A numerical control tool life automatic prediction system and a prediction method thereof

Abstract

The application relates to the field of aviation manufacturing automation processing, and discloses a numerical control tool life automatic prediction system and a prediction method thereof.According to full-life processing time length data of a numerical control tool in a use process, processing time length, cutting depth, cutting width, cutting speed and historical data of accumulated tool wear before corresponding secondary processing in each processing process of the numerical control tool, a prediction model of a residual processing life value of the numerical control tool after each processing is completed is constructed, the prediction model considers historical accumulated wear and related process parameters in the current processing process, residual safe processing time of the numerical control tool can be accurately predicted, and reliable support is provided for efficient and high-precision automatic processing of aviation engine parts.

Description

Technical Field

[0001] This invention relates to the field of automated machining in aerospace manufacturing, and discloses an automated prediction system and method for CNC tool life. Background Technology

[0002] Aero engines are hailed as the "crown jewel" of modern industry. Aero components are highly valuable and have long development cycles. Currently, the wear of CNC cutting tools in the industry is mostly measured manually. The remaining safe service life of CNC cutting tools, that is, the service life of CNC cutting tools, is mostly judged manually after measurement or the service life is specified. This is inefficient and the judgment error of CNC cutting tool processing time is large. It is easy to cause CNC cutting tools to break during processing or be judged as scrap tools too early, which affects the processing efficiency of aero engine parts or causes problems such as high CNC cutting tool usage costs. Summary of the Invention

[0003] The purpose of this invention is to provide an automated prediction system and method for CNC tool life, which can accurately predict the remaining safe machining time of CNC tools, providing reliable support for efficient and high-precision automated machining of aero-engine parts.

[0004] To achieve the above-mentioned technical effects, the technical solution adopted by the present invention is as follows:

[0005] A method for predicting the life of CNC tools, comprising:

[0006] Obtain the full life machining time data of CNC tools of the same model and material reaching the maximum allowable wear value, as well as the machining time, depth of cut, width of cut, cutting speed and historical data of cumulative tool wear before each machining process;

[0007] Based on the total machining time data of CNC tools and the machining time of each machining process, the remaining machining life value of CNC tools after each machining is obtained by analysis;

[0008] Using the depth of cut, width of cut, cutting speed and cumulative tool wear before each machining process as independent variables and the remaining machining life of the CNC tool after each machining process as the dependent variable, a prediction model for the remaining machining life of the CNC tool after each machining process is constructed.

[0009] Based on the depth of cut, width of cut, cutting speed of the current machining or machining process, and the cumulative tool wear before the current machining or machining process, the remaining cutting life of the tool after the completion of the current machining or machining process is obtained by using the prediction model.

[0010] Furthermore, the prediction model for the remaining machining life of the CNC tool after each machining operation is as follows: Where T i V represents the remaining machining life of the CNC tool after the i-th machining process is completed. B Q represents the maximum allowable wear value of the cutting tool. i A represents the cumulative tool wear before the i-th machining process. i H represents the depth of cut data for the i-th machining process. i V represents the cutting width data for the i-th processing step. i Let be the cutting speed of the i-th machining process, where a is a coefficient, and m, n, p, and q are exponents. a, m, n, p, and q are obtained through data fitting.

[0011] Furthermore, the cumulative tool wear before each machining operation is taken as the maximum value between the tool diameter wear and the tool length wear.

[0012] Furthermore, the cumulative wear of the cutting tool is obtained through machine vision measurement.

[0013] To achieve the above-mentioned technical effects, the present invention also provides an automated prediction system for CNC tool life, comprising:

[0014] The data acquisition module is used to acquire the full life processing time data of CNC tools of the same model and material reaching the maximum allowable wear value, as well as the processing time, depth of cut, width of cut, cutting speed and historical data of cumulative tool wear before each processing of CNC tools;

[0015] The data analysis module is used to analyze and obtain the remaining machining life value of the CNC tool after each machining process based on the CNC tool's total machining life data and the machining time of each machining process.

[0016] The model building module is used to construct a prediction model for the remaining machining life of the CNC tool after each machining process, with the depth of cut, width of cut, cutting speed and cumulative tool wear before the corresponding machining process as independent variables and the remaining machining life of the CNC tool after the corresponding machining as the dependent variable.

[0017] The prediction module is used to analyze and obtain the remaining cutting life of the tool after the current machining or machining process is completed, based on the depth of cut, width of cut, cutting speed of the current machining or machining process and the cumulative tool wear before the current machining or machining process.

[0018] Furthermore, the prediction model constructed in the model building module is: Where T i V represents the remaining machining life of the CNC tool after the i-th machining process is completed. B Q represents the maximum allowable wear value of the cutting tool.i A represents the cumulative tool wear before the i-th machining process. i H represents the depth of cut data for the i-th machining process. i V represents the cutting width data for the i-th processing step. i Let be the cutting speed of the i-th machining process, α be a coefficient, and m, n, p, q be exponents. α, m, n, p, q are obtained through data fitting.

[0019] Furthermore, in the model building module, the cumulative tool wear before each machining operation is taken as the maximum value between the tool diameter wear and the tool length wear.

[0020] Furthermore, in the data acquisition module or the prediction module, the cumulative wear of the cutting tool is obtained through machine vision measurement.

[0021] Furthermore, it also includes a display module, which displays the remaining cutting life time of the corresponding CNC tool based on the prediction results in the prediction module.

[0022] Compared with the prior art, the beneficial effects of this invention are as follows: Based on the full life-cycle machining time data of CNC tools, as well as the historical data of machining time, depth of cut, width of cut, cutting speed, and cumulative tool wear before each machining process, this invention constructs a predictive model for the remaining machining life of CNC tools after each machining operation. This predictive model takes into account the historical cumulative wear and relevant process parameters in the current machining process, and can accurately predict the remaining safe machining time of CNC tools, providing reliable support for efficient and high-precision automated machining of aero-engine parts. Attached image description:

[0023] Figure 1 This is a flowchart of the CNC tool life prediction method in the embodiment;

[0024] Figure 2 This is a block diagram of the automated prediction system for CNC tool life in the embodiment;

[0025] The module consists of: 1. Data acquisition module; 2. Data analysis module; 3. Model building module; 4. Prediction module; and 5. Display module. Detailed Implementation

[0026] The present invention will be further described in detail below with reference to embodiments. However, it should not be construed that the scope of the above-described subject matter of the present invention is limited to the following embodiments, and all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0027] Example

[0028] See Figure 1A method for predicting the life of CNC tools, comprising:

[0029] Obtain the full life machining time data of CNC tools of the same model and material reaching the maximum allowable wear value, as well as the machining time, depth of cut, width of cut, cutting speed and historical data of cumulative tool wear before each machining process;

[0030] Based on the total machining time data of CNC tools and the machining time of each machining process, the remaining machining life value of CNC tools after each machining is obtained by analysis;

[0031] Using the depth of cut, width of cut, cutting speed and cumulative tool wear before each machining process as independent variables and the remaining machining life of the CNC tool after each machining process as the dependent variable, a prediction model for the remaining machining life of the CNC tool after each machining process is constructed.

[0032] Based on the depth of cut, width of cut, cutting speed of the current machining or machining process, and the cumulative tool wear before the current machining or machining process, the remaining cutting life of the tool after the completion of the current machining or machining process is obtained by using the prediction model.

[0033] In this embodiment, based on the full lifespan machining time data of the CNC tool during its use, as well as the historical data of machining time, depth of cut, width of cut, cutting speed, and cumulative tool wear before each machining process, a predictive model for the remaining machining lifespan of the CNC tool after each machining operation is constructed. This predictive model takes into account the historical cumulative wear and relevant process parameters during the current machining process, and can accurately predict the remaining safe machining time of the CNC tool, providing reliable support for the efficient and high-precision automated machining of aero-engine components.

[0034] In this embodiment, the prediction model for the remaining machining life of the CNC tool after each machining operation is as follows: Where T i V represents the remaining machining life of the CNC tool after the i-th machining process is completed. B Q represents the maximum allowable wear value of the cutting tool. i A represents the cumulative tool wear before the i-th machining process. i H represents the depth of cut data for the i-th machining process. i V represents the cutting width data for the i-th processing step. i Let be the cutting speed of the i-th machining process, α be a coefficient, and m, n, p, q be exponents. α, m, n, p, q are obtained through data fitting.

[0035] To further ensure the accuracy of the prediction model and provide operators with reliable remaining safe machining time, the cumulative tool wear before each machining operation in this embodiment is taken as the maximum value between the tool diameter wear and the tool length wear. Furthermore, the tool diameter wear and tool length wear in this embodiment are obtained through machine vision measurement.

[0036] See Figure 2 Based on the same inventive concept, this embodiment also provides an automated prediction system for CNC tool life, including:

[0037] Data acquisition module 1 is used to acquire the full life processing time data of CNC tools of the same model and material reaching the maximum allowable wear value, as well as the processing time, depth of cut, width of cut, cutting speed and historical data of cumulative tool wear before each processing of CNC tools;

[0038] Data analysis module 2 is used to analyze and obtain the remaining machining life value of the CNC tool after each machining process based on the CNC tool's total machining life data and the machining time of each machining process.

[0039] Model building module 3 is used to construct a prediction model for the remaining machining life of the CNC tool after each machining process, with the depth of cut, width of cut, cutting speed and cumulative tool wear before the corresponding machining process as independent variables and the remaining machining life of the CNC tool after the corresponding machining as the dependent variable.

[0040] Prediction module 4 is used to analyze and obtain the remaining cutting life of the tool after the current machining or machining process is completed by using the prediction model based on the depth of cut, width of cut, cutting speed of the current machining or machining process and the cumulative tool wear before the current machining or machining process.

[0041] The automated prediction system for CNC tool life in this embodiment also includes a display module 5, which is used to display the remaining cutting life time of the corresponding CNC tool based on the prediction results in the prediction module 4.

[0042] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting the life of CNC cutting tools, characterized in that, include: Obtain the full life machining time data of CNC tools of the same model and material reaching the maximum allowable wear value, as well as the machining time, depth of cut, width of cut, cutting speed and historical data of cumulative tool wear before each machining process; Based on the total machining time data of CNC tools and the machining time of each machining process, the remaining machining life value of CNC tools after each machining is obtained by analysis; Using the depth of cut, width of cut, cutting speed, and cumulative tool wear before each machining operation as independent variables, and the remaining tool life after each machining operation as the dependent variable, a predictive model for the remaining tool life after each machining operation is constructed. ,in For the first The remaining machining life of the CNC tool after the completion of the next machining process. This represents the maximum allowable wear value for the cutting tool. For the first Cumulative tool wear before the next machining process. For the first Cutting depth data for the next processing step. For the first Cutting width data for the secondary processing step. For the first Cutting speed of the next machining process For coefficients, , , , For the index, , , , , Obtained through data fitting; Based on the depth of cut, width of cut, cutting speed of the current machining or machining process, and the cumulative tool wear before the current machining or machining process, the remaining cutting life of the tool after the completion of the current machining or machining process is obtained by using the prediction model.

2. The CNC tool life prediction method according to claim 1, characterized in that, The cumulative tool wear before each machining operation is taken as the maximum value between the tool diameter wear and the tool length wear.

3. The CNC tool life prediction method according to claim 2, characterized in that, The cumulative wear of the cutting tool was obtained through machine vision measurement.

4. An automated prediction system for CNC tool life, characterized in that, include: The data acquisition module is used to acquire the full life processing time data of CNC tools of the same model and material reaching the maximum allowable wear value, as well as the processing time, depth of cut, width of cut, cutting speed and historical data of cumulative tool wear before each processing of CNC tools; The data analysis module is used to analyze and obtain the remaining machining life value of the CNC tool after each machining process based on the CNC tool's total machining life data and the machining time of each machining process. The model building module is used to construct a predictive model for the remaining machining life of the CNC tool after each machining operation, using the depth of cut, width of cut, cutting speed, and cumulative tool wear before the corresponding machining operation as independent variables, and the remaining machining life of the CNC tool after the corresponding machining operation as the dependent variable. ,in For the first The remaining machining life of the CNC tool after the completion of the next machining process. This represents the maximum allowable wear value for the cutting tool. For the first Cumulative tool wear before the next machining process. For the first Cutting depth data for the next processing step. For the first Cutting width data for the secondary processing step. For the first Cutting speed of the next machining process For coefficients, , , , For the index, , , , , Obtained through data fitting; The prediction module is used to analyze and obtain the remaining cutting life of the tool after the current machining or machining process is completed, based on the depth of cut, width of cut, cutting speed of the current machining or machining process and the cumulative tool wear before the current machining or machining process.

5. The automated prediction system for CNC tool life according to claim 4, characterized in that, In the model building module, the cumulative tool wear before each machining operation is taken as the maximum value between the tool diameter wear and the tool length wear.

6. The automated prediction system for CNC tool life according to claim 5, characterized in that, In the data acquisition module or the prediction module, the cumulative wear of the cutting tool is obtained through machine vision measurement.

7. The automated prediction system for CNC tool life according to claim 4, characterized in that, It also includes a display module, which displays the remaining cutting life of the corresponding CNC tool based on the prediction results in the prediction module.

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