Method for operating a handheld power tool

Abstract

The invention relates to a method for operating a handheld power tool, wherein the handheld power tool comprises an electric motor and the method comprises the following method steps: S1 determining a signal of an operating variable (200) of the electric motor (180); S2 determining an application category at least partially based on the signal of the operating variable (200); S3 providing comparison information at least partially based on the application category, comprising the following steps: S3a providing at least one model signal shape (240), wherein the model signal shape (240) can be assigned to a certain working progress of the handheld power tool (100); S3b providing a consistency threshold; S4 comparing the signal of the operating variable (200) with the model signal shape (240) and determining a consistency evaluation from the comparison, wherein the consistency evaluation is performed at least partially based on the consistency threshold; S5 identifying the working progress at least partially based on the consistency evaluation determined in method step S4. The invention also relates to a handheld power tool.

Description

Technical Field

[0001] The invention relates to a method for operating a handheld power tool and to a method provided for carrying out the handheld power tool. In particular, the invention relates to a method for screwing in or out a threaded element using a handheld power tool. Background Art

[0002] A rotary impact driver for tightening threaded elements, such as nuts and screws, is known from the prior art (see, for example, document EP3202537 A1). This type of rotary impact driver includes, for example, a structure in which the impact force is transmitted to the threaded element in the rotational direction by the rotary impact force of a hammer. A rotary impact driver having such a structure includes a motor, a hammer to be driven by the motor, an anvil, and a tool, the anvil being impacted by the hammer. The rotary impact driver also includes a position sensor, which senses the position of the motor, and a control device, which is coupled to the position sensor. The control device senses the collision of the collision mechanism, calculates the drive angle of the anvil caused by the collision based on the output of the position sensor, and controls the brushless DC motor based on the drive angle.

[0003] An electrically driven tool with an impact mechanism, in which the hammer is driven by a motor, is also known from document US Pat. No. 9,744,658. The rotary impact driver also includes a method for recording and reproducing motor parameters.

[0004] When using a rotary impact driver, the user needs to pay close attention to the progress of the work in order to react accordingly when certain machine properties change, such as when the impact mechanism is switched on or off, for example by stopping the electric motor and / or changing the speed via a manual switch. Since the user is often unable to react quickly or appropriately to the progress of the work, when using a rotary impact driver, for example, over-rotation of the screw may occur during the screwing-in process, and during the unscrewing process, the screw may fall out if the screw is unscrewed at too high a speed.

[0005] It is therefore generally desirable to automate the operation to a greater extent and to help the customer achieve a completely completed work schedule more easily and to reliably achieve high-quality, reproducible screw-in and screw-out processes.

[0006] Furthermore, the user should be assisted by machine-triggered reactions or routines of the appliance that are suitable for the work process. Examples of such machine-triggered reactions or routines include, for example, switching off the motor, changing the motor speed or triggering a notification to the user.

[0007] The provision of such intelligent tool functions can be achieved in particular by identifying an existing operating state. In the prior art, the identification of an existing operating state is carried out independently of determining the working progress or state of the application, for example by monitoring operating variables of the electric motor, such as the rotational speed and the motor current. The operating variables are checked in this case in terms of whether certain extreme values ​​and / or threshold values ​​have been reached. The corresponding evaluation methods work with absolute threshold values ​​and / or signal gradients.

[0008] The disadvantage here is that fixed extreme values ​​and / or threshold values ​​can actually only be perfectly set for one application. As soon as the application changes, the associated current or speed value or its time profile also changes, and the impact detection based on the set extreme values ​​and / or threshold values ​​or their time profile no longer works.

[0009] It can happen, for example, that an automatic shutdown based on the detection of an impact run in the individual application case when using self-tapping screws shuts off reliably in different speed ranges, whereas in other application cases when using self-tapping screws no shutdown is achieved.

[0010] Other methods for determining the operating mode in rotary impact drivers use additional sensors, such as acceleration sensors, in order to infer the operating mode that is occurring from the vibration state of the tool.

[0011] Disadvantages of this method are the additional cost outlay for the sensor and the loss in robustness of the handheld power tool, since the number of installed components and electrical connections increases compared to a handheld power tool without the sensor device.

[0012] Furthermore, the simple information "is the impact mechanism working?" is often not sufficient to be able to draw accurate conclusions about the progress of the work.

[0013] For example, when screwing in certain wood screws, the rotary impact mechanism is already activated very early, without the screw being completely screwed into the material, but the required torque already exceeds the so-called breakaway torque of the rotary impact mechanism.

[0014] A reaction based purely on the operating state of the rotary impact mechanism (impact operation / impact operation) is therefore not sufficient for a correct automatic system function of the tool (eg, shutdown).

[0015] Basically, there is the problem of automating the operation to a large extent also in other handheld power tools, such as hammer drills, so that the present invention is not limited to rotary impact drivers. Summary of the invention

[0016] The object of the present invention is to provide a method for operating a handheld power tool which is improved compared to the prior art and which at least partially eliminates the above-mentioned disadvantages or provides at least one alternative to the prior art. Another object is to provide a corresponding handheld power tool.

[0017] These objects are achieved by means of the respective subject matter of the independent claims. Advantageous embodiments of the invention are the subject matter of the respective dependent claims.

[0018] According to the present invention, a method for operating a handheld power tool is disclosed, wherein the handheld power tool has an electric motor. The method comprises the following steps:

[0019] S1 obtains the signal of the operating parameter of the electric motor;

[0020] S2 determines the application category at least partially based on the signal of the operating variable;

[0021] S3 provides comparison information based at least in part on the application category, including the following steps:

[0022] S3a provides at least one model signal shape, wherein the model signal shape can be assigned to a specific working progress of the hand-held power tool;

[0023] S3b provides a consistency threshold;

[0024] S4 compares the signal of the operating parameter with the model signal shape and obtains a consistency evaluation from the comparison, wherein the consistency evaluation is performed at least in part based on a consistency threshold;

[0025] S5 identifies the progress of the work at least partially based on the consistency evaluation determined in method step S4 .

[0026] The ascertainment of the signal of the operating variable also includes any signal processing of the measured signal, for example in the sense of classification or clustering of the measured signal.

[0027] The method according to the invention effectively assists the user of a handheld power tool in achieving reproducible, high-quality application results, wherein the method is characterized in that the application category is determined with a high degree of automation, which makes the preselection of specific applications customary in previous methods superfluous. This also avoids user errors that can occur everywhere when calling up specific machine programs.

[0028] The term "application category" will be briefly specified with the aid of an example. When assembling furniture, for example kitchen furniture, many different types of screws must be screwed into different carrier materials. On the one hand, hinges must be fastened with the aid of small wood screws, for example; on the other hand, the cabinet itself must be fastened to the wall using wood wedges and larger screws. According to the invention, for the screwing situations that occur, one of the application categories "small wood screws" and "large wood wedge screws" is determined as a function of the signals of the operating variables assigned to these screwing situations. Within the scope of the method according to the invention, on this basis, a work progress is now further identified, which in turn can be used as a trigger for the automated execution of a specific routine or reaction of the handheld power tool.

[0029] The invention thus makes it possible to provide the user with assistance with which a constant quality of work can be achieved with the lowest possible effort.

[0030] A person skilled in the art will recognize that the characteristics of the model signal shape include the signal shape of the continuous progress of the working process. In one embodiment, the model signal shape is a state-specific model signal shape, which is state-specific for a certain working progress of the hand-held power tool. Examples of such working progress include: the screw head abutting against the fastening base, the free rotation of a loosened screw, the activation or deactivation of the rotary impact mechanism of the hand-held power tool, reaching a certain screw-in depth of a connecting element to be screwed in with the hand-held power tool, and / or the impact of the rotary impact mechanism without further rotation of the impacted element or the tool recording part.

[0031] The concept for detecting the progress of work by means of operating variables from tool-internal measured variables, such as the rotational speed of an electric motor, has proven to be particularly advantageous, since the progress of work can be determined in this way particularly reliably and largely independently of the general operating state of the tool or its application.

[0032] In this case, in particular additional sensor units, such as acceleration sensor units, for sensing tool-internal measurement variables are substantially omitted, so that the method according to the invention is used substantially only for detecting the progress of the work.

[0033] In some embodiments of the present invention, the method further comprises the following method steps:

[0034] The SM performs a machine learning phase according to at least two or more exemplary applications, wherein the exemplary applications include reaching a determined work progress;

[0035] Therein, determining the application category in step S2 and providing the model signal shape and / or consistency threshold in step S3 are performed at least partially based on the application category generated in the machine learning phase and the model signal shape and / or consistency threshold assigned to the application category.

[0036] In certain specific embodiments of the invention, the term "machine learning phase" includes the fact that the impact driver evaluates, for example, by means of an impact quality analysis, which will be explained in more detail below, different profiles of operating variables stored in the application cases or exemplary applications implemented by the user. In addition, the impact driver autonomously stores at which consistency thresholds the user reacted to the individual profiles, for example by reducing the speed or switching off the machine. With a sufficiently large data base, the impact driver can now establish a connection between similar application case profiles and consistency thresholds by means of data analysis methods. The impact driver can thus autonomously classify the application profiles and assign specific consistency thresholds to the classes.

[0037] In the above-mentioned structural example of kitchen furniture, the impact driver according to the present invention used by the user learns over time and with a sufficiently large amount of data: whether there is a tightening situation of "wood screws" and "wood wedge threaded parts", and when the corresponding determined work progress is reached.

[0038] In other embodiments, at least one exemplary application is read into a memory connected to or integrated into the handheld power tool. In this context, "reading in" is to be understood as reading in one or more screwing characteristics, i.e., exemplary signals of operating variables of the electric motor. The screwing characteristics can be read in, for example, by means of a connection to the Internet.

[0039] In addition, method step SM can also be to store and classify the signals of the operating variables assigned to the exemplary application in at least one or more application categories. Here, "classification" should also be understood as assigning the exemplary signals of the operating variables to at least one or more application categories.

[0040] In some embodiments of the present invention, the method step SM further comprises the following method steps:

[0041] SMa determines, stores and classifies model signal shapes associated with these exemplary applications at least partially as a function of corresponding signals of operating variables when specific work progress points in time are reached.

[0042] In some embodiments of the present invention, the method step SM further comprises the following method steps:

[0043] SMb determines, stores and classifies the consistency threshold values ​​associated with the exemplary applications at least partially as a function of corresponding signals of the operating variables when the determined time of the work progress is reached.

[0044] In some embodiments of the present invention, the method step SM further comprises the following method steps:

[0045] Based on the stored model signal shapes and consistency thresholds assigned to the exemplary applications, SMc determines and stores the consistency thresholds assigned to the application categories.

[0046] In some embodiments of the present invention, the method further comprises the following method steps:

[0047] S6 executes a first routine of the handheld power tool at least partially based on the work progress detected in method step S5 .

[0048] According to the invention, the handheld power tool can thus react to different application situations. The reaction may be, for example, an immediate reduction in speed, an immediate stop of the motor, a time-staggered reduction in speed and / or a time-staggered stop of the motor. In addition, a combination of different reactions is also possible.

[0049] In the above-mentioned example of a kitchen furniture structure, the impact driver according to the invention in this embodiment recognizes during screwing that a small wood screw is being screwed in and automatically executes the user's learned procedure or reaction at the right time, such as reducing the speed. If a wood wedge screw connection is subsequently performed, the appliance also autonomously recognizes this other screwing situation and automatically reacts at the right time, for example by reducing the speed.

[0050] This means that, according to the invention, it can be ensured, for example, that all screws of the same screw type are screwed in to the same depth in a reproducible manner, which also simplifies the work and improves the quality of the work.

[0051] In an alternative specific embodiment, it is provided that, in the case of an unknown application, the first routine is estimated with the aid of known application cases having similar characteristics or application categories.

[0052] In some embodiments of the present invention, the method further comprises the following method steps:

[0053] S7 an evaluation of the user of the handheld power tool regarding the quality of the first routine executed in step S6 is obtained and the routine is optimized at least partially as a function of this evaluation.

[0054] The method according to the invention may also include executing method steps SMa, SMb and SMc in a control unit of the handheld power tool and / or on a central computer, in particular by transmitting a signal of the operating variable determined in step S1 and assigned to the exemplary application via an Internet connection.

[0055] A further embodiment of the invention comprises that the machine learning phase comprises executing or reading in at least two exemplary applications, preferably a plurality of exemplary applications, and also comprises ascertaining an average value of the consistency threshold values ​​from the consistency threshold values ​​associated with the two or more exemplary applications.

[0056] In this way, irregularities which may occur anywhere during the screwing process, such as variations in the carrier material, the screwing-in angle, the force applied by the user, etc., are statistically averaged and their influence in the learning process is thus reduced.

[0057] In some embodiments of the invention, the exemplary application is executed by a user of the handheld power tool and / or read in from a database. External and internal databases can be implemented. The use of an external database can, for example, include reading in screw characteristics via the Internet, while the use of an internal database can be the provision of the database on the handheld power tool at the factory.

[0058] Through different routines, one or more system functionalities can be provided to the user, with which an application situation can be concluded more simply and / or quickly. Through the machine learning phase provided according to the invention, the method can be highly adaptive and adapted to the needs of the user to a high degree.

[0059] In one embodiment, the first routine includes stopping the electric motor taking into account at least one defined and / or predeterminable parameter, in particular predeterminable by a user of the handheld power tool. Examples of such parameters include a time interval, a number of electric motor revolutions, a number of tool register revolutions, a rotation angle of the electric motor, and a number of impacts of the impact mechanism of the handheld power tool.

[0060] In another embodiment, the first routine includes a change, in particular a reduction and / or increase, of the electric motor speed. Such a change in the electric motor speed can be achieved, for example, by a change in the motor current, the motor voltage, the battery current or the battery voltage or by a combination of these measures.

[0061] Preferably, the amplitude of the change in the electric motor speed can be defined by the user of the handheld power tool. Alternatively or additionally, the change in the electric motor speed can also be predetermined by a target value. The term "amplitude" in this context should also generally be understood in the sense of the height of the change and is not only related to periodic processes.

[0062] In one embodiment, the speed of the electric motor is changed multiple times and / or dynamically, in particular in stages over time and / or along a characteristic curve of the speed change and / or depending on the progress of work of the hand-held power tool, wherein the change in speed is at least partially determined based on a learning process based on an exemplary application.

[0063] In principle, different operating variables can be considered as operating variables recorded by suitable measured value sensors. It is particularly advantageous that according to the invention no additional sensors are required in this respect, since various sensors, such as sensors for speed monitoring, preferably Hall sensors, are already installed in the electric motor.

[0064] Advantageously, the operating variable is the rotational speed of the electric motor or an operating variable that is related to the rotational speed. For example, a fixed transmission ratio from the electric motor to the impact mechanism results in a direct correlation between the motor rotational speed and the impact frequency. Another conceivable operating variable that is related to the rotational speed is the motor current. As operating variables of the electric motor, motor voltage, Hall signals of the motor, battery current or battery voltage may also be considered, wherein acceleration of the electric motor, acceleration of the tool register or acoustic signals of the impact mechanism of the handheld power tool may also be considered as operating variables.

[0065] In some embodiments, in method step S1, the signal of the operating parameter is recorded as a time variation curve of the measured value of the operating parameter, or as a measured value of the operating parameter as a parameter of the electric motor related to the time variation curve, such as the acceleration of the electric motor, especially higher-order impacts, power, energy, rotation angle, rotation angle or frequency of the tool recording part.

[0066] In the last-mentioned embodiment, it can be ensured that a constant periodicity of the signal to be investigated is produced independently of the motor speed.

[0067] If in method step S1 the signal of the operating variable is recorded as a time profile of a measured value of the operating variable, then in method step S1a following method step S1 the time profile of the measured value of the operating variable is converted into a profile of the measured value of the operating variable as a time-dependent variable of the electric motor based on the fixed transmission ratio of the transmission. This again results in the same advantages as when the signal of the operating variable is recorded directly over time.

[0068] Preferably, the work progress is output to the user of the handheld machine tool using an output device of the handheld machine tool. "Output by means of an output device" can be understood in particular as a display or document recording of the work progress. Here, the document recording can also be an analysis, evaluation and / or storage of the work progress. This also includes, for example, storing multiple screwing processes in a memory.

[0069] In one embodiment, the first routine and / or characteristic parameters of the first routine can be set and / or displayed by a user via application software (“App”) or a user interface (“human-machine interface”, “HMI”).

[0070] Furthermore, in one embodiment, the HMI may be disposed on the machine itself, while in other embodiments the HMI is disposed on an external device, such as a smartphone, tablet, computer.

[0071] In one embodiment of the invention, the first routine includes visual, auditory and / or tactile feedback to the user.

[0072] Preferably, the model signal shape is an oscillation curve, for example an oscillation curve around a mean value, in particular a substantially triangular oscillation curve. In this case, the model signal shape can represent, for example, an ideal impact operation of a hammer on an anvil of a rotary impact mechanism, wherein the ideal impact operation is preferably an impact without further rotation of the tool spindle of the handheld power tool.

[0073] In one specific embodiment of the invention, in method step S4 , the signals of the operating variables are compared by means of a comparison method with respect to whether at least one predefined threshold value for consistency is met.

[0074] Preferably, the comparison method comprises at least one frequency-based comparison method and / or a comparison method that performs a comparison.

[0075] In this case, a decision can be made at least partially with the aid of a frequency-based comparison method, in particular a bandpass filter and / or a frequency analysis, as to whether the work progress to be identified has been identified in the signal of the operating variable.

[0076] In one specific embodiment, the frequency-based comparison method comprises at least a bandpass filter and / or a frequency analysis, wherein the predefined threshold value is at least 90%, in particular 95%, and in particular exactly 98% of the predefined extreme value.

[0077] In the case of bandpass filtering, the recorded signal of the operating variable is filtered, for example, by a bandpass filter whose pass range corresponds to the model signal shape. The corresponding amplitude in the generated signal is expected when there is a work progress that is decisively to be identified. The predetermined threshold value of the bandpass filtering can therefore be at least 90%, in particular 95%, and in particular 98% of the corresponding amplitude in the work progress to be identified. The predetermined extreme value can here be the corresponding amplitude in the generated signal of the ideal work progress to be identified.

[0078] By means of known frequency-based comparison methods of frequency analysis, a previously determined model signal shape can be searched in the recorded signal of the operating variable, for example the frequency spectrum of the work progress to be identified. In the recorded signal of the operating variable, the corresponding amplitude of the work progress to be identified is expected. The predetermined threshold value of the frequency analysis can be at least 90%, in particular 95%, completely, in particular 98% of the corresponding amplitude in the work progress to be identified. The predetermined extreme value can be the corresponding amplitude in the recorded signal of the ideal work progress to be identified. In this case, it may be necessary to appropriately segment the recorded signal of the operating variable.

[0079] In one specific embodiment, the comparison method for the comparison comprises at least one parameter estimation and / or a cross correlation, wherein the predefined threshold value is at least 40% of the correspondence between the signal of the operating variable and the shape of the model signal.

[0080] The measured signal of the operating variable can be compared with the model signal shape by means of a comparison method. The measured signal of the operating variable is determined in such a way that the signal has a final signal length that is substantially the same as the final signal length of the model signal shape. The comparison of the model signal shape with the measured signal of the operating variable can be output as a particularly discrete or continuous signal of the final length. Depending on the degree of consistency or deviation of the comparison, the following result can be output: whether there is a work progress to be identified. If the measured signal of the operating variable is at least 40% consistent with the model signal shape, the work progress to be identified may exist. It is also conceivable that the comparison method can output the degree of mutual comparison as a result of the comparison by means of the comparison of the measured signal of the operating variable with the model signal shape. Here, a comparison of at least 60% with each other can be used as a criterion for the existence of the work progress to be identified. It can be assumed here that the lower limit of consistency is at 40%, while the upper limit of consistency is at 90%. Accordingly, the upper limit of the deviation is at 60%, while the lower limit of the deviation is at 10%.

[0081] In the parameter estimation, a comparison between a previously determined model signal shape and the signal of the operating variable can be realized in a simple manner. For this purpose, the estimated parameters of the model signal shape can be identified in order to adapt the model signal shape to the measured signal of the operating variable. By means of the comparison between the estimated parameters of the previously determined model signal shape and the extreme values, a result can be obtained regarding the existence of a work progress to be identified. Subsequently, the comparison result can be further evaluated: whether a predetermined threshold value has been reached. This evaluation can be a quality determination of the estimated parameters or can be a consistency between the determined model signal shape and the sensed signal of the operating variable.

[0082] In another embodiment, method step S4 includes a step S4a of quality determination of the identified model signal shape in the signal of the operating variable, wherein in method step S5 the work progress is detected at least partially based on the quality determination. The quality of the adaptation of the estimated parameters can be determined as a measure of the quality determination.

[0083] In method step S5 , a decision can be made at least partially with the aid of a quality determination, in particular a quality measure, as to whether a work progress to be identified has been recognized in the signal of the operating variable.

[0084] Additionally or alternatively to the quality determination, method step S4a may include the identification of the model signal shape and the comparative determination of the signal of the operating variable. The comparison of the estimated parameters of the model signal shape with the measured signal of the operating variable may be, for example, 70%, in particular 60%, or even 50%. In method step S5, a decision is made at least partially based on the comparative determination as to whether there is a work progress to be identified. The decision that there is a work progress to be identified may be made at a predetermined threshold of at least 40% agreement between the measured signal of the operating variable and the model signal shape.

[0085] In the case of a cross correlation, a comparison can be made between a previously determined model signal shape and the measured signal of the operating variable. In the case of a cross correlation, the previously determined model signal shape can be correlated with the measured signal of the operating variable. In the case of a correlation between the model signal shape and the measured signal of the operating variable, a measure of the consistency of the two signals can be determined. This measure of consistency can be, for example, 40%, in particular 50%, or even, in particular, 60%.

[0086] In method step S5 of the method according to the invention, the identification of the work progress can be carried out at least partially based on the cross-correlation of the model signal shape with the measured signal of the operating variable. The identification can be carried out at least partially based on a predetermined threshold value of at least 40% consistency between the measured signal of the operating variable and the model signal shape.

[0087] In one specific embodiment, the consistency threshold value is definable by a user of the handheld power tool and / or is predefined at the factory.

[0088] In another embodiment, the handheld power tool is an impact driver, in particular a rotary impact driver, and the work process is starting or ending an impact operation, in particular a rotary impact operation.

[0089] In one embodiment, one or more of the above steps SM, SMa, SMb, SMc are stored in the controller of the handheld machine tool as an operating mode of the handheld machine tool that can be called up by the user. In particular, steps SMa, SMb, ... include obtaining, storing and classifying exemplary applications, wherein in principle other reactions can also be called up by the user, such as speed control by reducing, increasing or shutting down. In parallel with this, the handheld machine tool can have an operating mode in which the consistency threshold can be selected by the user based on factory-defined pre-selections of the application situation of the handheld machine tool. This can occur, for example, via a user interface, such as an HMI (human-machine interface), such as a mobile device, in particular a smartphone and / or a tablet computer.

[0090] In particular, in method step S3a, the model signal shape can be variably determined, in particular by a user. In this case, the model signal shape is assigned to the work progress to be identified, so that the user can predetermine the work progress to be identified.

[0091] Advantageously, the model signal shape is defined, in particular factory-determined, in method step S3a. In principle, it is conceivable that the model signal shape is stored or stored within the device or alternatively and / or additionally provided to the handheld power tool, in particular by an external data device.

[0092] A person skilled in the art will recognize that the method according to the invention makes it possible to identify the progress of work independently of at least one desired speed of the electric motor, at least one starting characteristic of the electric motor and / or at least one charge state of a power source of the handheld power tool, in particular a battery.

[0093] The signal of the operating variable is to be understood here as a time series of measured values. Alternatively and / or additionally, the signal of the operating variable can also be a spectrum. Alternatively and / or additionally, the signal of the operating variable can also be processed again, for example, smoothed, filtered, adapted, etc.

[0094] In a further embodiment, the signal of the operating variable is stored as a sequence of measured values ​​in a memory, preferably a ring memory, in particular of the handheld power tool.

[0095] In one method step, the work progress to be identified is identified based on less than ten impacts of the impact mechanism of the handheld power tool, in particular less than ten impact vibration cycles of the electric motor, preferably less than six impacts of the impact mechanism of the handheld power tool, in particular less than six impact vibration cycles of the electric motor, and completely preferably less than four impacts of the impact mechanism, in particular less than four impact vibration cycles of the electric motor. Here, "impact of the impact mechanism" should be understood as an axial, radial, tangential and / or circumferential impact of the impact mechanism hammer, in particular the hammer, on the impact mechanism body, in particular the anvil. The impact vibration cycle of the electric motor is related to the operating parameters of the electric motor. The impact vibration cycle of the electric motor can be determined based on the operating parameter fluctuations in the signal of the operating parameter.

[0096] A further subject of the invention is a handheld power tool having an electric motor, a measured value recorder for operating variables of the electric motor and a control unit, wherein the handheld power tool is advantageously an impact screwdriver, in particular a rotary impact screwdriver, and is configured to carry out the above-described method.

[0097] The electric motor of the handheld power tool rotates the input spindle, and the output spindle is connected to the tool register. The anvil is connected to the output spindle in a rotationally fixed manner, and the hammer is connected to the input spindle in such a way that the hammer performs intermittent movements in the axial direction of the input spindle and intermittent rotational movements around the input spindle due to the rotational movement of the input spindle, wherein the hammer intermittently strikes the anvil in this way and thus outputs impact and rotation pulses to the anvil and thus to the output spindle. The first sensor transmits a first signal, for example, for determining the motor rotation angle, to the control unit. In addition, the second sensor transmits a second signal for determining the motor speed to the control unit.

[0098] Advantageously, the handheld power tool has a memory unit in which a plurality of values ​​can be stored.

[0099] In another embodiment, the handheld power tool is a battery-driven handheld power tool, in particular a battery-driven rotary impact screwdriver. In this way, flexible and power-independent use of the handheld power tool is ensured.

[0100] Advantageously, the hand-held power tool is an impact screwdriver, in particular a rotary impact screwdriver, and the work progress to be detected is: the screw head abutting against the fastening base, the free rotation of a loosened screw, the starting and ending of the rotary impact mechanism of the hand-held power tool, and / or the impact of the rotary impact mechanism without further rotation of the impacted element or tool recording part.

[0101] Identification of the impact of the impact mechanism of the handheld power tool, in particular the impact vibration cycle of the electric motor, can be achieved, for example, by using a fast fitting algorithm, with the aid of which the evaluation of the impact recognition can be achieved in less than 100 ms, in particular less than 60 ms, and in particular less than 40 ms. In this case, the method according to the invention enables the recognition of the progress of work for essentially all of the above-mentioned application cases and the recognition of the screwing of loose and fixed fastening elements in the fastening carrier.

[0102] The invention makes it possible to largely dispense with complex signal processing methods, such as filtering, signal loops, (static and adaptive) system models and signal tracking.

[0103] Furthermore, the method allows for a faster identification of impact movements or work progress, which can lead to a faster reaction of the tool. This applies in particular to a number of past impacts after the impact mechanism has been inserted until identification and also to special operating situations, such as the start-up phase of the drive motor. In this case, the functionality of the tool does not need to be restricted, for example by reducing the maximum drive speed. Furthermore, the operation of the algorithm is also independent of other influencing variables, such as the desired speed and the battery state.

[0104] In principle, additional sensors, such as acceleration sensors, are not necessary, but these analysis and evaluation methods can also be applied to signals of other sensor devices. In addition, in other motor schemes that are sufficient to cope with, for example, without speed sensing, the method can also be applied to other signals.

[0105] In a preferred embodiment, the handheld power tool is a battery screwdriver, a drill, a percussion drill or a hammer drill, wherein a drill, a drill bit or a different bit set can be used as a tool. The handheld power tool according to the invention is in particular designed as an impact screwdriver, wherein a higher peak torque for screwing in or out a screw or nut is generated by the pulsed release of motor energy. In this context, the transmission of electrical energy is to be understood in particular as: the handheld power tool transmits energy to the body via a battery and / or via a cable connection.

[0106] In addition, depending on the selected embodiment, the screwing tool can be configured flexibly along the direction of rotation. In this way, the proposed method can be used not only for screwing in a screw or nut but also for screwing it out.

[0107] In the context of the present invention, “ascertaining” is intended to include, in particular, measuring or recording, wherein “recording” is to be understood in the sense of measuring and storing, and “ascertaining” is also intended to include any signal processing of the measured signals, for example, ascertaining the signals by classification or clustering.

[0108] Furthermore, “determine” is also to be understood as recognition or detection, wherein a clear assignment is to be achieved. “Identify” is to be understood as recognition of a partial agreement with the sample, which agreement can be achieved, for example, by adapting the signal to the sample, Fourier analysis, etc. “Partial agreement” is to be understood such that the adaptation has an error of less than a predetermined threshold, in particular less than 30% of a predetermined threshold, and in particular less than 20% of a predetermined threshold.

[0109] Further features, application possibilities and advantages of the invention are apparent from the following description of the exemplary embodiments of the invention shown in the drawings. It should be noted that the features described or shown in the drawings alone or in any combination constitute the subject matter of the invention, independently of their summary in the claims or their references and independently of their expression or representation in the description or in the drawings, and are merely descriptive features and should not be considered as limiting the invention in any way. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] Below, the present invention is described in detail according to preferred embodiments. The accompanying drawings are schematic and show:

[0111] Figure 1 A schematic diagram of an electric handheld machine tool;

[0112] FIG. 2( a ) shows the working progress of an exemplary application and the signals assigned to the operating parameters;

[0113] FIG2(b) shows the consistency of the signal of the operating parameter shown in FIG2(a) with the model signal;

[0114] Figure 3 The working progress of the exemplary application and two associated signals of the operating variables;

[0115] Figure 4 Variation curves of signals of operating variables according to two embodiments of the present invention;

[0116] Figure 5 Variation curves of signals of operating variables according to two embodiments of the present invention;

[0117] Figure 6 The working progress of the exemplary application and two associated signals of the operating variables;

[0118] Figure 7 Variation curves of signals of two operating variables according to two embodiments of the present invention;

[0119] Figure 8 Variation curves of signals of two operating variables according to two embodiments of the present invention;

[0120] Fig. 9 is a schematic diagram of two different recordings of the signal of the operating parameter;

[0121] Fig. 10 (a) Signal of operating parameters;

[0122] FIG10( b ) is a function of the amplitude of a first frequency contained in the signal of FIG10( a );

[0123] FIG10( c ) is a function of the amplitude of a second frequency contained in the signal of FIG10( a );

[0124] FIG11 is a common view of the signal of the operating parameter and the output signal of the bandpass filter based on the model signal;

[0125] FIG12 is a common view of the signal of the operating parameter and the output of the frequency analysis based on the model signal;

[0126] FIG13 is a common view of the signal of the operating parameter and the model signal used for parameter estimation; and

[0127] FIG. 14 shows a common view of the signals of the operating parameters and the model signals used for cross-correlation. DETAILED DESCRIPTION

[0128] Figure 1 A handheld power tool 100 according to the invention is shown, which has a housing 105 with a handle 115. According to the embodiment shown, the handheld power tool 100 can be mechanically and electrically connected to a battery pack 190 for mains-independent power supply. Figure 1 In the embodiment, the handheld power tool 100 is exemplarily configured as a battery-operated rotary impact driver. However, it should be noted that the present invention is not limited to battery-operated rotary impact drivers, but can be applied to handheld power tools 100 that require work progress identification, such as impact drills.

[0129] An electric motor 180, which is supplied with current from a battery pack 190, and a transmission 170 are arranged in the housing 105. The electric motor 180 is connected to the input spindle via the transmission 170. In addition, a control unit 370 is arranged in the housing 105 in the region of the battery pack 190, which controls and / or regulates the electric motor 180 and the transmission 170, for example by means of a set motor speed n, a selected rotation pulse, a desired transmission gear x, etc.

[0130] The electric motor 180 can be actuated, i.e. can be switched on and off, for example, by means of a manual switch 195 and can be any motor type, for example an electronically commutated motor or a DC motor. In principle, the electric motor 180 can be electronically controlled or regulated in such a way that both reversible operation and presetting are possible with respect to the desired motor speed n and the desired rotation pulses. The function and structure of suitable electric motors are sufficiently known from the prior art that a detailed description is omitted here for the sake of simplicity of description.

[0131] The tool register 140 is rotatably supported by the input spindle and the output spindle in the housing 105. The tool register 140 is used to register the tool and can be directly formed onto the output spindle or connected to the output spindle in a plug-in manner.

[0132] The control unit 370 is connected to a power source and is designed in such a way that it can actuate the electric motor 180 in an electronically controllable or regulated manner by means of different current signals. The different current signals cause different rotation pulses of the electric motor 180, wherein the current signals are conducted to the electric motor 180 via control lines. The power source can be designed, for example, as a battery or, as in the illustrated embodiment, as a battery pack 190 or as a mains connection.

[0133] Furthermore, operating elements (not shown in detail) may be provided in order to set different operating modes and / or directions of rotation of electric motor 180 .

[0134] According to one aspect of the present invention, a method for operating a handheld power tool 100 is provided, by means of which the handheld power tool 100 can be operated, for example, in Figure 1 The handheld power tool 100 shown in FIG. 1 is used, for example, to determine the progress of a work during a screw-in operation or a screw-out operation.

[0135] As a result of determining the progress of the work, in one embodiment of the invention, a corresponding reaction or routine is triggered on the machine side. This allows a reproducible, high-quality screw-in and screw-out process to be reliably achieved. Aspects of the method are based in particular on the study of the signal shapes and the determination of the degree of consistency of these signal shapes, which can correspond, for example, to an evaluation of the continued rotation of an element driven by the handheld power tool 100, such as a screw.

[0136] 2 shows an exemplary signal of an operating variable 200 of an electric motor 180 of a rotary impact driver, such as a signal that occurs during conventional use of a rotary impact driver or a signal in a similar form. The following embodiments relate to a rotary impact driver, but within the scope of the invention they are also applicable to other handheld power tools 100, such as impact drills.

[0137] In the present example of FIG. 2 , time is plotted on the abscissa x as a reference variable. However, in an alternative embodiment, a time-dependent variable is used as a reference variable, such as the rotation angle of the tool recording unit 140, the rotation angle of the electric motor 180, acceleration, impact, in particular higher-order impact, power or energy. The motor speed n applied at each time point is plotted in the figure on the ordinate f(x). Instead of the motor speed, other operating variables related to the motor speed can also be selected. In an alternative embodiment of the invention, f(x) represents, for example, a signal of the motor current.

[0138] The motor speed and the motor current are operating variables which are generally sensed in the portable power tool 100 and without additional effort by the control unit 370. Within the scope of the present disclosure, "providing a signal of an operating variable 200 of the electric motor 180" is referred to as method step S1. In this context, "providing" is understood to mean making the corresponding features available in an internal or external memory of the portable power tool 100.

[0139] In a preferred embodiment of the present invention, a user of handheld power tool 100 can select the operating variables on the basis of which the method of the present invention is to be carried out.

[0140] FIG. 2( a ) shows the application of a loosened fastening element, such as a screw 900, to a fastening carrier 902, such as a wooden board. FIG. 2( a ) shows that the signal includes a first region 310, which is marked by a monotonically increasing motor speed and by a region in which the motor speed is relatively constant, which can also be referred to as a plateau. The intersection point between the abscissa x and the ordinate f(x) in FIG. 2( a ) corresponds to the start of the rotary impact driver during the screwing process.

[0141] In the first region 310, the screw 900 encounters relatively little resistance in the fastening carrier 902, and the torque required for screwing in is lower than the disengagement torque of the rotary impact mechanism. Therefore, the motor speed curve in the first region 310 corresponds to a screwing operation state without impact.

[0142] As can be seen from FIG. 2( a ), the head of the screw 900 does not rest against the fastening carrier 902 in the region 322 , which means that the screw 900 driven by the rotary impact driver continues to rotate with each impact. This additional rotation angle can decrease during the progressive operation, which is reflected in the figure by the decreasing cycle duration. In addition, the continued screwing in can also be indicated by the average reduced rotation speed.

[0143] If the head of screw 900 subsequently reaches base 902, a higher torque and thus greater impact energy are required for further screwing in. However, since handheld power tool 100 does not provide more impact energy, screw 900 does not rotate further or only rotates further by a significantly smaller rotation angle.

[0144] The rotary impact operation performed in the second region 322 and the third region 324 is marked by the vibration curve of the signal of the operating variable 200, wherein the vibration form can be, for example, a vibration in the form of a trigonometric function or another form. In the present case, the vibration has a curve that can be called a modified trigonometric function. The characteristic signal shape of the operating variable 200 in the impact screwing operation is generated by the impact mechanism hammer and the system chain between the impact mechanism and the electric motor 180, as well as the tensioning and release of the transmission 170.

[0145] In the case of the use of “different screwing situations each having a characteristic signal shape of an operating variable”, in step S2 of the method according to the invention, an application category is determined based on the signal of the operating variable 200. In the case of a screwing process, the term “application category” may include one or more aspects, such as screw parameters, screw type, screwing direction (screwing in or screwing out), screwing resistance, screwing speed, material of the screw base, and / or an applied handheld power tool operating mode implemented by the user.

[0146] As can be seen above, in addition, individual work progress, for example, the signal shape associated with the start of the impact operation, is also marked in principle by a certain characteristic feature, which is at least partially predetermined by the inherent characteristics of the rotary impact driver. In the method according to the invention, starting from this knowledge, in step S3, comparative information is provided at least partially as a function of the application category determined in step S2, wherein at least one model signal shape 240 is provided in step S3a. In this case, the model signal shape 240 can be associated with a work progress, for example, reaching the point where the head of the screw 900 rests on the fastening carrier 902; and in the context of some embodiments of the invention, the model signal shape 240 is also referred to as a state-specific model signal shape. In other words, the model signal shape 240 contains features that are typical for a work progress, such as the presence of a vibration curve, a vibration frequency or an amplitude, or individual signal sequences in continuous, quasi-continuous or discrete form.

[0147] In other applications, the work progress to be detected can be marked by a signal shape other than by vibration, for example by a discontinuity or growth rate in the function f(x). In such a case, instead of marking by vibration, the state-specific model signal shape is marked by these parameters.

[0148] In method step S3b, further comparison information is provided, namely a consistency threshold value, which will be described in more detail below.

[0149] In a preferred embodiment of the method according to the invention, in method step S3, the user can determine the state-specific model signal 240. The state-specific model signal 240 can also be stored or stored within the device or provided by an external data source.

[0150] In method step S4 of the method according to the invention, the signal of operating variable 200 of electric motor 180 is compared with state-specific model signal 240. The feature "comparison" is to be interpreted broadly in the context of the invention and in the sense of signal analysis, so that the result of the comparison can also be a partial or gradual agreement between the signal of operating variable 200 of electric motor 180 and model signal 240, wherein the degree of agreement between the two signals can be determined by various mathematical methods, which will be mentioned later.

[0151] In step S4, a consistency evaluation of the signal of operating variable 200 of electric motor 180 and state-specific model signal 240 is also determined from this comparison, and conclusions about the consistency of the two signals are thus obtained. In this case, the consistency evaluation is carried out at least partially based on the above-mentioned consistency threshold value, which can therefore also be understood as a minimum limit for the consistency of the signal of operating variable 200 and model signal shape 240 and will be explained in more detail below.

[0152] Figure 2(b) shows a curve of the function q(x) of the consistency evaluation 201 corresponding to the signal of the operating parameter 200 of Figure 2(a), which indicates the value of the consistency between the signal of the operating parameter 200 of the electric motor 180 and the state-specific model signal 240 at each position of the abscissa x.

[0153] In the present example of screwing in a screw 900, this evaluation is taken into account in order to determine the extent of further rotation during an impact. The model signal shape 240 provided in step S3a corresponds in this example to an ideal impact without further rotation, i.e. a state in which the head of the screw 900 rests on the surface of the fastening carrier 902, as shown in region 324 of FIG. 2 (a). Accordingly, a high degree of consistency of the two signals is obtained in region 324, which is reflected by a constant high value of the function q(x) of the consistency evaluation 201. On the other hand, in region 310, each impact is accompanied by a large rotation angle of the screw 900, in which only small consistency values ​​are achieved. The less the screw 900 continues to rotate during an impact, the higher the consistency. It can be seen here that the function q(x) of the consistency evaluation 201 already reflects a continuously increasing consistency value in region 322 when using an impact mechanism, which is characterized by the rotation angle of the screw 200 continuously decreasing with each impact due to the increased screw-in resistance.

[0154] As can be seen in the example of FIG. 2 , the consistency evaluation 201 of the signal is well suited for impact differentiation due to its more or less abrupt behavior, wherein the abrupt change is determined by an equally more or less abrupt change in the further rotation angle of the screw 900 at the end of the exemplary working process. According to the invention, the identification of the progress of the work is at least partially realized based on a comparison of the consistency evaluation 201 with a consistency threshold provided in step S3b, which is represented by the dashed line 202 in FIG. 2 (b). In the present example of FIG. 2 (b), the intersection point SP of the function q(x) of the consistency evaluation 201 with the line 202 is assigned to the progress of the work in which the head of the screw 900 rests on the surface of the fastening carrier 902.

[0155] In method step S5 of the method according to the invention, the progress of the work is now identified at least partially based on the consistency evaluation 201 determined in method step S4. It should be noted that this function is not limited to screw-in applications but also includes use in screw-out applications.

[0156] According to the invention, the provision of the comparison information in step S3 can be implemented at least partially based on a machine learning phase. In an embodiment of the invention, the machine learning phase includes executing or reading in at least two or more exemplary applications of the handheld power tool 100, wherein at least one exemplary application includes reaching a certain working progress of the handheld power tool 100, for example, reaching a state of "the head of the screw 900 abuts against the surface of the fastening carrier 902", as shown in the area 324 of FIG. 2 (a). Therefore, the term "determined working progress" should not be understood here as: the working progress must be determined by the user compulsorily. On the contrary, in an advantageous embodiment of the invention, it is provided that the handheld power tool automatically recognizes the certain working progress according to the exemplary application when using the data analysis method, for example, recognizes that when a certain curve of the model signal shape 200' (for example corresponding to the head of the screw 900 abutting against the fastening carrier 902) is reached, the speed of the handheld power tool 100 is reduced or switched off.

[0157] Therefore, in this embodiment, the method according to the invention comprises a step SM of performing a machine learning phase based on at least two or more exemplary applications, wherein the exemplary applications comprise reaching a certain work progress. In this embodiment, the ascertainment of the application class in step S2 and the provision of the model signal shape 240 and / or the consistency threshold in step S3 are performed at least partially based on the application class generated in the machine learning phase and the model signal shape 240' and / or the consistency threshold assigned to the application class.

[0158] The handheld power tool thus learns autonomously or partially autonomously at what point in time a response to the profile of the consistency evaluation is desired in different applications, without requiring corresponding user instructions. In certain specific embodiments of the invention, method step SM advantageously comprises storing and classifying the signals of the operating variables 200' which are assigned to the exemplary applications into at least one or more application categories.

[0159] For this purpose, a specific embodiment of the method according to the invention can comprise one or more of the following method steps.

[0160] SMa determines, stores and classifies a model signal shape 240 assigned to the exemplary application at least partially as a function of a corresponding signal of the operating variable 200 ′ when a specific point in time of the work progress is reached.

[0161] SMb determines, stores and classifies a consistency threshold value associated with the exemplary application at least partially as a function of a corresponding signal of operating variable 200 ′ when a specific point in time of the work progress is reached.

[0162] SMc determines and stores a consistency threshold value assigned to the application class based on the stored model signal shape 240 ′ and the consistency threshold value assigned to the exemplary application.

[0163] Here, steps SMa, SMb and SMc include various data analysis methods known per se, such as averaging of exploratory statistics or more advanced operations, which generally provide more accurate results the larger the set of exemplary applications. In this context, it should be mentioned that method steps SMa, SMb and SMc can be optionally implemented in a control unit of the handheld power tool 100 and / or on a central computer, in particular by sending a signal of the operating variable 200 ′ assigned to the exemplary application determined in step S1 via an Internet connection. In this case, it is possible that the determination steps of the method according to the invention, such as the above-mentioned data analysis for determining a specific work progress or consistency threshold, may not be executed by the handheld power tool 100, but by a central computer node, and that the basic set of exemplary applications is maximized by combining exemplary applications recorded and stored by different users.

[0164] On the contrary, such a process makes it possible that the exemplary applications do not necessarily have to be executed by the user of the handheld power tool 100. Instead, these exemplary applications can also be read directly from a database. In this case, the database can be an external database, for example a database connected via the Internet, or an internal database, for example in the form of a database provided by the factory on the handheld power tool itself. In conjunction with the present invention, the exemplary applications read from the external database or the data characterizing these exemplary applications are also referred to as "screwing characteristics (Schraubprofil)".

[0165] The basis for these embodiments is therefore to enrich the “experience base” of the handheld power tool 100 with exemplary applications which are either executed by the handheld power tool 100 itself or are transmitted to the handheld power tool 100 in the form of data records.

[0166] In brief, in a first variant, the user performs, for example, a large number of screw-in processes, wherein the handheld power tool 100 autonomously and cumulatively performs data analysis for the purpose of classifying or arranging these applications, and for determining the model signal shape 200' and the consistency threshold value when the user performs recurring routines, such as stopping the handheld power tool 100 or reducing the rotational speed. It should be noted that these recurring routines themselves can also be used for the classification and arrangement of these applications. In subsequent applications that are also evaluated according to this method, the handheld power tool 100 automatically determines the application category, provides comparison information assigned to the application category, i.e., at least the model signal shape 240 and the consistency threshold value, and executes the routine executed by the user in the corresponding application category, such as reducing the rotational speed of the electric motor 180, when the current existing signal of the operating variable 200 and the model signal 240 are evaluated accordingly for consistency, which will be described in more detail later.

[0167] Thus, according to the invention, by differentiating or comparing signal shapes, the progress of the work of an element operated by a rotary impact driver can be evaluated and a routine following the progress of the work can be initiated, wherein the determined signal shape used here, i.e., the model signal shape 240, and a part of the evaluation criterion for the consistency of the compared signal shapes, i.e., the consistency threshold, are at least partially provided by the machine learning stage.

[0168] In one embodiment of the invention, it is provided that the machine learning phase in step SM includes learning the handheld machine tool by images in the sense of "deep learning". In this case, a suitable image capture device and / or existing images of the application change process are used. In this case, the image capture device can include an image sensor or a camera, by which the exemplary application is optically sensed, analyzed by a known image processing tool and then classified. In the same way, the application category can also be determined in step S2 by means of optical sensing by the image capture device and subsequent image analysis. In this case, it is conceivable to use an internal image sensor or camera, i.e. integrated into the handheld machine tool, or an external image sensor or camera, such as a camera of a smartphone, as the image capture device.

[0169] Advantageously, the learning of the determination of the work progress in the sense of the above-described embodiment is supplemented by a further method step S6, in which a first routine of the handheld power tool 100 is executed at least partially based on the work progress identified in method step S5, as explained below. It is assumed here that the result of the work progress to be identified is that the handheld power tool executes the aforementioned first routine in method step S6, which work progress to be identified has been defined by the parameter model signal shape 240 and / or the consistency threshold value by the machine learning phase as described above. However, an alternative embodiment also provides that, in the case of an unknown application situation, the first routine is estimated with the help of known application situations with similar characteristics.

[0170] Even if the speed is reduced when the operating state changes to the impact mode, it is difficult to prevent the screw head from penetrating into the material, for example in the case of small wood screws or self-tapping screws. This is due to the high spindle speeds that occur even when the torque is increased due to the impact of the impact mechanism.

[0171] This behavior in Figure 3 As shown in FIG. 2 , for example, time is plotted on the abscissa x, the motor speed is plotted on the ordinate f(x), and the torque g(x) is plotted on the ordinate g(x). The curves f and g thus illustrate the variation of the motor speed f and the torque g over time. Figure 3 2 , different states during the screwing process of the wood screws 900 , 900 ′ and 900 “into the fastening carrier 902 are schematically shown.

[0172] In the operating state "non-impact" indicated by reference numeral 310 in the drawing, the screw rotates at a high speed f and a low torque g. In the operating state "impact" indicated by reference numeral 320, the torque g increases rapidly, while the speed f decreases only slightly, as has already been found above. Figure 3 The region 310 ′ in FIG. 2 marks the region in which the impact detection explained in conjunction with FIG. 2 takes place.

[0173] In order to, for example, prevent the screw head of screw 900 from penetrating into the fastening carrier 902, according to the present invention, in method step S6 a tool-related, application-appropriate routine or reaction is implemented at least partially based on the work progress identified in method step S5, such as shutting down the machine, changing the rotational speed of the electric motor 180, and / or providing visual, auditory and / or tactile feedback to the user of the handheld machine tool 100.

[0174] In one specific embodiment of the present invention, the first routine includes stopping electric motor 180 taking into account at least one defined and / or predefinable parameter, in particular predefinable by a user of the handheld power tool.

[0175] For this purpose, for example Figure 4 Schematically shown in FIG. 3 is the stopping of the tool immediately after the impact detection 310 ′, thereby assisting the user in this case to prevent the screw head from penetrating into the fastening carrier 902 . In the figure, this is illustrated by the branch f′ of the curve f that drops rapidly after the region 310 ′.

[0176] Examples of defined and / or predefinable parameters, in particular predefinable by a user of the handheld power tool 100, include a time defined by the user, after which the tool stops, which is Figure 4 The time interval T stopp The corresponding branch f" of the curve f is shown. In the ideal case, the handheld power tool 100 stops just in such a way that the screw head is flush with the screw contact surface. However, since the time until this situation occurs varies from one application to another, it is advantageous if the time interval T stopp Can be defined by the user.

[0177] Alternatively or additionally thereto, in one embodiment of the invention it is provided that the first routine includes a change in the rotational speed, in particular the desired rotational speed, of the electric motor 180 , in particular a reduction and / or increase and thus also a change in the spindle speed after impact detection. Figure 5 , an embodiment of the speed reduction is shown in . First, the handheld power tool 100 is operated again in the "non-impact" operating state 310, which is represented by the motor speed curve represented by the curve f. After the impact recognition in the area 310', the motor speed is reduced by a certain amplitude in the example, which is represented by the curve f' or f".

[0178] In one embodiment of the present invention, the magnitude or level of the speed change of the electric motor 180 is Figure 5 The branch f" of the curve f passes through Δ D The user can set the speed by the motor. By reducing the speed, the user has more time to react if the screw head approaches the surface of the fastening carrier 902. As soon as the user sees that the screw head is sufficiently flush with the support surface, the user can stop the handheld power tool 100 with the switch. Compared with stopping the handheld power tool 100 after impact recognition, the change in motor speed, Figure 5 The example of a reduction has the advantage that, due to the user-defined switch-off, the routine is largely independent of the application case.

[0179] In one embodiment of the present invention, the amplitude Δ of the speed change of the electric motor 180 Dand / or a target value for the rotational speed of electric motor 180 can be defined by a user of handheld power tool 100 , which further increases the flexibility of the routine in terms of applicability for various application cases.

[0180] In an embodiment of the present invention, the speed of the electric motor 180 is changed multiple times and / or dynamically. In particular, it can be provided that the speed of the electric motor 180 is changed in time steps and / or along a characteristic curve of the speed change and / or according to the working progress of the portable power tool 100.

[0181] Examples for this also include a combination of a speed reduction and a speed increase. In addition, different routines or combinations thereof can be executed in a time-staggered manner relative to the impact recognition. In addition, the present invention also includes an embodiment in which a time offset is provided between two or more routines. If, for example, the motor speed is reduced directly after the impact recognition, the motor speed can also be increased again after a certain time value. In addition, it is provided that the characteristic curve not only predetermines the different routines themselves, but also predetermines the embodiment of the time offset between these routines.

[0182] As mentioned at the outset, the present invention includes embodiments in which the progress of the work is represented by a change from the operating state “impact” in the region 320 to the operating state “non-impact” in the region 310. Figure 6 Shown in.

[0183] Such a transition of the operating state of the handheld power tool 100 is provided, for example, in the following work process: the screw 900 is separated from the fastening carrier 902, that is, in the process of unscrewing, which Figure 6 This is schematically shown in the lower region of Figure 3 Like in Figure 6 In FIG. 1 , curve f represents the rotation speed of the electric motor 180 , and curve g represents the torque.

[0184] As already explained in conjunction with other specific embodiments of the present invention, here too, an operating state of the handheld power tool, in the present case an operating state of the impact mechanism, is detected by means of detecting the characteristic signal shape.

[0185] In the "Shock" operating state, i.e. Figure 6 In region 320, screw 900 does not rotate and exerts a large torque g. In other words, the spindle speed is equal to zero in this case. Figure 6In region 310, the torque g drops rapidly, which in turn causes an equally rapid increase in the spindle and motor speed f. Due to this rapid increase in the motor speed f, which is caused by the drop in torque g from the time when the screw 900 is loosened from the fastening carrier 902, it is usually difficult for the user to record the loosened screw 900 or nut and prevent it from falling off.

[0186] The method according to the invention can be used to prevent a threaded element, which can be a screw 900 or a nut, from being unscrewed so quickly after being released from the fastening carrier 902 that it falls off. Figure 7 . Figure 7 The axes and curves shown correspond essentially to Figure 6 , and corresponding reference numerals indicate corresponding features.

[0187] In one embodiment, the routine includes in step S6 that the handheld power tool 100 immediately stops after it has been determined that the handheld power tool 100 has recognized the work progress to be recognized, in this example the “non-impact” operating mode. Figure 7 In the example, the motor speed is shown by the steeply decreasing branch f' of the curve f in the region 310. In an alternative embodiment, the time T stopp The time after which the appliance stops can be defined by the user. In the figure, this is shown by the branch f" of the curve f of the motor speed. A person skilled in the art knows that the motor speed is also Figure 6 As shown in FIG. , after the transition from region 320 (operating state “impact”) to region 310 (operating state “non-impact”), the velocity increases rapidly at first and at a time interval T stopp After the expiration, it drops steeply.

[0188] By appropriately selecting the time interval T stopp In the case of , it can be achieved that the motor speed just drops to "zero" so that the screw 900 or nut is just still in the thread. In this case, the user can remove the screw 900 or nut with a small amount of thread rotation, or alternatively keep it in the thread, so as to open the clamp, for example.

[0189] In the following Figure 8 Another embodiment of the invention is described. In this case, the motor speed is reduced after the transition from region 320 (operating state "impact") to region 310 (operating state "non-impact"). The magnitude or level of the reduction is indicated in the figure by Δ D The reduction is indicated as a measure between the average value f" of the motor speed in the region 320 and the reduced motor speed f'. The reduction can be set by the user in certain embodiments, in particular by specifying a target value for the speed of the handheld power tool 100, which is Figure 8is located at the level of branch f'.

[0190] By reducing the motor speed and thus the spindle speed, the user has more time to react if the head of the screw 900 comes loose from the screw bearing surface. Once the user thinks that the screw head or nut has been screwed sufficiently, the user can stop the handheld power tool 100 by means of a switch.

[0191] Combined with Figure 7 Compared to the embodiment described above, in which the handheld power tool 100 is stopped with a delay directly or after the transition from the region 320 (operating state "impact") to the region 310 (operating state "non-impact"), the speed reduction has the advantage that it is largely independent of the application case, because the user ultimately determines when the handheld power tool is to be switched off after the speed reduction. This can be helpful, for example, in the case of long threaded rods. There are application cases in which, after the threaded rod is loosened and the impact mechanism is extended therewith, a more or less long unscrewing process must still be carried out. Therefore, switching off the handheld power tool 100 after the impact mechanism is extended in these cases is not suitable.

[0192] It should be mentioned that in some embodiments of the present invention, it is provided that the parameters used in method step S6 of the first routine described above, such as the change curve and amplitude of the speed reduction or increase, can be defined by a machine learning phase based on exemplary applications and / or screwing characteristics.

[0193] Furthermore, in a further method step S7 , a quality evaluation of the user of the handheld power tool 100 regarding the first routine executed in step S6 is determined, by which the routine is optimized at least partially as a function of this evaluation.

[0194] In some embodiments of the present invention, the progress of the work is output to a user of the handheld power tool using an output device of the handheld power tool.

[0195] In the following, some technical connections and implementation methods related to the implementation of method steps S1 - S5 are explained.

[0196] In practical applications, it can be provided that one or more of the method steps S1 to S4 are repeatedly executed during operation of the handheld power tool 100 in order to monitor the progress of the work of the executed application. For this purpose, in method step S1 the determined signal of the operating variable 200 can be segmented so that method steps S2 and S4 are executed on signal segments of a certain length, which are preferably always the same.

[0197] For this purpose, the signal of the operating variable 200 can be stored as a sequence of measured values ​​in a memory, preferably a ring memory. In this embodiment, the handheld power tool 100 comprises a memory, preferably a ring memory.

[0198] As already mentioned in conjunction with FIG. 2 , in a preferred embodiment of the invention, in method step S1, a signal of operating variable 200 is determined as a time profile of a measured value of the operating variable, or as a measured value of an operating variable, which is a variable of electric motor 180 that is correlated with the time profile. The measured value can be discrete, quasi-continuous or continuous.

[0199] One embodiment provides that in method step S1, the signal of the operating parameter 200 is recorded as a time variation curve of the measured value of the operating parameter, and in method step S1a following method step S1, the time variation curve of the measured value of the operating parameter is converted into a variation curve of the measured value of the operating parameter as a parameter of the electric motor 180 related to the time variation curve, such as the rotation angle of the tool recording unit 140, the motor rotation angle, acceleration, in particular higher-order impact, power or energy.

[0200] The advantages of this embodiment are described below with reference to FIG. 9. Similar to FIG. 2, Figure 9a A signal f(x) of an operating variable 200 is shown over an abscissa x, in this case over time t. As in FIG2 , the operating variable may be the motor speed or a variable that is correlated with the motor speed.

[0201] The diagram includes two signal curves of an operating variable 200, which can each be assigned to a work process, for example, a rotary impact screwing mode in the case of a rotary impact screwdriver. In both cases, the signal includes the wavelength of an ideally assumed sinusoidal vibration curve, wherein the signal with a shorter wavelength T1 has a curve with a higher impact frequency, and the signal with a longer wavelength T2 has a curve with a lower impact frequency.

[0202] Both signals can be generated with the same handheld power tool 100 at different motor speeds and are also dependent on which rotational speed the user requests via an operating switch of the handheld power tool 100 .

[0203] If, for example, the parameter “wavelength” is now to be considered for defining the state-specific model signal shape 240, then in the present case at least two different wavelengths T1 and T2 must be stored as possible parts of the state-specific model signal shape so that the comparison of the signal of the operating variable 200 with the state-specific model signal shape 240 leads to “identical” results in both cases. Since the motor speed is usually variable over time and over a wide range, this has the consequence that the wavelength to be sought also changes and therefore the method for identifying the impulse frequency must be adapted accordingly.

[0204] With a large number of possible wavelengths, the costs of the method and programming increase rapidly accordingly.

[0205] In a preferred embodiment, the time values ​​of the abscissa are therefore transformed into values ​​that are correlated with the time values, such as acceleration values, higher-order impact values, power values, energy values, frequency values, rotation angle values ​​of the tool recording unit 140 or rotation angle values ​​of the electric motor 180. This is possible because the fixed transmission ratio of the electric motor 180 to the impact mechanism and to the tool recording unit 140 results in a known direct relationship between the motor speed and the impact frequency. This normalization results in a vibration signal of a constant period that is independent of the motor speed, which is Figure 9b , represented by the transformation of the two signals belonging to T1 and T2, wherein the two signals now have the same wavelength P1=P2.

[0206] Accordingly, in this embodiment of the invention, a state-specific model signal shape 240 valid for all rotational speeds can be determined by a unique parameter of the wavelength regarding a time-dependent variable, such as the rotation angle of the tool recording unit 140, the motor rotation angle, acceleration, and in particular higher-order impacts, power or energy.

[0207] In a preferred embodiment, the comparison of the signal of the operating parameter 200 is implemented in method step S4 by means of a comparison method, wherein the comparison method includes at least one frequency-based comparison method and / or a comparison method for comparison. The comparison method compares the signal of the operating parameter 200 with the state-specific model signal shape 240: whether at least a consistency threshold is met. The comparison method compares the measured signal of the operating parameter 200 with the consistency threshold. The frequency-based comparison method includes at least bandpass filtering and / or frequency analysis. The comparison method for comparison includes at least parameter estimation and / or cross correlation. The frequency-based and comparison-based comparison method is described in more detail below.

[0208] In an embodiment with bandpass filtering, the input signal, which is converted into a time-dependent variable as described, is filtered through one or more bandpass filters, the pass range of which corresponds to one or more state-specific model signal shapes. The pass range is derived from the state-specific model signal shape 240. It is also conceivable that the pass range corresponds to a frequency determined in conjunction with the state-specific model signal shape 240. In the case where the amplitude of this frequency exceeds the previously determined extreme value, such as is the case when the work progress to be identified is reached, the comparison in method step S4 leads to the following result: the signal of the operating variable 200 is equal to the state-specific model signal shape 240, and the work progress to be identified is therefore reached. The determination of the amplitude extreme value in this embodiment can be understood as the determination of the consistency evaluation of the state-specific model signal shape 240 and the signal of the operating variable 200, based on which it is decided in method step S5 whether there is a work progress to be identified.

[0209] According to FIG. 10 , an embodiment is described in which a frequency analysis is used as a frequency-based comparison method. In this case, a signal of the operating variable 200 shown in FIG. 10 (a) and corresponding, for example, to the time curve of the rotational speed of the electric motor 180 is transformed from the time domain into the frequency domain with corresponding frequency weighting based on a frequency analysis, for example, a fast Fourier transform (FFT). In this case, according to the above embodiment, the term "time domain" is to be understood not only as "the time curve of the operating variable", but also as "the curve of the operating variable as a time-dependent variable".

[0210] Frequency analysis in this form is well known as a mathematical tool for signal analysis from many technical fields and is also used to approximate the measured signal as a series expansion to a weighted periodic harmonic function of different wavelengths. Figure 10(b) and 10(c) For example, the weighting coefficients κ1(x) and κ2(x) as time function curves 203 and 204 indicate whether and to what extent corresponding frequencies or frequency bands are present in the signal under investigation, i.e. in the curve of the operating variable 200, which are not given here for reasons of clarity.

[0211] With regard to the method according to the invention, it is thus possible to determine by means of frequency analysis whether and with what magnitude a frequency associated with the state-specific model signal shape 240 is present in the signal of the operating variable 200. However, it is also possible to define a frequency whose absence is a measure for the presence of a work progress to be identified. As explained in conjunction with the bandpass filtering, it is possible to determine an extreme value of the amplitude, which is a measure of the degree of correspondence of the signal of the operating variable 200 to the state-specific model signal shape 240.

[0212] In the example of FIG. 10( b ), at time t2 (point SP2), the amplitude κ1(x) of a first frequency, which is typically not found in the state-specific model signal shape 240, falls below the corresponding extreme value 203(a) in the signal of the operating variable 200, which in the example is a necessary, but not sufficient criterion for the presence of the work progress to be identified. At time t3 (point SP3), the amplitude κ2(x) of a second frequency, which is typically found in the state-specific model signal shape 240, exceeds the associated extreme value 204(a) in the signal of the operating variable 200. In a corresponding embodiment of the invention, the presence of the amplitude function κ1(x) or κ2(x) falling below or exceeding the extreme value 203(a), 204(a) is a decisive criterion for the evaluation of the consistency of the signal of the operating variable 200 with the state-specific model signal shape 240. Accordingly, in this case, it is determined in method step S5 that the work progress to be identified has been reached.

[0213] In alternative embodiments of the present invention, only one of these criteria is used, or also one of both criteria or a combination of both criteria with other criteria, for example reaching a set rotational speed of electric motor 180 .

[0214] In an embodiment of the comparison method using a comparison, the signal of operating variable 200 is compared with state-specific model signal shape 240 in order to find out whether the measured signal of operating variable 200 has at least 50% consistency with state-specific model signal shape 240 and thus reaches a predefined threshold value. It is also conceivable to compare the signal of operating variable 200 with state-specific model signal shape 240 in order to determine the consistency of the two signals with each other.

[0215] In the following embodiment of the method according to the invention, in which parameter estimation is used as a comparison method for comparison, the measured signal of the operating variable 200 is compared with the state-specific model signal shape 240, wherein the estimated parameters are identified for the state-specific model signal shape 240. With the aid of the estimated parameters, a consistency measure of the measured signal of the operating variable 200 and the state-specific model signal shape 240 can be determined: whether the work progress to be identified has been achieved. The parameter estimation is based on curve fitting (Ausgleichsrechnung), which is a mathematical optimization method known to those skilled in the art. This mathematical optimization method can achieve the adaptation of the state-specific model signal shape 240 to a series of measurement data of the signal of the operating variable 200 with the aid of the estimated parameters. Based on the consistency measure of the state-specific model signal shape 240 parameterized with the aid of the estimated parameters and the extreme value, it can be determined whether the work progress to be identified has been achieved.

[0216] Curve fitting using the comparative method of parameter estimation can also determine a measure of consistency between the estimated parameters of state-specific model signal shape 240 and the measured signal of operating variable 200 .

[0217] In one embodiment of the method according to the invention, a cross-correlation method is used as a comparison method in method step S4. As in the above-mentioned mathematical method, the cross-correlation method is known per se to a person skilled in the art. In the cross-correlation method, a state-specific model signal shape 240 is correlated with a measured signal of an operating variable 200.

[0218] Compared to the method of parameter estimation proposed above, the result of the cross-correlation is again a signal sequence with a summed signal length consisting of the lengths of the signals of the operating variable 200 and the state-specific model signal shape 240, which represents the similarity of the time-shifted input signals. Here, the maximum value of the output sequence represents the time point of the highest consistency of the two signals, namely the operating variable 200 and the state-specific model signal shape 240, and is thus also a measure for the correlation itself, which in this embodiment is used in method step S5 as a decision criterion for reaching the work progress to be identified. In the execution of the method according to the invention, the main difference from parameter estimation is that for the cross-correlation, any state-specific model signal shape can be used, while in parameter estimation, the state-specific model signal shape 240 must be able to be represented by a parameterizable mathematical function.

[0219] 11 shows a measured signal for an operating variable 200 for the case where a bandpass filter is used as a frequency-based comparison method. In this case, time or a time-dependent variable is plotted as the abscissa x. Fig.11a The measured signal of the operating variable is shown as a bandpass filtered input signal, wherein in a first range 310 the handheld power tool 100 is operated in a screw-driving mode. In a second range 320 the handheld power tool 100 is operated in a rotary impact mode. Fig.11b Represents the output signal after the bandpass filter has filtered the input signal.

[0220] FIG. 12 shows a measured signal of an operating variable 200 for the case where a frequency analysis is used as a frequency-based comparison method. Fig.12a A and b show a first region 310, in which the handheld power tool 100 is in a screwing operation. Fig.12a Plot time or time-related parameters on the horizontal axis x. Figure 12b The transformed signal of the operating variable 200 is shown in , wherein, for example, a transformation from the time domain into the frequency domain can be performed by means of a fast Fourier transformation. Figure 12bFor example, the frequency f is plotted on the abscissa x' of , thereby showing the amplitude of the signal of the operating variable 200. Fig.12c d show a second region 320 , in which the hand-held power tool 100 is in rotary impact operation. Fig.12c The signal showing the measurement of operating variable 200 over time in rotary impact operation is shown. Fig.12d The transformed signal of operating variable 200 is shown, wherein the signal of operating variable 200 is plotted over frequency f as abscissa x′. Fig.12d Typical amplitudes for rotary impact operation are shown.

[0221] Fig.13a A typical case of a comparison between the signal of the operating variable 200 and the state-specific model signal shape 240 is shown in the first region 310 described in FIG. 2 , using a comparison method for comparison by means of parameter estimation. The state-specific model signal shape 240 has a substantially trigonometric curve, while the signal of the operating variable 200 has a curve that is very different therefrom. Independently of the selection of one of the above-described comparison methods, in this case the comparison between the state-specific model signal shape 240 and the signal of the operating variable 200 carried out in method step S4 results in the following result: the degree of agreement between the two signals is so low that the work progress to be identified is not identified in method step S5 .

[0222] On the contrary, in Fig.13b 2 shows a situation in which there is a work progress to be identified and therefore, even if deviations can be determined at individual measuring points, the state-specific model signal shape 240 has a high degree of consistency overall with the signal of the operating variable 200. Thus, in the comparison method for comparing the parameter estimates, it can be decided whether the work progress to be identified has been achieved.

[0223] FIG. 14 shows a state-specific model signal shape 240 (see Fig.14b and 14e) and the measured signal of the operating variable 200 (see Fig.14a and 14d), for the case where cross correlation is used as the comparison method for the comparison. Fig.14a -f, plots time or time-related parameters on the horizontal axis x. Fig.14a -c shows the first region 310 corresponding to the screwing operation. Fig.14d -f shows a third area 324 corresponding to the work progress to be identified. As described above, the measured signal of the operating parameter ( Fig.14a and 14d) with state-specific model signal shapes ( Fig.14b Related to 14e). Fig.14c The respective results of the correlation are shown in 14f and 14f. Fig.14cThe result of the correlation during the first region 310 is shown in , where it can be seen that there is a small coincidence of the two signals. Fig.14c In the example of , it is therefore decided in method step S5 that the work progress to be identified has not been reached. Fig.14f The results of the correlation during the third region 324 are shown in FIG. Fig.14f As can be seen in FIG. 1 , there is a high degree of consistency, so that in method step S5 it is decided that the work progress to be identified has been reached.

[0224] The invention is not limited to the exemplary embodiments described and shown. The invention also encompasses all special developments within the scope of the invention defined by the claims.

[0225] In addition to the embodiments described and illustrated, further embodiments are conceivable which may include other modifications and combinations of features.

Claims

1. A method for operating a handheld power tool (100), the handheld power tool (100) comprising an electric motor (180), the method comprising the following method steps: S1 obtaining a signal (200) of an operating parameter of the electric motor (180); S2 determining the application category at least partially based on the signal (200) of the operating variable; S3 provides comparison information at least in part based on the application category, comprising the following steps: S3a provides at least one model signal shape (240), wherein the model signal shape (240) can be assigned to a certain work progress of the handheld power tool (100), wherein the model signal shape (240) contains features that are typical for a work progress; S3b provides a consistency threshold, wherein the consistency threshold is a minimum limit of consistency between the signal (200) of the operating parameter and the model signal shape (240); S4: comparing the signal (200) of the operating variable with the model signal shape (240), and determining a consistency evaluation from the comparison, wherein the consistency evaluation is performed at least in part based on the consistency threshold, wherein the identification of the work progress is performed at least in part based on the comparison of the consistency evaluation with the consistency threshold provided in step S3b; S5 identifies the work progress at least partially based on the consistency evaluation determined in method step S4 , wherein, depending on the degree of consistency or the degree of deviation of the comparison, a result is output as to whether there is a work progress to be identified.

2. The method according to claim 1, comprising the following steps: The SM performs a machine learning phase according to at least two or more exemplary applications, wherein: The exemplary application includes reaching the determined work progress; Wherein, determining the application category in step S2 and providing the model signal shape (240) and / or the consistency threshold in step S3 are performed at least partially based on the application category generated in the machine learning stage and the model signal shape (240') and / or the consistency threshold assigned to the application category.

3. The method according to claim 2, wherein the method step SM further comprises: The signals (200') of the operating variables which are assigned to the exemplary application are stored and classified in at least one or more application categories.

4. The method according to any one of claims 2 or 3, wherein the method step SM further comprises the following method steps: SMa determines, stores and classifies a model signal shape (240') assigned to the exemplary application at least partially based on the signal (200') of the operating variable assigned to the exemplary application when the determined work progress point in time is reached.

5. The method according to any one of claims 2 or 3, wherein the method step SM further comprises the following method steps: SMb determines, stores and classifies a consistency threshold value assigned to the exemplary application at least partially based on the signal (200') of the operating variable assigned to the exemplary application when the time point of the determined work progress is reached.

6. The method according to any one of claims 2 or 3, wherein the method step SM further comprises the following method steps: SMc determines and stores the consistency threshold value assigned to the application category based on the stored model signal shape (240') and the consistency threshold value assigned to the exemplary application.

7. The method according to any one of claims 1 to 3, comprising the following method steps: S6 executes a first routine of the handheld power tool (100) at least partially based on the work progress detected in method step S5.

8. The method according to claim 7, comprising the following method steps: S7: obtaining an evaluation of the user of the handheld power tool (100) regarding the quality of the first routine executed in step S6; and optimizing the routine at least partially based on the evaluation.

9. The method according to claim 6, comprising: Method steps SMa, SMb and SMc are carried out in a control unit of the handheld power tool (100) and / or on a central computer.

10. The method according to any one of claims 2 or 3, wherein: The exemplary application is executed by a user of the handheld power tool (100) and / or is read in from a database.

11. The method according to claim 7, characterized in that The first routine includes stopping the electric motor (180) within defined and / or predefinable parameters.

12. The method according to claim 11, characterized in that The first routine includes a variation in the rotational speed of the electric motor (180).

13. The method according to claim 12, characterized in that The speed of the electric motor (180) is varied multiple times and / or dynamically, wherein the speed variation is determined at least partially based on a learning process based on an exemplary application.

14. The method according to any one of claims 1 to 3, characterized in that The operating variable is the rotational speed of the electric motor (180) or an operating variable that is related to the rotational speed.

15. The method according to any one of claims 1 to 3, characterized in that In method step S1, the signal (200) of the operating variable is recorded as a time profile of a measured value of the operating variable or as a measured value of the operating variable as a variable of the electric motor (180) that is dependent on the time profile.

16. The method according to any one of claims 1 to 3, characterized in that In method step S1, the signal (200) of the operating variable is recorded as a time variation curve of the measured value of the operating variable, and, in method step S1a following this method step, the time variation curve of the measured value of the operating variable is converted into a variation curve of the measured value of the operating variable as a variable of the electric motor (180) related to the time variation curve.

17. The method according to any one of claims 1 to 3, characterized in that The handheld power tool (100) is an impact driver, and the first operating state is an impact operation.

18. The method according to claim 9, characterized in that By sending a signal ( 200 ′) of the operating variables determined in step S1 and assigned to the exemplary application via an Internet connection, method steps SMa, SMb and SMc are executed on the central computer.

19. The method according to claim 11, characterized in that The predefinable parameters are parameters that can be predefinable by a user of the handheld power tool (100).

20. The method according to claim 12, characterized in that The change in the rotation speed of the electric motor (180) is a decrease and / or an increase in the rotation speed of the electric motor (180).

21. The method according to claim 13, characterized in that The speed of the electric motor (180) is varied in stages over time and / or along a characteristic curve of the speed variation and / or as a function of the progress of work of the handheld power tool (100).

22. The method according to claim 17, characterized in that The impact driver is a rotary impact driver.

23. The method according to claim 17, characterized in that The impact operation is a rotational impact operation.

24. A handheld power tool (100) comprising an electric motor (180), a measured value recorder for operating variables of the electric motor (180), and a control unit (370), characterized in that: The control unit (370) is configured to carry out the method according to any one of claims 1 to 23.

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