System and method for controlling a cutting tool during cutting operations

Abstract

The present invention relates to a system and method for controlling a cutting tool during cutting operations. [Solution] The present invention relates to a system (100) for controlling a cutting tool (110) during a cutting operation comprising a sequence of n operation steps, wherein each operation step generates an operation state, and the system (100) comprises a control unit (120) operably connected to the cutting tool (110) and at least one sensor (112) operably connected to the control unit (120), wherein the control unit (120) is configured to compare a measured value of the operation state for the current operation step with an expected value of the operation state for the current operation step, determine the difference between them, and determine an adjusted expected value of the operation state for the operation step following the current operation step based on this difference.

Description

Technical Field

[0001] The present invention relates to a system and method for controlling a cutting tool during a cutting operation.

Background Art

[0002] When performing a cutting operation using a cutting tool, in order to achieve a high quality of the operation performed and to achieve the maximum life of the cutting tool, it is essential to have the stability of the rigidity of the cutting operation, for example, not having an excessive vibration amount, not having an excessive temperature, etc.

[0003] To maintain stability in the cutting operation, an operation control unit is generally configured to adapt the cutting data parameters of the cutting operation, for example, to reduce the feed rate or the cutting speed, in response to an input from an external sensor arranged to detect the actual cutting state. Such an operation control unit is disclosed, for example, in WO2018 / 108352 A1.

[0004] A problem with these systems is that, in order to set the cutting data parameters required for stable operation, the operation has to be performed in an unstable state for some time for the sensor to react. As a result, the workpiece on which the operation is being performed by the cutting tool has areas with poor surface quality. Depending on where these areas are located on the workpiece, there is a risk that the workpiece has to be discarded. Furthermore, the wear of the cutting tool is accelerated, which results in a reduction in the life of the cutting tool.

Summary of the Invention

[0005] An object of the present invention is to overcome or at least partially overcome the above problems by introducing a system and method for controlling a cutting tool during a cutting operation.

[0006] The object of the present invention is a system for controlling a cutting tool during a cutting operation, wherein the cutting operation comprises a sequence of n operation steps, where n is an integer ≥ 2, and each operation step produces an operation state, the operation state includes at least one of vibration value, temperature value, force value and sound level value, and the system - A control unit operably connected to the cutting tool, - At least one sensor operably connected to the control unit and Equipped with, At least one sensor is configured to generate a measurement signal indicating a measured value of the operating state for the current operating step. The control unit comprises a processing circuit and a memory, the memory containing instructions that can be executed by the processing circuit, and the processing circuit provides the control unit with - Receiving a cutting program for cutting operations, wherein for each of the n operation steps, the cutting program is - At least one initial cutting data parameter, wherein at least one initial cutting data parameter has a parameter value, and at least one initial cutting data parameter includes at least one of cutting depth, radial engagement, feed / rotation, feed / cutting edge and cutting speed, - An expected value of the operating state, wherein the expected value of the operating state is a function of at least one initial cutting data parameter, - A predetermined operating state threshold for the operation step and It is equipped to receive cutting programs. It is configured to perform the following: The processing circuit is located in the control unit. - Controlling the cutting tool according to the cutting program, - Receiving a measurement signal from at least one sensor, - Compare the measured value of the operating state for the current operating step with the expected value of the operating state for the current operating step, and determine the difference between them. - Determining an adjusted expected value of the operating state for each subsequent operating step based on the difference, wherein the adjusted expected value of the operating state is a prediction made by the first machine learning model. - For each operation step following the current operation step, determine whether the adjusted expected value of the operating state is higher than a predetermined operating state threshold. - For each operating step in which it is determined that the adjusted expected value of the operating state is higher than a predetermined operating state threshold, the generation of at least one adjusted cutting data parameter includes a reduction in the parameter value of at least one initial cutting data parameter. - Update the cutting program with at least one adjusted cutting data parameter, - Controlling the cutting tool according to the updated cutting program and This is achieved by a system that is further configured to perform this action.

[0007] By predicting the operating state for a specific operating step before that step is executed in a cutting operation, it is possible to take necessary actions to prevent the cutting tool from operating in an unstable operating mode. Reducing the parameter value of at least one initial cutting data parameter can achieve a less aggressive cutting operation, which reduces the risk of the cutting tool operating in an unstable operating mode. This reduces the risk of damage to the workpiece being operated on by the cutting tool. System efficiency is improved because action is only required for operating steps where the adjusted expected value of the operating state is determined to be higher than a predetermined operating state threshold for that operating step.

[0008] The predetermined operating state threshold depends on the actions performed in the operating step. Some actions may allow for higher thresholds than others.

[0009] According to one embodiment, a predetermined operating state threshold is set based on user preference, for example, according to how much margin the user requests when performing a cutting operation. According to one embodiment, the predetermined operating state threshold is equal to the expected value of the operating state for the operation step. According to one embodiment, the predetermined operating state threshold is higher than the expected value of the operating state for the operation step.

[0010] According to one embodiment, the adjusted expected value of the operating state is an adjusted function of at least one initial cutting data parameter.

[0011] The cutting operation is preferably a metal cutting operation such as turning, milling, drilling, or boring.

[0012] The cutting tool is preferably a metal cutting tool such as a turning tool, milling tool, drilling tool, or boring tool.

[0013] According to one embodiment, the cutting tool is configured to be attached to a machine unit.

[0014] According to one embodiment, the cutting tool comprises at least one cutting element securely mounted to a tool holder. The cutting element may be, for example, a cutting insert, an end mill, or a drill bit.

[0015] According to one embodiment, the cutting element is an indexable cutting insert.

[0016] According to one embodiment, the cutting element is a cubic boron nitride (CBN) cutting element, a polycrystalline diamond (PCD) cutting element, a cemented carbide cutting element, a cermet cutting element, or a ceramic cutting element.

[0017] The control unit is any type of electronic device suitable for performing information analysis, such as a computer or a smartphone.

[0018] According to one embodiment, the received cutting program is a cutting program recommended by a cutting tool manufacturer.

[0019] According to one embodiment, the cutting program depends on at least one of the following parameters: the type of cutting tool used in the cutting operation, the material of the workpiece on which the operation by the cutting tool is being performed, workpiece clamping, and machining limitations.

[0020] According to one embodiment, the cutting program is received by the control unit via an interface configured to input the cutting program. The cutting program can be received via automatic input from an external source such as a cutting tool manufacturer, or via manual input via a user interface such as a keyboard or a touch screen.

[0021] According to one embodiment, the expected value of the operating state is a predicted value based on the historical operations of similar cutting tools used in similar cutting operations.

[0022] According to one embodiment, the expected value of the operating state is predicted by a machine learning model based on, for example, historical operation data collected by a cutting tool manufacturer. During, for example, the testing of a cutting tool by a cutting tool manufacturer during the development process, a large amount of data is available for training the machine learning model by collecting historical operation data, which will result in a reliable prediction of the expected value of the operating state.

[0023] Based on the difference between the measured value and the predicted value of the operating state for the current operating step, the first machine learning model is configured to predict how the predicted value of the operating step after the current operating step should be adjusted in order to better cope with the reality. According to one embodiment, this prediction is based on the historical operation data of similar cutting tools used in similar cutting operations. According to one embodiment, the historical operation data is collected by the cutting tool manufacturer, for example, during the testing of the cutting tool by the cutting tool manufacturer during the development process.

[0024] The first machine learning model is any suitable machine learning model known in the art, such as a supervised learning model or an unsupervised learning model.

[0025] At least one sensor is at least any one of a vibration sensor, a temperature sensor, a force sensor, or a sound sensor.

[0026] According to one embodiment, at least one sensor is provided on the cutting tool.

[0027] According to one embodiment, at least one sensor is provided on the machine unit.

[0028] According to one embodiment, at least one adjusted cutting data parameter includes a predetermined minimum parameter value, and the step of generating at least one adjusted cutting data parameter includes reducing the parameter value of at least one initial cutting data parameter to a value greater than or equal to the predetermined minimum parameter value.

[0029] According to one embodiment, the predetermined operating threshold value is a function of at least one threshold cutting data parameter having a threshold parameter value, and the generation of at least one adjusted cutting data parameter - predicting the threshold parameter value of at least one threshold cutting data parameter by a second machine learning model; - A step of reducing the parameter value of at least one initial cutting data parameter to a value equal to or lower than a threshold parameter value. Includes.

[0030] By reducing the parameter value of at least one initial cutting data parameter to a value equal to or lower than the threshold parameter value, the risk of the cutting tool operating in an unstable operating mode is further reduced by ensuring that the margin required by the user is met when performing the cutting operation.

[0031] A second machine learning algorithm is configured to predict threshold parameter values ​​by solving an inverse problem that determines which parameter values ​​of cutting data parameters result in a predetermined operating threshold state value for each operating step. According to one embodiment, this prediction is based on historical operating data of similar cutting tools used in similar cutting operations. According to one embodiment, historical operating data is collected by the cutting tool manufacturer, for example, during testing of the cutting tool by the cutting tool manufacturer during the development process.

[0032] The second machine learning model is any suitable machine learning model known in the art, such as a supervised learning model or an unsupervised learning model.

[0033] According to one embodiment, the processing circuit is connected to the control unit, - For each operation step following the current operation step, determine whether the adjusted expected value of the operating state is lower than the expected value of the operating state. - For each operating step in which the adjusted expected value of the operating state is lower than the expected value of the operating state, the generation of at least one modified cutting data parameter includes increasing the parameter value of at least one initial cutting data parameter to a value equal to or lower than a threshold parameter value, - Update the cutting program with at least one corrected cutting data parameter. It is further configured to perform the following action.

[0034] If the adjusted expected value of the operating state is determined to be lower than the expected value of the operating state, this indicates that the cutting operation will be performed with a higher margin than necessary to avoid the cutting tool operating in an unstable operating mode. By increasing the parameter values ​​of the initial cutting data parameters, the cutting operation will be performed with higher operational efficiency.

[0035] According to one embodiment, the system further comprises a user interface operably connected to a control unit, and the processing circuit is connected to the control unit. - To generate a warning signal indicating an operation step among the operation steps in which the adjusted expected value of the operation state is determined to be higher than a predetermined operation state threshold, - To present a warning indicator via the user interface based on the generated warning signal. It is further configured to perform the following action.

[0036] Through the user interface, a warning indicator is displayed for operation steps in which the adjusted expected value of the operating state is determined to be higher than a predetermined operating state threshold. This allows necessary actions to be taken for those operation steps to prevent the cutting tool from operating in an unstable operating mode. Since action is only required for operation steps in which the adjusted expected value of the operating state is determined to be higher than a predetermined operating state threshold, the efficiency of the system is improved.

[0037] The user interface is any type of user interface suitable for presenting warning indicators, such as a display or a speaker.

[0038] According to one embodiment, the processing circuit is connected to the control unit, - To determine whether the measured value of the operating state for the current operating step is higher than a predetermined operating state threshold for the current operating step, - A stop signal is generated when the measured value of the operating state for the current operating step is determined to be higher than a predetermined operating state threshold for the current operating step. - Stopping the cutting operation based on a stop signal. It is further configured to perform the following action.

[0039] The risk of damaging the workpiece being worked on by the cutting tool is further reduced by stopping the cutting operation when the measured value of the operating state for the current operation step is determined to be higher than a predetermined operating state threshold.

[0040] According to one embodiment, the system further comprises a database operably connected to a control unit, and a processing circuit is provided to the control unit. - Store the received measurement signals for each operation step in a database. It is further configured to perform the following action.

[0041] By storing the received measurement signals for each operation step in a database, data on the actual performance of the cutting operation is collected. In embodiments where the expected values ​​of the operating state are determined by a machine learning model, the collected data may be used to retrain the model to improve the prediction of the expected values ​​of the operating state.

[0042] The object of the present invention is a method for controlling a cutting tool during a cutting operation, wherein the cutting operation comprises a sequence of n operation steps, where n is an integer ≥ 2, each operation step produces an operation state, the operation state includes at least one of vibration value, temperature value, force value and sound level value, at least one sensor is configured to generate a measurement signal indicating a measured value of the operation state for the current operation step, the cutting tool and at least one sensor are operably connected to a control unit, the control unit comprises a processing circuit and a memory, the memory includes instructions executable by the processing circuit, the processing circuit is configured to cause the control unit to carry out the method, and the method is - A step of receiving a cutting program for cutting operations, wherein for each of the n operation steps, the cutting program is - At least one initial cutting data parameter, wherein at least one initial cutting data parameter has a parameter value, and at least one initial cutting data parameter includes at least one of cutting depth, radial engagement, feed / rotation, feed / cutting edge and cutting speed, - An expected value of the operating state, wherein the expected value of the operating state is a function of at least one initial cutting data parameter, - A predetermined operating state threshold for the operation step and The process includes receiving a cutting program, - A step of controlling the cutting tool according to the cutting program, - A step of receiving a measurement signal from at least one sensor, - A step of comparing the measured value of the operating state for the current operating step with the expected value of the operating state for the current operating step, and determining the difference between them. - A step of determining an adjusted expected value of the operating state for each operation step after the current operation step based on the difference, wherein the adjusted expected value of the operating state is a prediction made by a first machine learning model, - For each operation step following the current operation step, a step is to determine whether the adjusted expected value of the operating state is higher than a predetermined operating state threshold. - For each operation step in which it is determined that the adjusted expected value of the operating state is higher than a predetermined operating state threshold, a step of generating at least one adjusted cutting data parameter, wherein the generation of at least one adjusted cutting data parameter includes a reduction in the parameter value of at least one initial cutting data parameter, - A step of updating the cutting program with at least one adjusted cutting data parameter, - A step of controlling the cutting tool according to the updated cutting program and This is further achieved by methods including those mentioned above.

[0043] According to one embodiment, a predetermined operating threshold state value is a function of at least one threshold cutting data parameter having a threshold parameter value, and the step of generating at least one adjusted cutting data parameter is: - A second machine learning model predicts the threshold parameter value of at least one threshold cutting data parameter, - A step of reducing the parameter value of at least one initial cutting data parameter to a value equal to or lower than a threshold parameter value. Includes.

[0044] According to one embodiment, this method is - For each operation step following the current operation step, a step is to determine whether the adjusted expected value of the operating state is lower than the expected value of the operating state. - A step of generating at least one modified cutting data parameter for each operating step in which the adjusted expected value of the operating state is lower than the expected value of the operating state, wherein the generation of at least one modified cutting data parameter includes increasing the parameter value of at least one initial cutting data parameter to a value equal to or lower than a threshold parameter value, - The step of updating the cutting program with at least one modified cutting data parameter and It also includes.

[0045] According to one embodiment, the control unit is operably connected to the user interface, and this method is - A step of generating a warning signal indicating an operation step among operation steps in which the adjusted expected value of the operation state is determined to be higher than a predetermined operation state threshold, - A step of presenting a warning indicator via the user interface based on the generated warning signal. It also includes.

[0046] According to one embodiment, this method is - A step of determining whether the measured value of the operating state for the current operating step is higher than a predetermined operating state threshold for the current operating step, - A step of generating a stop signal when it is determined that the measured value of the operating state for the current operating step is higher than a predetermined operating state threshold for the current operating step, - Steps to stop the cutting operation based on a stop signal and It also includes.

[0047] According to one embodiment, the control unit is operably connected to the database, and this method is - A step to save the received measurement signals for each operation step to a database. It also includes.

[0048] The object of the present invention is further achieved by a computer program comprising computer-readable code means to be operated in the system described above, wherein when the computer-readable code means is operated in the system, the computer program causes the system to carry out the method described above.

[0049] The object of the present invention is further achieved by a carrier containing the computer program described above, wherein the carrier is one of an electronic signal, an optical signal, a wireless signal, or a computer-readable storage medium. [Brief explanation of the drawing]

[0050] [Figure 1] This figure schematically illustrates a system for controlling a cutting tool during cutting operations, according to one embodiment of the present invention. [Figure 2] This is a flowchart of a method for controlling a cutting tool during a cutting operation, according to one embodiment of the present invention. [Modes for carrying out the invention]

[0051] Next, the embodiments disclosed will be described more thoroughly below with reference to the accompanying drawings illustrating several embodiments of the present invention. However, the present invention can be embodied in many different forms and should not be construed as being limited to the embodiments described herein. Rather, these embodiments are provided as examples so that this disclosure may be thorough and complete and so as to convey the scope of the invention to those skilled in the art. Similar reference numerals refer to similar elements throughout. Elements shown in the drawings do not necessarily adhere to a constant scale. Some elements may be enlarged to clearly show them.

[0052] Figure 1 schematically shows a system (100) for controlling a cutting tool (110) during a cutting operation, according to one embodiment of the present invention. The system (100) comprises a cutting tool (110) in the form of a milling cutter operably connected to a control unit (120). The control unit (120) comprises a processing circuit (122) and a memory (124), the memory (124) containing instructions that can be executed by the processing circuit (122). The system (100) further comprises a sensor (112) attached to the cutting tool (110). The sensor (112) is operably connected to the control unit (120) and is configured to generate a measurement signal indicating a measured value of the operating state for the current operating step. The system (100) further comprises a user interface (130) in the form of a monitor provided with a display and a database (140), which are operably connected to the control unit (120).

[0053] Figure 2 shows a flowchart of a method (200) implemented by a system (100) for controlling a cutting tool (110) during a cutting operation, according to one embodiment of the present invention, wherein the cutting operation comprises a sequence of n operation steps, where n is an integer ≥ 2, and each operation step produces an operation state, the operation state including at least one of vibration value, temperature value, force value and sound level value. The method (200) is, - A step (202) of receiving a cutting program for cutting operations, wherein for each of the n operation steps, the cutting program is - At least one initial cutting data parameter, wherein at least one initial cutting data parameter has a parameter value, and at least one initial cutting data parameter includes at least one of cutting depth, radial engagement, feed / rotation, feed / cutting edge and cutting speed, - An expected value of the operating state, wherein the expected value of the operating state is a function of at least one initial cutting data parameter, - A predetermined operating state threshold for the operation step and The process includes a step (202) of receiving a cutting program, - A step (204) of controlling the cutting tool (110) according to the cutting program, - A step (206) of receiving a measurement signal from at least one sensor (112), - A step (208) of comparing the measured value of the operating state for the current operating step with the expected value of the operating state for the current operating step and determining the difference between them, - A step (210) to determine whether the measured value of the operating state for the current operating step is higher than a predetermined operating state threshold for the current operating step, - A step (212) to generate a stop signal if it is determined that the measured value of the operating state for the current operating step is higher than a predetermined operating state threshold for the current operating step, - Step (214) of stopping the cutting operation based on a stop signal, - A step (216) of determining an adjusted expected value of the operating state for each subsequent operating step after the current operating step based on the difference, wherein the adjusted expected value of the operating state is a prediction made by a first machine learning model, - For each operation step following the current operation step, a step (218) is taken to determine whether the adjusted expected value of the operation state is higher than a predetermined operation state threshold, - A step (220) of generating a warning signal indicating an operation step among operation steps in which the adjusted expected value of the operation state is determined to be higher than a predetermined operation state threshold, - A step (222) of presenting a warning indicator via a user interface (130) based on the generated warning signal, - For each operation step in which it is determined that the adjusted expected value of the operating state is higher than a predetermined operating state threshold, a step (224) to generate at least one adjusted cutting data parameter, wherein the generation of at least one adjusted cutting data parameter includes a reduction in the parameter value of at least one initial cutting data parameter. Includes, The predetermined operating threshold state value is a function of at least one threshold cutting data parameter having a threshold parameter value, and the step (224) of generating at least one adjusted cutting data parameter is, - A second machine learning model predicts the threshold parameter value of at least one threshold cutting data parameter (226), - A step (228) of reducing the parameter value of at least one initial cutting data parameter to a value equal to or lower than the threshold parameter value. Includes, Method (200) is, - Step (230) of updating the cutting program with at least one adjusted cutting data parameter, - For each operation step following the current operation step, a step (232) is taken to determine whether the adjusted expected value of the operating state is lower than the expected value of the operating state. - A step (234) of generating at least one modified cutting data parameter for each operating step in which the adjusted expected value of the operating state is lower than the expected value of the operating state, wherein the generation of at least one modified cutting data parameter includes increasing the parameter value of at least one initial cutting data parameter to a value equal to or lower than a threshold parameter value, - Step (236) of updating the cutting program with at least one modified cutting data parameter, - A step (238) of controlling the cutting tool (110) according to the updated cutting program, - Step (240) of saving the received measurement signals for each operation step to a database (140) and It also includes.

[0054] Figure 2 shows an example of the method steps. This method may include additional steps. Some of the method steps may be performed simultaneously. The method steps may be performed in different orders.

[0055] The above description contains several specificities, but these specificities should not be interpreted as limiting the scope of the concepts described herein, but merely as providing examples of some exemplary embodiments of the concepts described herein. It will be understood that the scope of the concepts described herein fully encompasses other embodiments that may be apparent to those skilled in the art, and therefore the scope of the concepts described herein is not limited. References to elements in the singular form mean "one or more" rather than "only" unless explicitly stated so. All structural and functional equivalents to the elements of the embodiments described above, known to those skilled in the art, are expressly incorporated herein by reference and are encompassed herein. In illustrative figures, dashed lines generally indicate that the features within the dashed lines are optional.

Claims

1. A system (100) for controlling a cutting tool (110) during a cutting operation, wherein the cutting operation comprises a sequence of n operation steps, where n is an integer ≥ 2, each operation step produces an operation state, the operation state includes at least one of vibration value, temperature value, force value and sound level value, and the system (100) - A control unit (120) operably connected to the cutting tool (110), - At least one sensor (112) operably connected to the control unit (120) and Equipped with, The at least one sensor (112) is configured to generate a measurement signal indicating a measured value of the operating state for the current operating step, In a system (100) comprising a control unit (120) including a processing circuit (122) and a memory (124), wherein the memory (124) includes instructions that can be executed by the processing circuit (122), the processing circuit (122) provides the control unit (120) with respect to the control unit (120). - Receiving a cutting program for the cutting operation, wherein for each of the n operation steps, the cutting program is - At least one initial cutting data parameter, wherein the at least one initial cutting data parameter has a parameter value, and the at least one initial cutting data parameter includes at least one of cutting depth, radial engagement, feed / rotation, feed / cutting edge and cutting speed, - The expected value of the operating state, wherein the expected value of the operating state is a function of the at least one initial cutting data parameter, - A predetermined operating state threshold for the aforementioned operating step and It is equipped to receive cutting programs. It is configured to perform the following: The processing circuit (122) is connected to the control unit (120), - Controlling the cutting tool (110) according to the cutting program, - Receiving the measurement signal from at least one of the sensors (112), - Comparing the measured value of the operating state for the current operating step with the expected value of the operating state for the current operating step, and determining the difference between them, - Determining an adjusted expected value of the operating state for each operation step after the current operation step based on the difference, wherein the adjusted expected value of the operating state is a prediction made by a first machine learning model, - For each operation step following the current operation step, determine whether the adjusted expected value of the operation state is higher than the predetermined operation state threshold, - For each of the operation steps in which it is determined that the adjusted expected value of the operation state is higher than the predetermined operation state threshold, the generation of the at least one adjusted cutting data parameter includes a reduction of the parameter value of the at least one initial cutting data parameter. - Updating the cutting program with at least one adjusted cutting data parameter, - Controlling the cutting tool (110) according to the updated cutting program and A system (100) further configured to perform the following.

2. The predetermined operating threshold state value is a function of at least one threshold cutting data parameter having a threshold parameter value, and the generation of the at least one adjusted cutting data parameter is - A step of predicting the threshold parameter value of the at least one threshold cutting data parameter using a second machine learning model, - A step of reducing the parameter value of the at least one initial cutting data parameter to a value equal to or lower than the threshold parameter value. The system (100) according to claim 1, including the above.

3. The processing circuit (122) is connected to the control unit (120), - For each operation step following the current operation step, determine whether the adjusted expected value of the operation state is lower than the expected value of the operation state. - For each of the operating steps in which the adjusted expected value of the operating state is lower than the expected value of the operating state, to generate at least one modified cutting data parameter, wherein the generation of the at least one modified cutting data parameter includes increasing the parameter value of the at least one initial cutting data parameter to a value equal to or lower than the threshold parameter value, - Updating the cutting program with at least one of the modified cutting data parameters. The system (100) according to claim 2, further configured to perform the following.

4. The system (100) further comprises a user interface (130) operably connected to the control unit (120), and the processing circuit (122) is connected to the control unit (120), - To generate a warning signal indicating an operation step among the operation steps in which the adjusted expected value of the operation state is determined to be higher than the predetermined operation state threshold, - To present a warning indicator via the user interface (130) based on the generated warning signal. A system (100) according to any one of claims 1 to 3, further configured to perform the following.

5. The processing circuit (122) is connected to the control unit (120), - To determine whether the measured value of the operating state for the current operating step is higher than the predetermined operating state threshold for the current operating step, - A stop signal is generated when it is determined that the measured value of the operating state for the current operating step is higher than the predetermined operating state threshold for the current operating step. - Stop the cutting operation based on the stop signal. A system (100) according to any one of claims 1 to 3, further configured to perform the following.

6. The system (100) further comprises a database (140) operably connected to the control unit (120), and the processing circuit (122) is connected to the control unit (120), - The received measurement signals for each operation step are stored in the database (140). A system (100) according to any one of claims 1 to 3, further configured to perform the following.

7. A method (200) for controlling a cutting tool (110) during a cutting operation, wherein the cutting operation comprises a sequence of n operation steps, where n is an integer ≥ 2, each operation step produces an operation state, the operation state includes at least one of vibration value, temperature value, force value and sound level value, at least one sensor (112) is configured to generate a measurement signal indicating a measured value of the operation state for the current operation step, the cutting tool (110) and the at least one sensor (112) are operably connected to a control unit (120), the control unit (120) comprises a processing circuit (122) and a memory (124), the memory (124) includes instructions executable by the processing circuit (122), the processing circuit (122) is configured to cause the control unit (120) to perform the method (200), and the method (200) is: - A step (202) of receiving a cutting program for the cutting operation, wherein for each of the n operation steps, the cutting program is - At least one initial cutting data parameter, wherein the at least one initial cutting data parameter has a parameter value, and the at least one initial cutting data parameter includes at least one of cutting depth, radial engagement, feed / rotation, feed / cutting edge and cutting speed, - The expected value of the operating state, wherein the expected value of the operating state is a function of the at least one initial cutting data parameter, - A predetermined operating state threshold for the aforementioned operating step and The steps include receiving a cutting program (202), - A step (204) of controlling the cutting tool (110) according to the cutting program, - A step (206) of receiving the measurement signal from at least one sensor (112), - A step (208) of comparing the measured value of the operating state for the current operating step with the expected value of the operating state for the current operating step and determining the difference between them, - A step (216) of determining an adjusted expected value of the operating state for each operation step after the current operation step based on the difference, wherein the adjusted expected value of the operating state is a prediction made by a first machine learning model, - For each operation step following the current operation step, a step (218) of determining whether the adjusted expected value of the operation state is higher than the predetermined operation state threshold, - For each of the operation steps in which it is determined that the adjusted expected value of the operation state is higher than the predetermined operation state threshold, a step (224) of generating at least one adjusted cutting data parameter, wherein the generation of the at least one adjusted cutting data parameter includes a reduction of the parameter value of the at least one initial cutting data parameter, - Step (230) of updating the cutting program with at least one adjusted cutting data parameter, - A step (238) of controlling the cutting tool (110) according to the updated cutting program and A method (200) characterized by including the following:

8. The predetermined operating threshold state value is a function of at least one threshold cutting data parameter having a threshold parameter value, and the step (224) of generating the at least one adjusted cutting data parameter is, - A second machine learning model predicts the threshold parameter value of at least one threshold cutting data parameter (226), - A step (228) of reducing the parameter value of the at least one initial cutting data parameter to a value equal to or lower than the threshold parameter value. The method according to claim 7 (200), including the method according to claim 7.

9. The above method (200) is, - For each operation step following the current operation step, a step (232) is taken to determine whether the adjusted expected value of the operation state is lower than the expected value of the operation state. - A step (234) of generating at least one modified cutting data parameter for each of the operating steps in which the adjusted expected value of the operating state is lower than the expected value of the operating state, wherein the generation of the at least one modified cutting data parameter includes increasing the parameter value of the at least one initial cutting data parameter to a value equal to or lower than the threshold parameter value, - Step (236) of updating the cutting program with at least one modified cutting data parameter and The method according to claim 8 (200), further comprising:

10. The control unit (120) is operably connected to the user interface (130), and the method (200) is, - A step (220) of generating a warning signal indicating an operation step among the operation steps in which the adjusted expected value of the operation state is determined to be higher than the predetermined operation state threshold, - Step (222) of presenting a warning indicator via the user interface (130) based on the generated warning signal. The method according to any one of claims 7 to 9 (200), further comprising:

11. The above method (200) is, - A step (210) of determining whether the measured value of the operating state for the current operating step is higher than the predetermined operating state threshold for the current operating step, - A step (212) of generating a stop signal when it is determined that the measured value of the operating state for the current operating step is higher than the predetermined operating state threshold for the current operating step, - Step (214) of stopping the cutting operation based on the stop signal and The method according to any one of claims 7 to 9 (200), further comprising:

12. The control unit (120) is operably connected to the database (140), and the method (200) is, - Step (240) of saving the received measurement signals for each operation step in the database (140) The method according to any one of claims 7 to 9 (200), further comprising:

13. A computer program comprising computer-readable code means to be operated in the system (100) described in claim 1, wherein when the computer-readable code means is operated in the system (100), the computer program causes the system (100) to carry out the method (200) described in claim 7.

14. A carrier comprising the computer program described in claim 13, wherein the carrier is one of an electronic signal, an optical signal, a wireless signal, or a computer-readable storage medium.

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