Method and device for predicting machining depth of inner hole of part and machine tool equipment

Abstract

The embodiment of the invention provides a method and device for predicting the machining depth of an inner hole of a part and machine tool equipment, and belongs to the field of machine tool machining in industrial production. The part inner hole machining is carried out on a machine tool, and the prediction method comprises the steps that vibration signal data and machining parameters of a machine tool spindle are collected; determining a vibration signal curve of the machine tool spindle according to the vibration signal data of the machine tool spindle; according to the vibration signal curve and the machining parameters of the machine tool spindle, the inner hole machining depth of the to-be-measured part is determined. The embodiment of the invention can automatically analyze the machining depth of the inner hole of the part and reduce the measurement error, thereby improving the production efficiency.

Description

Technical Field

[0001] The invention relates to the field of machine tool processing in industrial production, and in particular to a method and device for predicting the processing depth of a part inner hole and a machine tool equipment. Background Art

[0002] In the field of industrial production, machine tools are often used to manufacture and repair the inner holes of parts, such as boring, drilling, reaming, and reaming to further process the inner holes. During the inner hole processing of parts, the inner hole depth of the parts needs to be obtained, and the inner hole depth of the parts needs to be measured manually, such as using a micrometer, vernier caliper, or steel ruler.

[0003] The above scheme has the following defects: the depth of the inner hole of the part is measured manually, there are measurement and reading errors, and the processing time is increased. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a method and device for predicting the machining depth of a part inner hole and a machine tool equipment. The embodiments of the present invention can solve or at least partially solve the above-mentioned defects of the prior art.

[0005] In order to achieve the above-mentioned object, a first aspect of an embodiment of the present invention provides a method for predicting the machining depth of a part inner hole, wherein the inner hole machining of the part is performed on a machine tool, and the prediction method comprises:

[0006] Collect vibration signal data and processing parameters of machine tool spindle;

[0007] Determine the vibration signal curve of the machine tool spindle according to the vibration signal data of the machine tool spindle;

[0008] Using the following method 1 or method 2, according to the vibration signal curve of the machine tool spindle and the processing parameters, determine the inner hole processing depth of the part to be tested;

[0009] Method 1: The processing parameters include feed speed. According to the vibration signal curve of the machine tool spindle and the processing parameters, the inner hole processing depth of the part to be tested is determined, including:

[0010] Determining the processing time according to the vibration signal curve of the machine tool spindle;

[0011] Determine the inner hole processing depth of the part to be tested according to the processing time and feed speed;

[0012] Method 2: The processing parameters include feed speed, cutting fluid temperature and tool life. According to the vibration signal curve of the machine tool spindle and the processing parameters, the inner hole processing depth of the part to be tested is determined, including:

[0013] Pre-set the inner hole processing depth of the part;

[0014] Determine the standard processing time according to the preset part inner hole processing depth and feed speed;

[0015] Determine the integral of the power, acceleration and speed of the machine tool spindle to the standard processing time according to the vibration signal curve of the machine tool spindle and the standard processing time;

[0016] The integral of the power, acceleration and speed of the machine tool spindle over the standard processing time, the cutting fluid temperature and the tool life are brought into the prediction model to determine the processing depth of the inner hole of the part to be tested.

[0017] Optionally, the vibration signal data of the machine tool spindle includes power, acceleration and speed of the machine tool spindle;

[0018] The vibration signal curve of the machine tool spindle includes a power curve, an acceleration curve, and a speed curve of the machine tool spindle;

[0019] The vibration signal curve has time as the horizontal coordinate and the corresponding variable effective value as the vertical coordinate.

[0020] Optionally, in the first mode, determining the processing time according to the vibration signal curve of the machine tool spindle includes:

[0021] Selecting a target curve, wherein the target vibration curve is any one of a machine tool spindle power curve, an acceleration curve, and a speed curve;

[0022] Set a first threshold and a second threshold; the first threshold is the ordinate threshold corresponding to the target curve when processing starts, and the second threshold is the ordinate threshold corresponding to the target curve when processing ends;

[0023] When the vertical coordinate value of the target curve exceeds the first threshold, the corresponding time is the start processing time; when the vertical coordinate value of the target curve is lower than the second threshold and the corresponding time is greater than the start time, the corresponding time is the end processing time;

[0024] The actual processing time is determined according to the processing start time and the processing end time.

[0025] Optionally, in the second method, the prediction model is obtained by:

[0026] Establish training data based on the vibration signal curves and processing parameters of the machine tool spindle corresponding to different processing depths;

[0027] The training data is standardized and denoised, and a prediction model is obtained by training the training data using a machine learning algorithm.

[0028] A second aspect of an embodiment of the present invention provides a device for predicting the machining depth of a part inner hole, the device comprising:

[0029] Data acquisition module: used to collect vibration signal data and processing parameters of the machine tool spindle;

[0030] Data processing module: used to determine the vibration signal curve of the machine tool spindle according to the vibration signal data of the machine tool spindle;

[0031] Using the following method 1 or method 2, according to the vibration signal curve of the machine tool spindle and the processing parameters, determine the inner hole processing depth of the part to be tested;

[0032] Method 1: The processing parameters include feed speed. According to the vibration signal curve of the machine tool spindle and the processing parameters, the inner hole processing depth of the part to be tested is determined, including:

[0033] Determining the processing time according to the vibration signal curve of the machine tool spindle;

[0034] Determine the inner hole processing depth of the part to be tested according to the processing time and feed speed;

[0035] Method 2: The processing parameters include feed speed, cutting fluid temperature and tool life. According to the vibration signal curve of the machine tool spindle and the processing parameters, the inner hole processing depth of the part to be tested is determined, including:

[0036] Pre-set the inner hole processing depth of the part;

[0037] Determine the standard processing time according to the preset part inner hole processing depth and feed speed;

[0038] Determine the integral of the power, acceleration and speed of the machine tool spindle to the standard processing time according to the vibration signal curve of the machine tool spindle and the standard processing time;

[0039] The power, acceleration and speed of the machine tool spindle are integrated with the standard processing time, and the processing parameters are brought into the prediction model to determine the processing depth of the inner hole of the part to be tested;

[0040] The data output module is used to output the inner hole processing depth of the part to be tested.

[0041] Optionally, the vibration signal data of the machine tool spindle includes power, acceleration and speed of the machine tool spindle;

[0042] The vibration signal curve of the machine tool spindle includes a power curve, an acceleration curve, and a speed curve of the machine tool spindle;

[0043] The vibration signal curve has time as the horizontal coordinate and the corresponding variable effective value as the vertical coordinate.

[0044] Optionally, in the first mode, determining the processing time according to the vibration signal curve of the machine tool spindle includes:

[0045] Selecting a target curve, wherein the target vibration curve is any one of a machine tool spindle power curve, an acceleration curve, and a speed curve;

[0046] Set a first threshold and a second threshold; the first threshold is the ordinate threshold corresponding to the target curve when starting processing, and the second threshold is the ordinate threshold corresponding to the target curve when ending processing;

[0047] When the vertical coordinate value of the target curve exceeds the first threshold, the corresponding time is the start processing time; when the vertical coordinate value of the target curve is lower than the second threshold and the corresponding time is greater than the start time, the corresponding time is the end processing time;

[0048] The actual processing time is determined according to the processing start time and processing end time.

[0049] Optionally, in the second method, the prediction model is obtained by:

[0050] Establish training data based on the vibration signal curves and processing parameters of the machine tool spindle corresponding to different processing depths;

[0051] The training data is standardized and denoised, and a prediction model is obtained by training the training data using a machine learning algorithm.

[0052] A third aspect of an embodiment of the present invention provides a machine tool device, which includes a machine tool, a controller and a device for predicting the inner hole machining depth of a part: the controller controls the operation of the machine tool according to the inner hole machining depth of the part to be measured determined by the prediction device.

[0053] A fourth aspect of an embodiment of the present invention provides a machine-readable storage medium storing machine instructions. When the machine instructions are executed on a machine, the machine executes the method for predicting the machining depth of an inner hole of a part.

[0054] Through the above technical scheme, the purpose of the embodiment of the present invention is to provide a method and device for predicting the inner hole processing depth of a part and a machine tool equipment, which can automatically analyze the inner hole processing depth of the part, reduce measurement errors, and thus improve production efficiency.

[0055] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0057] Figure 1 is a flow chart of the prediction method provided by the present invention;

[0058] Figure 2 It is a flow chart of a prediction method implemented in a usage mode 1 provided by the present invention;

[0059] Figure 3 is a flow chart of a second method for implementing a prediction method provided by the present invention;

[0060] Figure 4 is a structural diagram of the prediction device provided by the present invention;

[0061] Figure 5 It is a structural diagram of the machine tool equipment provided by the present invention.

[0062] Description of Reference Numerals

[0063] 1. Data acquisition module

[0064] 2. Machine tool spindle hardware

[0065] 3. Processor DETAILED DESCRIPTION

[0066] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0067] Figure 1 is a flow chart of the prediction method provided by the present invention; Figure 2 It is a flow chart of a prediction method implemented in a usage mode 1 provided by the present invention; Figure 3 It is a flow chart of the prediction method implemented by the second usage mode provided by the present invention.

[0068] like Figure 1 As shown, the present invention provides a method for predicting the inner hole machining depth of a part, the method comprising:

[0069] S101, collecting vibration signal data and processing parameters of the machine tool spindle;

[0070] In industrial production, inner hole processing of parts is carried out on machine tools. The vibration signal data of the machine tool spindle includes: the power, acceleration and speed of the machine tool spindle. Sensors can be used for data collection. The sensor used to collect the acceleration and speed of the machine tool spindle is preferably a three-directional vibration sensor.

[0071] S102, determining a vibration signal curve of the machine tool spindle according to the vibration signal data of the machine tool spindle;

[0072] Furthermore, the collected vibration signal data can be filtered to eliminate interference signals and noise, improve signal quality, and more accurately reflect the vibration of the machine tool during the actual processing of the inner hole of the part.

[0073] The vibration signal curve of the machine tool spindle includes the machine tool spindle power curve, acceleration curve, and speed curve. The vibration signal curve takes time as the horizontal axis and the corresponding variable effective value as the vertical axis.

[0074] S103, determining the inner hole machining depth of the part to be tested according to the vibration signal curve of the machine tool spindle and the machining parameters.

[0075] The following method 1 or method 2 can be used to determine the inner hole machining depth of the part to be tested according to the vibration signal curve of the machine tool spindle and the machining parameters. Figure 2 The first method is described in detail. Figure 3 The second method is described in detail.

[0076] like Figure 2 As shown, the first method determines the inner hole machining depth of the part to be tested according to the vibration signal curve of the machine tool spindle and the machining parameters, including:

[0077] S201, the processing parameters include feed speed;

[0078] It should be noted that the feed rate reflects the rate at which the workpiece is removed by cutting material and is one of the key factors affecting the machining depth of the inner hole of the part.

[0079] S202, determining the processing time according to the vibration signal curve of the machine tool spindle;

[0080] Further, the drilling process is taken as an example for specific explanation;

[0081] First, the target curve selects the acceleration curve of the machine tool spindle under the specified vibration frequency. It should be noted that the target curve can select any one of the machine tool spindle power curve, acceleration curve, and speed curve, and the specified vibration frequency needs to be determined by testing the tool debugging.

[0082] Then, set the first threshold to a1mm / s 2 , the second threshold is a2mm / s 2 When the tool starts to contact the part and the processing is completed, the effective value of acceleration will change significantly, and the corresponding vibration curve will fluctuate greatly. When the effective value of acceleration exceeds a1, the corresponding time t1 is determined to be the start processing time. When the acceleration is lower than a2, the corresponding reagent t2 is determined to be the end processing time, and t2 is greater than t1.

[0083] Finally, the actual processing time is determined to be t2-t1;

[0084] It should be noted that a1 and a2 can be determined through advance debugging, and can be preferably set to 0.8 mm / s based on empirical values. 2 .

[0085] The actual processing time is determined according to the processing start time and the processing end time.

[0086] S203, determining the inner hole machining depth of the part to be tested according to the machining time and the feed speed.

[0087] Furthermore, the following formula can be used to calculate the machining depth of the inner hole of the part: d = v*t, where v is the feed speed, t is the machining time, and d is the machining depth;

[0088] like Figure 3 As shown, the second method determines the inner hole machining depth of the part to be tested according to the vibration signal curve of the machine tool spindle and the machining parameters, including:

[0089] S301, the processing parameters include feed speed, cutting fluid temperature and tool life;

[0090] S302, presetting the inner hole processing depth of the part;

[0091] S303, determining the processing standard time according to the preset part inner hole processing depth and feed speed;

[0092] Furthermore, the standard processing time can be calculated using the following formula: t=d / v, where v is the feed speed, t is the standard processing time, and d is the preset processing depth.

[0093] It should be noted that the standard processing time includes part of the feeding time after the tool contacts the workpiece and leaves the workpiece.

[0094] S304, determining the integral of the power, acceleration, and speed of the machine tool spindle with respect to the standard processing time according to the vibration signal curve of the machine tool spindle and the standard processing time;

[0095] Furthermore, the integration of the machine tool spindle acceleration over the processing standard time includes the integration of the spindle vibration acceleration in the x, y, and z directions over the processing standard time; the integration of the machine tool spindle speed over the processing standard time includes the integration of the effective value of the spindle vibration speed in the x, y, and z directions over the processing standard time;

[0096] S305, the integral of the power, acceleration and speed of the machine tool spindle over the standard processing time, the cutting fluid temperature and the tool life are brought into the prediction model to determine the processing depth of the inner hole of the part to be tested.

[0097] For example, the following multiple linear regression model can be used as a prediction model;

[0098] d=w0+w1x1+w2x2+w3x3+w4x4+w5x5+w6x6+w7x7

[0099] +w8x8+w9x9

[0100] Among them, w1~w9 are regression coefficients, and w0 is the regression constant;

[0101] x1: the integral of the spindle vibration acceleration in the x direction over the standard processing time;

[0102] x2: the integral of the spindle vibration acceleration in the y direction over the standard processing time;

[0103] x3: the integral of the spindle vibration acceleration in z direction over the standard processing time;

[0104] x4: the integral of the effective value of the spindle vibration speed in the x direction over the standard processing time;

[0105] x5: the integral of the effective value of the spindle vibration velocity in the y direction over the standard processing time;

[0106] x6: The integral of the effective value of the spindle vibration speed in the z direction and the standard processing time;

[0107] x7: integral of spindle power and standard machining time;

[0108] x8: Cutting fluid temperature during processing;

[0109] x9: Tool life during machining.

[0110] Furthermore, the regression coefficient is obtained in the following manner: establishing training data according to the vibration signal curve of the machine tool spindle and the processing parameters corresponding to different processing depths; standardizing and denoising the training data, and using a machine learning algorithm to train the training data to determine the regression coefficient.

[0111] like Figure 4 As shown, the structural diagram of the prediction device provided by the present invention includes:

[0112] Data acquisition module, used to collect vibration signal data and processing parameters of the machine tool spindle;

[0113] Furthermore, sensors may be used for data collection; the sensor for collecting the acceleration and speed of the machine tool spindle is preferably a three-directional vibration sensor.

[0114] The data processing module is used to determine the vibration signal curve of the machine tool spindle according to the vibration signal data of the machine tool spindle; and determine the inner hole processing depth of the part to be tested according to the vibration signal curve of the machine tool spindle and the processing parameters by using the following method 1 or method 2;

[0115] Method 1: The processing parameters include feed speed. According to the vibration signal curve of the machine tool spindle and the processing parameters, the inner hole processing depth of the part to be tested is determined, including:

[0116] Determining the processing time according to the vibration signal curve of the machine tool spindle;

[0117] Determine the inner hole processing depth of the part to be tested according to the processing time and feed speed;

[0118] Method 2: The processing parameters include feed speed, cutting fluid temperature and tool life. According to the vibration signal curve of the machine tool spindle and the processing parameters, the inner hole processing depth of the part to be tested is determined, including:

[0119] Pre-set the inner hole processing depth of the part;

[0120] Determine the standard processing time according to the preset part inner hole processing depth and feed speed;

[0121] Determine the integral of the power, acceleration and speed of the machine tool spindle to the standard processing time according to the vibration signal curve of the machine tool spindle and the standard processing time;

[0122] The power, acceleration and speed of the machine tool spindle are integrated with the standard processing time, and the processing parameters are brought into the prediction model to determine the processing depth of the inner hole of the part to be tested;

[0123] The implementation process of method 1 and method 2 are as follows: Figure 2 , Figure 3 As shown, no further description is given here.

[0124] The data output module is used to output the inner hole processing depth of the part to be tested, which is convenient for real-time monitoring and control.

[0125] like Figure 5 As shown, the structural diagram of the machine tool equipment provided by the present invention includes: a processor 3, a data acquisition module 1, and a machine tool spindle hardware 2.

[0126] It should be noted that the data acquisition module can be implemented using a sensor; the processor can be a microcomputer, and the controller, the data processing module of the prediction device, and the data output module are integrated inside the processor; wherein the controller is used to control the operation of the machine tool according to the inner hole processing depth of the part to be tested determined by the prediction device. The machine tool equipment provided by the embodiment of the present invention can be integrated or deployed separately, and is easy to implement and integrate into the existing machine tool system.

[0127] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0128] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0129] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0131] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0132] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0133] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0134] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0135] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for predicting the depth of inner hole machining of a part, wherein the inner hole machining of the part is performed on a machine tool, characterized in that: The prediction method comprises: Collect vibration signal data and processing parameters of machine tool spindle; Determine the vibration signal curve of the machine tool spindle according to the vibration signal data of the machine tool spindle; Using the following method 1 or method 2, according to the vibration signal curve of the machine tool spindle and the processing parameters, determine the inner hole processing depth of the part to be tested; Method 1: The processing parameters include feed speed. According to the vibration signal curve of the machine tool spindle and the processing parameters, the inner hole processing depth of the part to be tested is determined, including: Determining the processing time according to the vibration signal curve of the machine tool spindle; Determine the inner hole processing depth of the part to be tested according to the processing time and feed speed; Method 2: The processing parameters include feed speed, cutting fluid temperature and tool life. According to the vibration signal curve of the machine tool spindle and the processing parameters, the inner hole processing depth of the part to be tested is determined, including: Pre-set the inner hole processing depth of the part; Determine the standard processing time according to the preset part inner hole processing depth and feed speed; Determine the integral of the power, acceleration and speed of the machine tool spindle to the standard processing time according to the vibration signal curve of the machine tool spindle and the standard processing time; The integral of the power, acceleration and speed of the machine tool spindle over the standard processing time, the cutting fluid temperature and the tool life are brought into the prediction model to determine the processing depth of the inner hole of the part to be tested.

2. The method according to claim 1, characterized in that The vibration signal data of the machine tool spindle includes the power, acceleration and speed of the machine tool spindle; The vibration signal curve of the machine tool spindle includes a power curve, an acceleration curve, and a speed curve of the machine tool spindle; The vibration signal curve has time as the horizontal coordinate and the corresponding variable effective value as the vertical coordinate.

3. The method according to claim 2, characterized in that In the first mode, determining the processing time according to the vibration signal curve of the machine tool spindle includes: Selecting a target curve, wherein the target vibration curve is any one of a machine tool spindle power curve, an acceleration curve, and a speed curve; Set a first threshold and a second threshold; the first threshold is the ordinate threshold corresponding to the target curve when processing starts, and the second threshold is the ordinate threshold corresponding to the target curve when processing ends; When the vertical coordinate value of the target curve exceeds the first threshold, the corresponding time is the start processing time; when the vertical coordinate value of the target curve is lower than the second threshold and the corresponding time is greater than the start time, the corresponding time is the end processing time; The actual processing time is determined according to the processing start time and the processing end time.

4. The method according to claim 1, characterized in that In the second method, the prediction model is obtained by: Establish training data based on the vibration signal curves and processing parameters of the machine tool spindle corresponding to different processing depths; The training data is standardized and denoised, and a prediction model is obtained by training the training data using a machine learning algorithm.

5. A device for predicting the machining depth of a part inner hole, characterized in that: The device includes: Data acquisition module: used to collect vibration signal data and processing parameters of the machine tool spindle; Data processing module: used to determine the vibration signal curve of the machine tool spindle according to the vibration signal data of the machine tool spindle; Using the following method 1 or method 2, according to the vibration signal curve of the machine tool spindle and the processing parameters, determine the inner hole processing depth of the part to be tested; Method 1: The processing parameters include feed speed. According to the vibration signal curve of the machine tool spindle and the processing parameters, the inner hole processing depth of the part to be tested is determined, including: Determining the processing time according to the vibration signal curve of the machine tool spindle; Determine the inner hole processing depth of the part to be tested according to the processing time and feed speed; Method 2: The processing parameters include feed speed, cutting fluid temperature and tool life. According to the vibration signal curve of the machine tool spindle and the processing parameters, the inner hole processing depth of the part to be tested is determined, including: Pre-set the inner hole processing depth of the part; Determine the standard processing time according to the preset part inner hole processing depth and feed speed; Determine the integral of the power, acceleration and speed of the machine tool spindle to the standard processing time according to the vibration signal curve of the machine tool spindle and the standard processing time; The power, acceleration and speed of the machine tool spindle are integrated with the standard processing time, and the processing parameters are brought into the prediction model to determine the processing depth of the inner hole of the part to be tested; The data output module is used to output the inner hole processing depth of the part to be tested.

6. The device according to claim 5, characterized in that The vibration signal data of the machine tool spindle includes the power, acceleration and speed of the machine tool spindle; The vibration signal curve of the machine tool spindle includes a power curve, an acceleration curve, and a speed curve of the machine tool spindle; The vibration signal curve has time as the horizontal coordinate and the corresponding variable effective value as the vertical coordinate.

7. The device according to claim 6, characterized in that In the first mode, determining the processing time according to the vibration signal curve of the machine tool spindle includes: Selecting a target curve, wherein the target vibration curve is any one of a machine tool spindle power curve, an acceleration curve, and a speed curve; Set a first threshold and a second threshold; the first threshold is the ordinate threshold corresponding to the target curve when starting processing, and the second threshold is the ordinate threshold corresponding to the target curve when ending processing; When the vertical coordinate value of the target curve exceeds the first threshold, the corresponding time is the start processing time; when the vertical coordinate value of the target curve is lower than the second threshold and the corresponding time is greater than the start time, the corresponding time is the end processing time; The actual processing time is determined according to the processing start time and processing end time.

8. The device according to claim 5, characterized in that In the second method, the prediction model is obtained by: Establish training data based on the vibration signal curves and processing parameters of the machine tool spindle corresponding to different processing depths; The training data is standardized and denoised, and a machine learning algorithm is used to train the training data to obtain a prediction model.

9. A machine tool device, characterized in that: The machine tool equipment comprises a machine tool, a controller and a prediction device for the inner hole machining depth of a part according to any one of claims 5 to 8: The controller controls the operation of the machine tool according to the inner hole machining depth of the part to be measured determined by the prediction device.

10. A machine-readable storage medium, characterized in that: Machine instructions are stored, and when the machine instructions are run on the machine, the machine executes the method for predicting the inner hole machining depth of a part as claimed in any one of claims 1 to 4.

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