Double-Sided or Single-Sided Machine Tool and Method for Operating a Double-Sided or Single-Sided Machine Tool

Abstract

A double-sided or single-sided machine tool includes a first working disk and a counter-bearing element. The first working disk and the counter-bearing element can be driven rotationally relative to each other by means of a rotary drive. A working gap is formed between the first working disk and the counter-bearing element for the double-sided or single-sided machining of flat workpieces. The double-sided or single-sided machine tool comprises multiple sensors that record measurement data relating to tool and/or machining parameters of the double-sided or single-sided machine tool during operation. A control apparatus obtains the measurement data recorded by the sensors during operation. The control apparatus comprises an artificial neural network that is designed to create a state vector of the double-sided or single-sided machine tool from the measurement data and to compare said state vector with at least one target state vector.

Claims

1. A double-sided or single-sided machine tool, comprising: a first working disk and a counter-bearing element, wherein the first working disk and the counter-bearing element can be driven rotationally relative to each other by means of a rotary drive, and wherein a working gap is formed between the first working disk and the counter-bearing element for the double-sided or single-sided machining of flat workpieces; sensors that record measurement data relating to at least one of tool parameters or operating parameters of the double-sided or single-sided machine tool during operation of the double-sided or single-sided machine tool; and a control apparatus that obtains the measurement data recorded by the sensors during operation of the double-sided or single-sided machine tool, wherein the control apparatus comprises an artificial neural network that is designed to create a state vector of the double-sided or single-sided machine tool from the measurement data and to compare the state vector with a target state vector.

2. The double-sided or single-sided machine tool according to claim 1, wherein the control apparatus is designed to issue a warning message when the state vector deviates from the target state vector.

3. The double-sided or single-sided machine tool according to claim 1, comprising: a regulation apparatus that is designed, when the state vector deviates from the target state vector, to control at least one of the tool parameters or the operating parameters of the double-sided or single-sided machine tool, such that the state vector resulting from the control matches the target state vector.

4. The double-sided or single-sided machine tool according to claim 3, wherein the regulation apparatus is integrated in the control apparatus.

5. The double-sided or single-sided machine tool according to claim 4, wherein the regulation apparatus is designed to control at least one of the tool parameters or the operating parameters of the double-sided or single-sided machine tool based on an adjustment rule stored in the regulation apparatus.

6. The double-sided or single-sided machine tool according to claim 3, wherein an additional artificial neural network is provided that is designed to assess the measurement data by means of machine learning and to create or modify an adjustment rule stored in the regulation apparatus.

7. The double-sided or single-sided machine tool according to claim 6, wherein the regulation apparatus is integrated in the additional artificial neural network.

8. The double-sided or single-sided machine tool according to claim 1, wherein the control apparatus is configured to compare the state vector with multiple target state vectors including the target state vector, and is configured to control, when the state vector deviates from each of the multiple target state vectors, at least one of the tool parameters or the operating parameters of the double-sided or single-sided machine tool, such that the state vector resulting from the control matches one of the multiple target state vectors.

9. The double-sided or single-sided machine tool according to claim 2, further comprising: a regulation apparatus that is designed, when the state vector deviates from the target state vector, to control at least one of the tool parameters or the operating parameters of the double-sided or single-sided machine tool, such that the state vector matches the target state vector.

10. The double-sided or single-sided machine tool according to claim 9, wherein the regulation apparatus is integrated in the control apparatus.

11. The double-sided or single-sided machine tool according to claim 1, wherein an additional artificial neural network is provided that is designed to assess the measurement data by means of machine learning and to control at least one of the tool parameters or the operating parameters of the double-sided or single-sided machine tool, based on the assessment.

12. The double-sided or single-sided machine tool according to claim 1, wherein an additional artificial neural network is provided that is designed to assess the measurement data by means of machine learning and to create or modify an adjustment rule stored in a regulation apparatus.

13. The double-sided or single-sided machine tool according to claim 12, wherein the regulation apparatus is integrated in the additional artificial neural network.

14. The double-sided or single-sided machine tool according to claim 1, wherein the sensors comprise measuring apparatuses for measuring at least one of a distance between the first working disk and the counter-bearing element, a temperature of at least one of the first working disk, the counter-bearing element, or other machine components of the double-sided or single-sided machine tool, at least one of a temperature or a flow rate of a machining agent supplied to the working gap for machining the flat workpieces, a rotational speed of at least one of the first working disk, the counter-bearing element, or rotor disks that are rotatably mounted in the working gap, a load between the first working disk and the counter-bearing element, at least one of a rotational speed, a torque, or a temperature of the rotary drive, at least one of a pressure or a force of a deformation generator of at least one of the first working disk or the counter-bearing element, a thickness of a working lining of at least one of the first working disk or the counter-bearing element, or at least one of a thickness or shape of workpieces machined in the double-sided or single-sided machine tool.

15. The double-sided or single-sided machine tool according to claim 1, wherein: the counter-bearing element is formed by a second working disk; the first working disk and second working disk are arranged coaxially to each other and can be driven rotationally relative to each other; and the working gap is formed between the first working disk and the second working disk for double-sided or single-sided machining of flat workpieces.

16. A system, comprising: at least two double-sided or single-sided machine tools, wherein each double-sided or single-sided machine tool comprises: a first working disk and a counter-bearing element, wherein the first working disk and the counter-bearing element can be driven rotationally relative to each other by means of a rotary drive, and wherein a working gap is formed between the first working disk and the counter-bearing element for the double-sided or single-sided machining of flat workpieces; sensors that record measurement data relating to at least one of tool parameters or machining parameters of the double-sided or single-sided machine tool during operation of the double-sided or single-sided machine tool; and a control apparatus that obtains the measurement data recorded by the sensors during operation of the double-sided or single-sided machine tool, wherein the control apparatus comprises an artificial neural network that is designed to create a state vector of the double-sided or single-sided machine tool from the measurement data and to compare the state vector with a target state vector; and a higher-level artificial neural network connected to the artificial neural network of each of the at least two double-sided or single-sided machine tools, wherein the higher-level artificial neural network is designed to train at least one artificial neural network of the at least two double-sided or single-sided machine tools based on data obtained by the artificial neural network of each of the at least two double-sided or single-sided machine tools by inputting state vectors that lead to an acceptable machining result of flat workpieces.

17. A method for operating a double-sided or single-sided machine tool, wherein the double-sided or single-sided machine tool comprises a first working disk and a counter-bearing element, the first working disk and the counter-bearing element can be driven rotationally relative to each other by means of a rotary drive, and a working gap formed between the first working disk and the counter-bearing element for the double-sided or single-sided machining of flat workpieces; and sensors that record measurement data relating to at least one of tool parameters or operating parameters of the double-sided or single-sided machine tool during operation of the double-sided or single-sided machine tool, the method comprising: obtaining, by a control apparatus, the measurement data; creating, using an artificial neural network, a state vector of the double-sided or single-sided machine tool from the measurement data; and comparing the state vector with at least one target state vector.

18. The method according to claim 17, wherein the artificial neural network is trained by inputting target state vectors that lead to an acceptable machining result of flat workpieces.

19. The method according to claim 18, wherein the artificial neural network is trained further during operation of the double-sided or single-sided machine tool by inputting additional target state vectors that lead to an acceptable machining result of flat workpieces.

20. The method according to claim 18, wherein an additional artificial neural network is trained using the artificial neural network by inputting target state vectors that lead to an acceptable machining result of flat workpieces during operation of the double-sided or single-sided machine tool.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Exemplary embodiments of the invention are explained below in greater detail using schematic drawings.

[0036] FIG. 1 shows part of a double-sided or single-sided machine tool according to embodiments of the invention in a sectional view in a first operating state.

[0037] FIG. 2 shows the view from FIG. 1 in a second operating state.

[0038] FIG. 3 shows the view from FIG. 1 in a third operating state.

[0039] FIG. 4 is a schematic representation of the function of the double-sided machine tool in accordance with a first embodiment of the invention.

[0040] FIG. 5 is a schematic representation of the function of the double-sided machine tool in accordance with a second embodiment of the invention.

[0041] FIG. 6 is a schematic representation of the function of the double-sided machine tool in accordance with a third embodiment of the invention.

[0042] FIG. 7 is a schematic representation of the function of the double-sided machine tool in accordance with a fourth embodiment of the invention.

[0043] FIG. 8 is a schematic representation of the function of the double-sided machine tool in accordance with a fifth embodiment of the invention.

[0044] FIG. 9 is a schematic representation of the function of the double-sided machine tool in accordance with a sixth embodiment of the invention.

[0045] FIG. 10 shows a system in accordance with embodiments of the invention in a schematic representation.

[0046] The same reference numbers refer to the same objects in the figures unless indicated otherwise.

DETAILED DESCRIPTION

[0047] The double-sided machine tool depicted merely as an example in FIGS. 1 to 3 has an annular upper support disk 10 and also an annular lower support disk 12. A first annular upper working disk 14 is fastened to the upper support disk 10 and a second, likewise annular lower working disk 16 as a counter-bearing element is fastened to the lower support disk 12. Between the annular upper working disk 14 and the annular lower working disk 16, an annular working gap 18 is formed in which flat workpieces such as wafers are machined on both sides during operation. The double-sided machine tool can be a polishing machine, lapping machine, or a grinding machine, for example.

[0048] The upper support disk 10 and with it the upper working disk 14 and/or the lower support disk 12 and with it the lower working disk 16 can be driven rotationally relative to each other by a suitable drive apparatus, comprising, for example, an upper drive shaft and/or a lower drive shaft and at least one drive motor. The drive apparatus is known per se and will not be described further for reasons of clarity. In a manner that is also known per se, the workpieces to be machined can be held in the working gap 18 in a floating manner in rotor disks. Using suitable kinematics, for example planetary kinematics, it can be ensured that the rotor disks also rotate through the working gap 18 during the relative rotation of the upper support disk 10 and the lower support disk 12 or, respectively, the upper working disk 14 and the lower working disk 16. In the upper working disk 14 or the upper support disk 10 and possibly also the lower working disk 16 or the lower support disk 12, temperature-control channels can be designed through which a temperature-control fluid, for example, a temperature-control liquid such as cooling water, can be conveyed during operation. This is also known per se and is not shown in more detail.

[0049] The double-sided machine tool shown in FIGS. 1 to 3 further comprises distance-measuring apparatuses that are also known per se as sensors. The sensors may, for example, operate optically or electromagnetically (e.g., eddy current sensors). In the example shown, a first distance-measuring apparatus 20, a second distance-measuring apparatus 22, and a third distance-measuring apparatus 24 are provided, for example, which measure the distance between the upper working disk 14 and the lower working disk 16 at three radially spaced positions of the working gap 18, as illustrated by arrows in FIG. 1. As can be seen, the first distance-measuring apparatus 20 measures the distance between the upper working disk 14 and the lower working disk 16 in the region of the radially outer edge of the working gap 18. The third distance-measuring apparatus 24 measures the distance between the upper working disk 14 and the lower working disk 16 in the region of the radially inner edge of the working gap 18. The second distance-measuring apparatus 22 measures the distance between the upper working disk 14 and the lower working disk 16 in the center of the working gap 18.

[0050] The first distance-measuring apparatus 20, the second distance-measuring apparatus 22, and the third distance-measuring apparatus 24 have not been shown in FIGS. 2 and 3 for reasons of clarity. The measurement data of the first distance-measuring apparatus 20, the second distance-measuring apparatus 22, and the third distance-measuring apparatus 24 are supplied or provided to the control apparatus 34.

[0051] A control apparatus, such as the control apparatus 34, can be or include a microprocessor, processor, or other computing component with input and output connections coupled to the components described herein. A control apparatus is configured to perform the methods described herein. For example, a control apparatus can be programmed to perform the methods described herein. A control apparatus can include computer-readable instructions stored in a non-transitory storage medium that, when executed, causes the control apparatus to perform the methods described herein. A control apparatus can contain both hardware and software to implement the various functions described herein. For example, any of the artificial neural networks of a control apparatus described herein can be implemented by hardware, software, or some combination thereof.

[0052] In the present case, the lower working disk 16 is fastened to the lower support disk 12 only in the regions of the outer edge and the inner edge of the second working disk 16, for example, screwed along a partial circle in each case, as illustrated in FIG. 1 as a first fastening location 26 and a second fastening location 28. In contrast, the lower working disk 16 is not fastened to the lower support disk 12 between the first fastening location 26 and the second fastening location 28. Instead, between the first fastening location 26 and the second fastening location 28, an annular pressure volume 30 is located between the lower support disk 12 and the lower working disk 16. The pressure volume 30 is connected to a pressure fluid reservoir, for example a liquid reservoir, in particular a water reservoir, via a dynamic pressure line 32. In the dynamic pressure line 32, a pump and a control valve can be arranged. The pump and/or the control valve can be controlled by the control apparatus 34. In this way, a desired pressure that acts on the lower working disk 16 can be built up in the pressure volume 30 by fluid introduced into the pressure volume 30. The pressure prevailing in the pressure volume 30 can be measured by a pressure measuring apparatus. The measurement data of the pressure measuring apparatus can also be provided to the control apparatus 34 so that the control apparatus 34 can set a specified pressure in the pressure volume 30.

[0053] Due to its freedom of movement between the first fastening location 26 and the second fastening location 28, the lower working disk 16 can be brought into a convex shape locally, as indicated in FIG. 2 by a dotted line depicting a convex deformation 36, by setting a sufficiently high pressure in the pressure volume 30. If a pressure p.sub.0 is assumed in the pressure volume 30 in the operating state of FIG. 1, in which the lower working disk 16 has a planar shape, the convex deformation 36 of the lower working disk 16 shown in FIG. 2 can be achieved by setting a pressure p.sub.1>p.sub.0. On the other hand, a local concave deformation of the lower working disk 16 can be achieved by setting a pressure p.sub.2<p.sub.0 in the pressure volume 30, as illustrated in FIG. 3 by a dotted line depicting a concave deformation 38.

[0054] In this case, it can be seen that the lower working disk 16 can take on a locally convex shape (FIG. 2) or, respectively, a locally concave shape (FIG. 3), viewed in the radial direction, between its inner edge, in the region of the first fastening location 26, and its outer edge, in the region of the second fastening location 28.

[0055] In addition to this local radial deformation of the lower working disk 16, means can be provided for global deformation of the upper working disk 14. These means may be designed as described above or, respectively, in DE 10 2006 037 490 B4. The upper support disk 10 and with it the upper working disk 14 fastened thereto is globally deformed, such that a globally concave or globally convex shape of the working surface of the upper working disk 14 is produced over the entire cross section of the upper working disk 14. In contrast, the upper working disk 14, between its radially inner edge and its radially outer edge, may remain planar or be locally deformed in the above-mentioned manner by means of the pressure volume 30. The means for adjusting the shape of the upper working disk 14 can also be controlled by the control apparatus 34.

[0056] The first distance-measuring apparatus 20, the second distance-measuring apparatus 22, and the third distance-measuring apparatus 24 form sensors that record measurement data relating to tool and/or machining parameters of the double-sided machine tool, in the present case the thickness and geometry of the working gap 18, in particular during operation of the double-sided machine tool. Preferably, the double-sided machine tool comprises multiple additional sensors having corresponding additional measuring apparatuses. Said measuring apparatuses may be measuring apparatuses of the type explained above. Said measuring apparatuses record additional tool and/or machining parameters during operation of the double-sided machine tool.

[0057] The measurement data recorded by the sensors are fed to the control apparatus 34. From said measurement data, the control apparatus 34 creates a state vector of the double-sided machine tool by means of an artificial neural network integrated in the control apparatus 34 and compares said state vector with at least one target state vector, preferably a set of target state vectors that were assigned to an acceptable production process within the scope of training.

[0058] Stated generally, the state vector is a mathematical vector with a number of possible parameters. As described in further detail below, each parameter can be a measured value. For example, a current pad temperature, working disk distance, force or pressure between working disks and/or constructional fixed values, such as number of workpieces, position of workpieces in carrier disk, type of polishing pad, essentially everything that is unchangeable in the process, and/or target values for a number of controls, such as pressure/force, rotation of working disks per minute, disk temperature, etc.

[0059] Training of the artificial neural network integrated in the control apparatus 34 will be explained in more detail based on FIG. 4. A double-sided machine tool 40 in accordance with an embodiment of the invention is shown in FIG. 4. Unmachined workpieces 42, for example unmachined wafers, are supplied to the double-sided machine tool 40 for machining and finished machined workpieces 44, in particular machined wafers, are output by means of the double-sided machine tool 40. A data memory 46 is provided, to which, for example, measurement data relating to tool and machining parameters recorded by the sensors are supplied. Said data are made available to an operator 48, as illustrated in FIG. 4 with arrow 50. Furthermore, measurement data relating, for example, to the geometry of the machined workpieces are fed to the data memory 46 as additional tool and/or operating parameters, wherein said data are also fed to the operator 48, as illustrated in FIG. 4 with arrow 52. Finally, external environmental data are also available to the data memory, as illustrated with arrow 54. Said external environmental data may also be fed to the operator 48. On this basis, the operator 48 performs an assessment of the production process underlying the respective data as to whether the machining result is acceptable. The operator 48 makes this assessment available to the artificial neural network of the control apparatus 34, as shown in FIG. 4 with arrow 56. The corresponding state vectors are stored as target state vectors by the artificial neural network.

[0060] FIG. 5 shows how the double-sided machine tool can be operated on this basis. In this case, the process data relating to the tool and/or machining parameters can be supplied directly to the artificial neural network of the control apparatus 34, as illustrated in FIG. 5 with arrow 58. The artificial neural network creates a state vector from the obtained measurement data relating to the tool and/or machining parameters and compares said state vector with the stored target state vectors. If an impermissible deviation or, respectively, discrepancy is found, the control apparatus 34 issues a corresponding warning message to the operator 48, as shown in FIG. 5 with arrow 60. On this basis and, if applicable, in consideration of the measurement data of the machined workpieces 44 made available (shown via arrow 52), the operator 48 can control the double-sided machine tool 40, in particular the actuators for influencing tool and/or operating parameters, as shown in FIG. 5 with arrow 62, in order to bring the continuously monitored and created state vector in line with at least one target state vector. In this case, the operator 48 thus decides on the consequences from the assessment of the received data. The operator 48 is supported in this by the control apparatus 34 as an anomaly detector.

[0061] FIG. 6 shows another automated variant of the procedure shown in FIG. 5. In this embodiment, the control apparatus 34, in particular its artificial neural network, further comprises a regulation apparatus 64 connected thereto, as shown in FIG. 6 with arrow 66. In the event of a deviation between the created state vector and the at least one target state vector that is found by means of the comparison, regulation intervention by the regulation apparatus 64 in actuators of the double-sided machine tool 40 takes place without the intervention of the operator 48. This results in an adjustment of the recorded tool and/or machining parameters of the double-sided machine tool 40, as illustrated in FIG. 6 with arrow 68. All of the associated data can be stored in the data memory 46. The control apparatus 34 and the regulation apparatus 64, which are, for example, designed to be integral, can control the tool and/or operating parameters of the double-sided machine tool 40 based on an adjustment rule stored, for example, in the regulation apparatus 64. This adjustment rule may, for example, contain particular control instructions for particular established deviations of the tool and/or machining parameters created by an operator 48, according to which rule the control apparatus 34 and the regulation apparatus 64 control actuators.

[0062] FIG. 7 shows another embodiment of the procedure explained with reference to FIG. 6. The operator 48 is also involved in this embodiment. The operator 48 also obtains the process data relating to the recorded tool and machining parameters, as shown in FIG. 7 with arrow 70, and obtains the process data relating to the machined workpieces, as shown with arrow 52. Finally, the operator 48 also obtains the control commands performed by the control apparatus 34, as shown in FIG. 7 with arrow 72. On this basis, the respectively performed regulation can be monitored by the operator 48 and, if applicable, the regulation of the regulation apparatus 64 can be adjusted in a suitable manner, as shown in FIG. 7 with arrow 74.

[0063] FIG. 8 presents another embodiment of possible training of artificial neural networks as anomaly detectors. Here, training is done proceeding from the control apparatus 34 with an already pretrained artificial neural network, for example as explained above with reference to FIG. 4. Said control apparatus 34 issues any deviations or, respectively, anomaly data to the operator 48, as shown in FIG. 8 with arrow 60 and explained above with reference to FIG. 5. On this basis, the operator 48 can train an additional artificial neural network 76 in that said additional artificial neural network 76 is fed (further) target state vectors to acceptable tool and/or machining parameters of the double-sided machine tool 40 during operation of the double-sided machine tool 40, as shown in FIG. 8 with arrow 78. The additional artificial neural network 76 may be an untrained additional artificial neural network 76. However, it may also be an already (pre)trained additional artificial neural network 76, for example a duplicate of the neural network of the control apparatus 34. On this basis, specialized training of the additional artificial neural network 76 can be trained at the start of a production operation for the respective individual process parameters of the application scenario of the double-sided machine tool 40 based on the artificial neural network of the control apparatus 34 (pre)trained, for example, for the generic type of double-sided machine tool 40. After said training, it is possible for the additional artificial neural network 76 to replace the previously trained artificial neural network of the control apparatus 34.

[0064] Another embodiment of the invention will be explained based on FIGS. 9 and 10. This embodiment includes an additional artificial neural network 86 that is designed for machine learning. It may be a LCS, i.e., an artificial intelligence system. In the embodiment shown in FIG. 9, measurement data of the sensors relating to tool and/or machining parameters are supplied to the data memory 46, on the one hand, and to the control apparatus 34, on the other hand, as shown in FIG. 9 with arrow 80. Workpiece data, in particular measurement data relating to the geometry of the machined workpieces 44, are also supplied to both the data memory 46 and the control apparatus 34, as shown in FIG. 9 with arrow 82. The control apparatus 34 is also in exchange with the data memory 46, as shown in FIG. 9 with arrow 84. In FIG. 9, an additional artificial neural network 86 is shown, which is associated with the control apparatus 34. Said additional artificial neural network 86 may also be combined with the artificial neural network of the control apparatus 34. Said additional artificial neural network 86 is designed for machine learning and in particular forms a LCS.

[0065] The measurement data relating to the geometry of the machined workpieces 44 are also fed to the additional artificial neural network 86 (shown via arrow 82). If an inadmissible deviation between the currently recorded state vector and the acceptable values of the tool and/or machining parameters stored as target state vectors is found by the control apparatus 34, in particular its artificial neural network, during operation of the double-sided machine tool 40, a corresponding anomaly signal is output to the additional artificial neural network 86, as shown in FIG. 9 with arrow 88. Measurement data from the past can also be available to the additional artificial neural network 86 from the data memory 46. On this basis, the additional artificial neural network 86 can autonomously make decisions about the change of particular process parameters, in particular the control of actuators for influencing the recorded tool and/or machining parameters and control the actuators accordingly, as shown in FIG. 9 with arrow 90. In this way, maximum automation and autonomy can be achieved.

[0066] It is worth noting that for each target value of a target state vector there is a measured value such that deviations to the target value can be analyzed. However, it is not necessary that a target value is associated to each measured value such as, for example, polishing pad temperatures whose development can be monitored and analyzed over the whole process procedure.

[0067] FIG. 10 shows another embodiment of the variant shown in FIG. 9. In particular, FIG. 10 shows a system in accordance with embodiments of the invention comprising at least two double-sided machine tools 40. Of course, the system could also comprise more than two double-sided machine tools 40, illustrated in FIG. 10 by three points. In FIG. 10, two plants 92i that may in each case correspond in terms of their design and function to the embodiment according to FIG. 9 are shown as dashed blocks for illustrative purposes. The plants 92i may also be designed differently, for example to achieve different aims such as an optimal wafer quality, maximum output, etc. Said plants are connected to a common data memory 46 (shown via arrows 80, 84). Moreover, a higher-level artificial neural network 94, which, for example, also comprises a LCS and which may also be linked to an operator 48, is provided in the system shown in FIG. 10. The higher-level artificial neural network 94 is also connected to the data memory 46, as shown with arrow 96. Moreover, the higher-level artificial neural network 94 obtains the control commands executed in each case by the additional artificial neural network 86, as shown in FIG. 10 with arrow 98. On this basis, the higher-level artificial neural network 94 can further optimize or specialize the additional artificial neural networks 86 of the plants 92i, for example by specifying collective or individual control rules and/or target state vectors for the individual plants 92i.

THE FOLLOWING IS A LIST OF REFERENCE SIGNS USED IN THIS SPECIFICATION AND IN THE DRAWINGS

[0068] 10 Upper support disk [0069] 12 Lower support disk [0070] 14 Upper working disk [0071] 16 Lower working disk [0072] 18 Working gap [0073] 20 First distance-measuring apparatus [0074] 22 Second distance-measuring apparatus [0075] 24 Third distance-measuring apparatus [0076] 26 First fastening location [0077] 28 Second fastening location [0078] 30 Pressure volume [0079] 32 Dynamic pressure line [0080] 34 Control apparatus [0081] 36 Convex deformation [0082] 38 Concave deformation [0083] 50 Arrow [0084] 52, 54, 56 Arrow [0085] 58, 60, 62 Arrow [0086] 66, 68, 70 Arrow [0087] 72, 74, 78 Arrow [0088] 80, 82, 84 Arrow [0089] 88, 90, 96 Arrow [0090] 98 Arrow [0091] 40 Double-sided machine tool [0092] 42 Unmachined workpieces [0093] 44 Machined workpieces [0094] 46 Data memory [0095] 48 Operator [0096] 64 Regulation apparatus [0097] 76, 86 Additional artificial neural network [0098] 94 Higher-level artificial neural network [0099] 92i Plants