations, the dental machining system comprising a dental tool machine (1) comprising a dental blank holder for holding one or more dental blanks (2) movable relative to one or more dental tools (3), one or more drive units (4) for movably holding each at least one dental tool (3) for machining the dental blanks (2), a determination unit for determining the type of each dental blank (2), and adjustment means for allowing a user to adjust the machining time, the quality level of the dental restoration, and the safety level of the dental restoration and the dental tool (3) against machining damage. The dental machining system further comprises a control unit adapted to execute a trained artificial intelligence algorithm adapted to generate process parameters for machining based on the type of dental blank (2), the machining time, the quality level of the dental restoration, and the safety level of the dental restoration and the dental tool (3) against machining damage, to calculate a time trajectory of the dental tool (3) for machining based on the build data of the dental restoration and the generated process parameters, and to control the dental blank holder and the drive unit (4) based on the calculated time trajectory.

Description

Technical Field

[0001] The present invention relates to a dental machining system having a dental tool machine for manufacturing a dental restoration or dental device from a dental blank by using one or more dental tools. More specifically, the present invention relates to a method for generating process parameters for machining a dental blank.

Background Art

[0002] Generally, a dental machining system has a dental tool machine for machining dental blanks. The dental tool machine generally has one or more drive units each movably holding at least one dental tool for machining dental blanks. The dental tools are respectively attached to tool motors in the drive units. The dental tools can be replaced after their service life has passed. The dental blank is attached to a dental blank holder movable relative to the dental tool. A control unit controls the operation of the dental machining system. Generally, CAD / CAM software is executed, for example, on a PC connected to the dental tool machine. CAD / CAM software is generally used to digitally provide the construction data of the dental restoration to be manufactured. CAD / CAM software further generates the temporal trajectory of the dental tools in the dental tool machine based on the construction data and process parameters of the machining. Thereafter, the dental blank holder and the drive unit are controlled based on the temporal trajectory of the dental tools. Typically, the user inputs the type of dental blank via the user interface of the dental tool machine. The user is usually enabled to discretely adjust the machining time (e.g., very fast, fast, normal), the quality level of the dental restoration (e.g., very high, high, normal), and / or the safety level of the dental restoration and the dental tools against machining damage (e.g., very high, high, normal) via the graphical user interface of the CAD / CAM software. In complex test series, the process parameters for the dental machine tools, such as the feed rate of the dental blank, the path distance of the dental tool, the feed rate of the dental tool into the material, the rotational speed of the dental tool, etc., have to be manually defined for each type of dental blank, e.g., for each quality level of the dental restoration, and for each safety level desired by the user.

[0003] Previously, empirical values from previous tests have been used as a basis for defining process parameters. Based on the empirical values, the process parameters are defined for a basic setup. Using an appropriate method, such as statistical design of experiments (DoE), a process parameter space can be constructed, which is then inspected using actual refinement for a dental tool machine. From these inspection results, a rough process parameter model can be derived. Based on this model, combinations of process parameters that promise favorable behavior with respect to the above optimization variables, such as machining time, quality level of the dental restoration (e.g., no chipping), safety level of the dental restoration (e.g., no damage to the dental restoration or dental tool), service life of the dental tool, etc., can then be determined. The combination of process parameters has to be further inspected and / or refined by further tests on the dental tool machine. The problem associated with this prior art method is that for different / new types of dental blanks, different / new types of dental tool machines, different / new operating modes of the dental tool machine, or different / new types of framework conditions, a complex series of numerous tests have to be carried out. However, this is very time-consuming and labor-intensive. When the process parameters continuously depend on optimization variables, such as the wear state, dynamics, or load of the dental tool, the experimental load is too high. SUMMARY OF THE INVENTION

[0004] An object of the present invention is to provide a dental machining system that overcomes the problems of the prior art and can accurately generate process parameters for machining dental restorations / devices.

[0005] This object is achieved through the dental machining system defined in claim 1. The subject matter of the dependent claims relates to further embodiments and developments.

[0006] According to an embodiment of the present invention, a dental machining system utilizes artificial intelligence, such as a neural network. According to the present invention, the dental machining system has a training mode and an inference mode. First, the inference mode will be briefly disclosed. In the dental machining system of the present invention, in the inference mode, the control unit executes a trained artificial intelligence algorithm adapted to generate process parameters for machining based on the type of dental blank, machining time, quality level of the dental restoration, and safety level of the dental restoration and dental tool against machining damage, calculates the temporal trajectory of the dental tool for machining based on the construction data of the dental restoration and the generated process parameters, and is further adapted to control the dental blank holder and the drive unit based on the calculated temporal trajectory. The process parameters include at least one of the rotational speed of the dental tool, the feed speed of the dental tool into the material, the path distance of the dental tool, the limit values for the machining force and torque acting on the dental tool, the feed speed of the dental blank, etc.

[0007] The main advantageous effects of the present invention are that the trained artificial intelligence algorithm stores knowledge related to the process parameters used in dental machining tools resulting from past machining (or test series) in order to simplify or even replace future machining (or test series). Thereby, the test engineer is automatically supported in process parameter setting by the results of all previous tests. Another main advantageous effect of the present invention is that the trained artificial intelligence algorithm can generate clearly distinguishable process parameters, for example, in a continuous range, thanks to the large amount of knowledge learned from past machining (or test series). Another main advantageous effect of the present invention is that the trained artificial intelligence algorithm continuously improves the generation of process parameters, for example, based on the optimization of machining speed versus quality level, while ensuring the safety level.

[0008] According to an embodiment of the present invention, the determination unit is further adapted to determine the type of the dental tool and the wear state of the dental tool. In this embodiment, the type of the dental tool and the wear state of the dental tool can be input by the user to the dental tool machine or retrieved from a data storage device located directly on and / or at a remote location of the dental tool, such as by using an RFID tag on the dental tool. In this embodiment, in the inference mode, the control unit is further adapted to execute a trained artificial intelligence algorithm adapted to generate process parameters for machining based further on the type of the dental tool and the wear state of the dental tool. Thereby, process parameters can be generated taking into account such specific characteristics of the dental tool.

[0009] According to an embodiment of the present invention, the dental machining system further comprises a sensing unit for sensing mechanical quantities related to the dental tool. In this embodiment, in the inference mode, the control unit is adapted to execute a trained artificial intelligence algorithm adapted to generate process parameters for machining based further on the sensed mechanical quantities, and to adaptively control the dental blank holder and the drive unit based on the generated process parameters during machining. The mechanical quantity may correspond to at least one of the position, velocity, acceleration, vibration of each dental tool, the force acting on each dental tool, torque, the supply current to the dental tool motor of each dental tool, or the sound generated by each dental tool. Thereby, the generated process parameters can be adapted to machining in real time taking into account the dynamics of the dental tool.

[0010] According to an embodiment of the present invention, in the inference mode, the control unit is adapted to determine a dental tool load along a temporal trajectory, execute a trained artificial intelligence algorithm adapted to generate process parameters for machining based on the temporal trajectory and the determined dental tool load, and further adaptively control a dental blank holder and a drive unit based on the generated process parameters during machining. Thereby, process parameters can be generated taking into account the dental tool load. The dental tool load can be estimated, for example, based on an analysis of the course of the spatial tool trajectory.

[0011] According to an embodiment of the present invention, the adjustment means further enables the user to discretely or, alternatively, continuously adjust the machining time, the quality level of the dental restoration, and the safety level of the dental restoration and the dental tool against machining damage. Preferably, three different parameter sets for three different machining modes can be discretely adjusted. The continuous adjustment can be achieved using a software slider. Thereby, the process parameters can be generated in a more differentiated manner.

[0012] Subsequently, the training mode will be briefly disclosed. In the training mode, the dental machining system uses data derived from experimental or actual manufacturing operations. The storage medium is constantly updated with such data for the training mode and can serve as a database. According to an embodiment of the present invention, the dental machining system may also preferably have a CAD / CAM module including a computer station such as a PC that executes CAD / CAM software. The trained artificial intelligence algorithm is 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 plurality of different dental tool machines can use the trained artificial intelligence algorithm in the inference mode. The CAD / CAM module can also be provided as part of the dental tool machine. The present invention also provides CAD / CAM software for implementing the above-described functions of the dental machining system. The CAD / CAM software has computer-readable code for causing a computerized dental machining system to execute functions. The CAD / CAM software is stored in a computer-readable storage medium. The storage medium can be portable or integrated. The storage medium can be located outside or inside the dental machining system. The storage medium can be accessible through a network or the like. The present invention can be applied to dental tool machines having various types of kinematic and dynamic capabilities for moving dental blanks and dental tools.

[0013] According to an embodiment of the present invention, in the training mode, the control unit is further adapted to train an artificial intelligence algorithm to generate process parameters for machining based on the type of dental blank, the normalized machining time, the quality level of the dental restoration, and the safety levels of the dental restoration and the dental tool, and the process parameters used for the previously completed machining. The normalized machining time is preferably determined based on the measured machining time and the construction data of the dental restoration. For example, the normalized machining time can be obtained by dividing the measured machining time by the number of caps and / or the surface area of the dental restoration. Generally, the construction data implicitly or explicitly includes such information specific to the dental restoration and can be derived for normalization.

[0014] According to an embodiment of the present invention, in the training mode, the control unit is further adapted to train an artificial intelligence algorithm to generate process parameters for machining based on the type of dental tool and the wear state of the dental tool before and / or after the completion of the previously completed machining. The wear state of the dental tool generally changes with its operating time. From this, the cutting state at the beginning of the tool life is different from the cutting state at the end of the tool life. The trained artificial intelligence algorithm enables continuous tracking of process parameters over the entire tool life. In this way, the quality of the resulting manufactured dental restoration can be maintained regardless of whether a new dental tool or a used dental tool is used, and thus the dental tool can be optimally utilized.

[0015] According to an embodiment of the present invention, in the training mode, the control unit is further adapted to train an artificial intelligence algorithm to generate process parameters for machining based on the perceived mechanical quantities related to the dental tool for the previously completed machining. Thereby, the trained artificial intelligence algorithm improves the generation of process parameters based on the mechanical quantities.

[0016] According to an embodiment of the present invention, in the training mode, the control unit is further adapted to train an artificial intelligence algorithm to generate process parameters for machining based on the temporal trajectory of the dental tool relative to the dental blank and the determined dental tool load along the temporal trajectory of the previously completed machining. Thereby, the trained artificial intelligence algorithm improves process parameter generation based on aspects of the dental tool load, such as material-dependent speed reduction.

[0017] According to an embodiment of the present invention, in the training mode, the control unit is further adapted to train an artificial intelligence algorithm to generate process parameters for machining a new type of dental blank based on the new type of dental blank, the normalized machining time, the quality level of the dental restoration, and the safety levels of the dental restoration and the dental tool against machining damage, and the process parameters used for at least one completed machining of the new type of dental blank. Thereby, the trained artificial intelligence algorithm makes it possible to collect new knowledge about the new material properties in just a few trials when setting up the machining for that new material. And the rest results from the past knowledge of the artificial intelligence algorithm trained with other materials. In particular, in large-scale laboratories, there is often a desire to optimize the machining process for materials that have not been validated. The present invention provides the customer with a means to effectively generate process parameters for unknown materials.

[0018] According to an embodiment of the present invention, in the training mode, the control unit further generates process parameters for machining with a new type of dental tool machine based on a new type of dental tool machine, a normalized machining time, a quality level of a dental restoration, a safety level of the dental restoration and the dental tool against machining damage, and process parameters used for at least one completed machining with the new type of dental tool machine, so as to further adapt to train an artificial intelligence algorithm. Thereby, the trained artificial intelligence algorithm can learn machining with the new machine type based on several actual experiments.

[0019] According to an embodiment of the present invention, in the training mode, the control unit further generates process parameters for machining with a new trajectory calculation algorithm based on a change in the trajectory calculation algorithm, a normalized machining time, a quality level of a dental restoration, a safety level of the dental restoration and the dental tool against machining damage, and process parameters used for at least one completed machining with the new trajectory calculation algorithm, so as to further adapt to train an artificial intelligence algorithm. Thereby, the trained artificial intelligence algorithm enables learning of machining under framework conditions, for example when the trajectory calculation algorithm changes, based on several actual experiments. This increases the sensitivity of the manufacturing process.

[0020] According to an embodiment of the present invention, the quality level of the dental restoration comprises at least one of the surface smoothness, the degree of chipping, and the accuracy of the dental restoration.

[0021] In the following description, further aspects and advantageous effects of the present invention will be described in more detail by using exemplary embodiments and with reference to the drawings.

Brief Description of the Drawings

[0022] [Figure 1]It is a partial schematic view of a dental tool machine in a dental machining system according to an embodiment of the present invention.

[0023] The reference numerals shown in the drawings indicate the elements listed below and will be referred to in the following description of exemplary embodiments: 1. Dental tool machine 2. Dental blank 2a. Shaft 3. Dental tool 4. Drive unit 4a. Arm 4b. Shaft X, Y, Z: Directions

Best Mode for Carrying Out the Invention

[0024] Figure 1 shows a dental machining system for manufacturing dental restorations, comprising a dental blank holder for holding a dental blank (2) movable relative to a dental tool (3), two drive units (4) for movably holding dental tools (3) for machining the dental blank (2) respectively, a determination unit for determining the type of each dental blank (2), and adjustment means for enabling a user to adjust a desired machining time, a quality level of the dental restoration, and a safety level of the dental restoration and the dental tool (3) against machining damage. Each drive unit (4) has a shaft (4b) and an arm (4a) fixed radially to the shaft (4b). Each shaft (4b) can move along the z-axis towards or away from the dental blank (2) through the drive mechanism of the respective drive unit (4). Each arm (4a) can move about the z-axis through the drive mechanism. The dental tools (3) are respectively attached to tool motors in the arms (4a). The dental blank (2) is connected to a shaft (2a) that can move along the y-axis and rotate about the y-axis through another drive mechanism. The dental machining system comprises a control unit. The control unit has a training mode and an inference mode. First, the inference mode will be described. In the inference mode, the control unit is adapted to execute a trained artificial intelligence algorithm configured to generate process parameters for machining based on the type of the dental blank (2), the machining time, the quality level of the dental restoration, and the safety level of the dental restoration and the dental tool (3) against machining damage, calculate a temporal trajectory of the dental tool (3) for machining based on the construction data of the dental restoration and the generated process parameters, and further control the dental blank holder and the drive units (4) based on the calculated temporal trajectory. The process parameters include, for example, the rotational speed of the dental tool (3), the feed rate of the dental tool (3) into the material, the path distance of the dental tool (3), the limit values for the machining force and torque acting on the dental tool (3), the feed rate of the dental blank (2), etc.The dental machining system calculates the construction data or receives it from an external source. The quality level of the dental restoration comprises at least one of the surface smoothness, degree of chipping, and accuracy of the dental restoration.

[0025] In an embodiment, the determination unit is further adapted to determine the type of the dental tool (3) and the wear state of the dental tool (3), and in the inference mode, the control unit is further adapted to execute a trained artificial intelligence algorithm adapted to generate process parameters for machining further based on the type of the dental tool (3) and the wear state of the dental tool (3). The determination unit may use sensors such as RF sensors, touch sensors, user input means, and / or databases for such purposes.

[0026] In an embodiment, the dental machining system further comprises a sensing unit for sensing the mechanical quantities related to the dental tool (3), and in the inference mode, the control unit is adapted to execute a trained artificial intelligence algorithm adapted to generate process parameters for machining further based on the sensed mechanical quantities, and to adaptively control the dental blank holder and the drive unit (4) based on the generated process parameters during machining. The mechanical quantities correspond to at least one of the position, velocity, acceleration, vibration of each dental tool (3), the force acting on each dental tool (3), torque, the supply current to the dental tool motor of each dental tool (3), or the sound generated by each dental tool (3).

[0027] In an embodiment, in the inference mode, the control unit is adapted to determine the dental tool (3) load along the calculated or sensed temporal trajectory, to execute a trained artificial intelligence algorithm adapted to generate process parameters for machining further based on the temporal trajectory and the determined dental tool (3) load, and to adaptively control the dental blank holder and the drive unit (4) based on the generated process parameters during machining.

[0028] In an embodiment, in the inference mode, the adjustment means further enables the user to continuously adjust the machining time, the quality level of the dental restoration, and the safety levels of the dental restoration and the dental tool (3) against machining damage, for example, based on a preset range. Alternatively, the user may be enabled to discretely adjust the machining time, the quality level of the dental restoration, and the safety levels of the dental restoration and the dental tool (3) against machining damage, for example, based on one or more preset values.

[0029] In the following description, the training mode will be described. The training aims to learn the optimization of the triangle of machining time, quality level, and safety level for different dental blank types from the knowledge of a plurality of past machining (or test series). The knowledge includes the feed rate of the dental blank (2), the path distance of the dental tool (3), the feed rate of the dental tool (3) into the material, the rotational speed of the dental tool (3), the trajectory calculation algorithm used, the parameters of the dental tool (3) load algorithm, the parameters of any special treatments such as immersion, path smoothing, the type of the dental tool (3), the wear state of the dental tool (3) before the start and / or completion of machining, the type of the dental blank (2), for example its material, machining time, speed in each direction, the overall temporal trajectory of the dental tool (3) including acceleration at each point, the material removed according to the dental tool (3) load determination algorithm, the current to the tool motor, the forces and torques acting on the dental tool (3) obtained through sensor technology, the quality level resulting from the dental restoration, any special cases such as damage to the dental tool (3) or the dental restoration, and the type of the dental tool machine including kinematic and dynamic capabilities, and may include at least one of the process parameters for each past machining. In the training mode, the control unit is further adapted to train an artificial intelligence algorithm to generate process parameters for machining based on the type of the dental blank, the normalized machining time, the quality level of the dental restoration, and the safety levels of the dental restoration and the dental tool (3), and the process parameters used for the previously completed machining. The normalized machining time is determined based on the measured machining time and features derivable from the construction data of the dental restoration such as the number and / or surface area of the caps of the dental restoration.

[0030] In an embodiment, the training is directed to learning the type and wear state of the dental tool (3). In this embodiment, in the training mode, the control unit is further adapted to train an artificial intelligence algorithm to generate process parameters for machining based further on the type of the dental tool (3) and the wear state of the dental tool (3) before and / or after completion of the previously completed machining. The wear state of the dental tool (3) is given 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.

[0031] In an embodiment, the training is directed to learning the dynamics of the dental tool (3), for example, handling forces and torques. In this embodiment, in the training mode, the control unit is further adapted to train an artificial intelligence algorithm to generate process parameters for machining based further on the sensed mechanical quantities associated with the dental tool (3) of the previously completed machining.

[0032] In an embodiment, the training is directed to learning the dental tool (3) load. In this embodiment, in the training mode, the control unit is further adapted to train an artificial intelligence algorithm to generate process parameters for machining based further on the temporal trajectory of the dental tool (3) with respect to the dental blank (2) and the determined dental tool (3) load along the temporal trajectory of the previously completed machining.

[0033] In an embodiment, the training is directed to learning a new dental blank (2). In this embodiment, in the training mode, the control unit, based on the type of the new dental blank (2), the normalized machining time, the quality level of the dental restoration, and the safety level of the dental restoration and the dental tool (3) against machining damage, and further based on the process parameters used for at least one completed machining with the new type of dental blank (2), is further adapted to train an artificial intelligence algorithm to generate process parameters for machining the new type of dental blank (2). For example, for "Material A" of a certain type of dental blank (2), training is performed using a plurality of related past machinings (or test series) to generate process parameters in the most advantageous or optimized combination. For a new "Material B" of a certain type of dental blank (2), only directional tests in non-optimized combinations are required. The results are fed back to the trained artificial intelligence algorithm. This can directly enable finding the most advantageous or ideally optimal combination based on the correlations learned with Material A and the data from the directional tests, which then only needs to be validated in the final tests. The time-consuming tests for the optimization regarding Material B are no longer necessary.

[0034] In an embodiment, the training is directed to learning a new dental tool machine (1). The dental tool machine (1) can have various kinematic and dynamic capabilities. In this embodiment, in the training mode, the control unit, based on the type of the new dental tool machine (1), the normalized machining time, the quality level of the dental restoration, and the safety level of the dental restoration and the dental tool (3) against machining damage, and further based on the process parameters used for at least one completed machining with the new type of dental tool machine (1), is further adapted to train an artificial intelligence algorithm to generate process parameters for machining with the new type of dental tool machine (1).

[0035] In an embodiment, the training is directed to learning a new trajectory calculation algorithm. In this embodiment, in the training mode, the control unit further adapts to train an artificial intelligence algorithm to generate process parameters for machining with the new trajectory calculation algorithm based on changes in the trajectory calculation algorithm, the normalized machining time, the quality level of the dental restoration, and the safety levels of the dental restoration and the dental tool (3) against machining damage, and the process parameters used for at least one completed machining with the new trajectory calculation algorithm. The following is an appendix of the matters described in the original claims of the present application. [1] A dental machining system for manufacturing a dental restoration / device, comprising a dental blank holder for holding one or more dental blanks (2) movable relative to one or more dental tools (3); one or more drive units (4) each movably holding at least one dental tool (3) for machining the dental blank (2); a determination unit for determining the type of each dental blank (2); adjustment means for enabling a user to adjust at least one of machining time, the quality level of the dental restoration / device, and the safety level of the dental restoration / device and the dental tool (3) against machining damage In a dental machining system comprising a dental tool machine (1) provided with executing a trained artificial intelligence algorithm adapted to generate process parameters for the machining based on at least one of the type of the dental blank (2), the machining time, the quality level of the dental restoration / device, and the safety level of the dental restoration / device and the dental tool (3) against machining damage; calculating a temporal trajectory of the dental tool (3) for the machining based on the construction data of the dental restoration / device and the generated process parameters; controlling the dental blank holder and the drive unit (4) based on the calculated temporal trajectory A dental machining system, further comprising a control unit further adapted to perform the above. [2] The determination unit is further adapted to determine the type of the dental tool (3) and the wear state of the dental tool (3), The control unit is further adapted to execute the trained artificial intelligence algorithm adapted to generate process parameters for the machining based on the type of the dental tool (3) and the wear state of the dental tool (3) The dental machining system according to [1], characterized in that. [3] Further comprising a sensing unit for sensing a mechanical quantity related to the dental tool (3), and the control unit is executing the trained artificial intelligence algorithm adapted to generate process parameters for the machining further based on the sensed mechanical quantity; during the machining, adaptively controlling the dental blank holder and the drive unit (4) based on the generated process parameters; The dental machining system according to [1] or [2], further adapted to perform the above. [4] The control unit is determining the dental tool (3) load along the temporal trajectory of the dental tool (3); executing the trained artificial intelligence algorithm adapted to generate process parameters for the machining further based on the temporal trajectory and the determined dental tool (3) load; during the machining, adaptively controlling the dental blank holder and the drive unit (4) based on the generated process parameters; The dental machining system according to any one of [1] to [3], further adapted to perform the above. [5] The adjustment means further enables the user to continuously or discretely adjust at least one of the machining time, the quality level of the dental restoration / device, and the safety level of the dental restoration / device and the dental tool (3) against machining damage. The dental machining system according to any one of [1] to [4]. [6] The control unit is The artificial intelligence algorithm is further adapted to be trained to generate process parameters for the machining based on at least one of the type of the dental blank, the process parameters used for the previously completed machining, the normalized machining time, the quality level of the dental restoration / device, and the safety level of the dental restoration / device and the dental tool (3). The dental machining system according to any one of [1] to [5]. [7] The control unit is The dental machining system according to [6], which is further adapted to train the artificial intelligence algorithm to generate process parameters for the machining, based further on the type of the dental tool (3) and the wear state of the dental tool (3) before and / or after completion of previously completed machining. [8] The control unit The dental machining system according to [6] or [7], which is further adapted to train the artificial intelligence algorithm to generate process parameters for the machining, based further on the sensed mechanical quantities related to the dental tool (3) of previously completed machining. [9] The control unit The dental machining system according to any one of [6] to [8], which is further adapted to train the artificial intelligence algorithm to generate process parameters for the machining, based further on the temporal trajectory of the dental tool (3) relative to the dental blank (2) and the determined dental tool (3) load along the temporal trajectory of previously completed machining.

[10] The control unit The dental machining system according to any one of [6] to [9], which is further adapted to train the artificial intelligence algorithm to generate process parameters for the machining of the dental blank (2) of a new type, based further on the type of the new dental blank (2), the process parameters used for at least one previously completed machining of the dental blank (2) of the new type, the normalized machining time, the quality level of the dental restoration / device, and at least one of the safety levels of the dental restoration / device and the dental tool (3) against machining damage.

[11] The control unit The type of the new dental tool machine (1), the process parameters used for at least one completed machining in the new type of the dental tool machine (1), the normalized machining time, the quality level of the dental restoration / device, and at least one of the safety levels of the dental restoration / device and the dental tool (3) against machining damage, and further based on the artificial intelligence algorithm being further adapted to train the process parameters for the machining in the new type of the dental tool machine (1), the dental machining system according to any one of [6] to

[10] .

[12] The control unit The change of the new trajectory calculation algorithm, the process parameters used for at least one completed machining in the new trajectory calculation algorithm, the normalized machining time, the quality level of the dental restoration / device, and at least one of the safety levels of the dental restoration / device and the dental tool (3) against machining damage, and further based on the artificial intelligence algorithm being further adapted to train the process parameters for the machining in the new trajectory calculation algorithm, the dental machining system according to any one of [6] to

[11] .

[13] The normalized machining time is determined based on the measured machining time and the construction data of the dental restoration / device, the dental machining system according to any one of [7] to

[12] subordinate to [6].

[14] The process parameters include at least one of the rotational speed of the dental tool (3), the feed speed to the material of the dental tool (3), the path distance of the dental tool (3), the limit values for the machining force and torque acting on the dental tool (3), and the feed speed of the dental blank (2), the dental machining system according to any one of [1] to

[13] .

[15] The quality level of the dental restoration / device includes at least one of the surface smoothness, the degree of chipping, and the accuracy of the dental restoration / device, the dental machining system according to any one of [1] to

[14] .

[16] The dental machining system according to any one of [4] to

[15] , which is subordinate to [3], is characterized in that the mechanical quantity corresponds to at least one of the position, velocity, acceleration, vibration, force acting on each of the dental tools (3), torque, supply current to the dental tool motor of each of the dental tools (3), or sound generated by each of the dental tools (3).

Claims

1. A dental machining system for manufacturing a dental restoration / device, comprising: a dental blank holder for holding one or more dental blanks (2) movable relative to one or more dental tools (3); one or more drive units (4) each movably holding at least one dental tool (3) for machining the dental blank (2); a determination unit for determining the type of each dental blank (2); adjustment means for enabling a user to adjust at least one of machining time, the quality level of the dental restoration / device, and the safety level of the dental restoration / device and the dental tool (3) against machining damage; In a dental machining system comprising a dental tool machine (1) comprising: executing a trained artificial intelligence algorithm adapted to generate process parameters for the machining based on at least one of the type of the dental blank (2), the machining time, the quality level of the dental restoration / device, and the safety level of the dental restoration / device and the dental tool (3) against machining damage; calculating a temporal trajectory of the dental tool (3) for the machining based on the construction data of the dental restoration / device and the generated process parameters; controlling the dental blank holder and the drive unit (4) based on the calculated temporal trajectory; A dental machining system, further comprising a control unit further adapted to perform the above.

2. The determination unit is further adapted to determine the type of the dental tool (3) and the wear state of the dental tool (3). The control unit: is further adapted to execute the trained artificial intelligence algorithm adapted to generate process parameters for the machining based on the type of the dental tool (3) and the wear state of the dental tool (3). The dental machining system according to claim 1, characterized in that.

3. Further comprising a sensing unit for sensing a mechanical quantity related to the dental tool (3). The control unit: executes the trained artificial intelligence algorithm adapted to generate process parameters for the machining based on the sensed mechanical quantity. During the machining, adaptively controlling the dental blank holder and the drive unit (4) based on the generated process parameters The dental machining system according to claim 1 or 2, further adapted to perform the above.

4. The control unit Determining the load of the dental tool (3) along the temporal trajectory of the dental tool (3); Executing the trained artificial intelligence algorithm adapted to generate process parameters for the machining, further based on the temporal trajectory and the determined load of the dental tool (3) along the temporal trajectory in the previously completed machining; During the machining, adaptively controlling the dental blank holder and the drive unit (4) based on the generated process parameters The dental machining system according to any one of claims 1 to 3, further adapted to perform the above.

5. The adjustment means further enables the user to continuously or discretely adjust at least one of the machining time, the quality level of the dental restoration / device, and the safety level of the dental restoration / device and the dental tool (3) against machining damage. The dental machining system according to any one of claims 1 to 4.

6. The control unit The dental machining system according to any one of claims 1 to 5, further adapted to train the artificial intelligence algorithm to generate process parameters for the machining based on at least one of the type of the dental blank, the process parameters used for the previously completed machining, the normalized machining time, the quality level of the dental restoration / device, and the safety level of the dental restoration / device and the dental tool (3).

7. The control unit The dental machining system according to claim 6, dependent on claim 2, further adapted to train the artificial intelligence algorithm to generate process parameters for the machining, further based on the type of the dental tool (3) and the wear state of the dental tool (3) before and / or after the completion of the previously completed machining.

8. The control unit The dental machining system according to claim 6, further adapted to train the artificial intelligence algorithm to generate process parameters for the machining, further based on the sensed mechanical quantities associated with the dental tool (3) of the previously completed machining.

9. The control unit is The dental machining system according to claim 6, further adapted to train the artificial intelligence algorithm to generate process parameters for the machining, further based on the temporal trajectory of the dental tool (3) with respect to the dental blank (2) and the determined load of the dental tool (3) along the temporal trajectory of the previously completed machining.

10. The control unit is The dental machining system according to any one of claims 6 to 9, further adapted to train the artificial intelligence algorithm to generate process parameters for the machining of the new type of dental blank (2), further based on the type of the new dental blank (2), the process parameters used for at least one previously completed machining of the new type of dental blank (2), the normalized machining time, the quality level of the dental restoration / device, and at least one of the safety levels of the dental restoration / device and the dental tool (3) against machining damage.

11. The control unit is The dental machining system according to any one of claims 6 to 10, further adapted to train the artificial intelligence algorithm to generate process parameters for the machining in the new type of dental tool machine (1), further based on the type of the new dental tool machine (1), the process parameters used for at least one completed machining in the new type of dental tool machine (1), the normalized machining time, the quality level of the dental restoration / device, and at least one of the safety levels of the dental restoration / device and the dental tool (3) against machining damage.

12. The control unit is The artificial intelligence algorithm is further adapted to train the process parameters for the machining with the new trajectory calculation algorithm, further based on at least one of a change in the new trajectory calculation algorithm, process parameters used for at least one completed machining with the new trajectory calculation algorithm, the normalized machining time, the quality level of the dental restoration / device, and the safety level of the dental restoration / device and the dental tool (3) against machining damage, a dental machining system according to any one of claims 6 to 11.

13. The dental machining system according to any one of claims 7 to 12, wherein the normalized machining time is determined based on the measured machining time and the construction data of the dental restoration / device.

14. The process parameters include at least one of a rotational speed of the dental tool (3), a feed rate to the material of the dental tool (3), a path distance of the dental tool (3), limit values for machining force and torque acting on the dental tool (3), and a feed rate of the dental blank (2), a dental machining system according to any one of claims 1 to 13.

15. The quality level of the dental restoration / device includes at least one of surface smoothness, degree of chipping, and accuracy of the dental restoration / device, a dental machining system according to any one of claims 1 to 14.

16. The mechanical quantities respectively correspond to at least one of the position, speed, acceleration, vibration of each dental tool (3), the force and torque acting on each dental tool (3), the supply current to the dental tool motor of each dental tool (3), or the sound generated by each dental tool (3), a dental machining system according to any one of claims 4 to 6, 8 to 15, dependent on claim 3.

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