Method for nondestructive testing of mechanical properties of cast steel gear body by micro-magnetic technique

Abstract

The application relates to a method for nondestructively detecting the mechanical property of a cast steel large gear body by using micro-magnetic technology, a three-axis motion mechanism is used to carry a micro-magnetic sensor to scan along the tooth thickness direction of the cast steel large gear, four types of micro-magnetic signals of a tooth profile surface are obtained, and 41 magnetic parameters are extracted; the distribution curves of the values of the magnetic parameters along the tooth thickness direction can be used to evaluate the uniformity of the mechanical property of the tooth profile surface; the 41 magnetic parameters measured are input into a pre-labeled neural network model, and the mechanical property of the tooth profile surface of the cast steel large gear can be quantitatively predicted; the method disclosed by the application can directly face the cast steel large gear for online detection of the body mechanical property by using the nondestructive characteristics of the micro-magnetic technology.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing technology, specifically to a method for nondestructive testing of the mechanical properties of cast steel large gears using micromagnetic technology, which can perform online evaluation of the surface mechanical properties of the tooth profile of cast steel large gears. Background Technology

[0002] Mechanical property testing is a crucial part of the manufacturing process for cast steel large gears. Tested mechanical property indicators include surface hardness, yield strength, and impact energy. Currently, surface hardness, yield strength, and impact energy are primarily tested using destructive testing methods, which cannot directly test the cast steel gear body itself.

[0003] Internationally, the method of indirectly characterizing mechanical properties using a single micro-magnetic signal (such as Barkhausen noise, eddy current, etc.) has been fully validated. For example, “Sorsa A, Santa-Aho S , Wartiainen J, et al. Effect of Shot Peening Parameters to Residual Stress Profiles and Barkhausen Noise[J]. Journal of Nondestructive Evaluation, 2018, 37(1):10. ” and “Ding S , Tian G , Sutthawe Ek Ul R . Non-destructive hardness prediction for 18CrNiMo7-6 steel based on feature selection and fusion of Magnetic Barkhausen Noise[J]. NDT &E international, 2019, 107(Oct.):102138.1-102138.8. ”, nondestructive characterization of surface hardness and residual stress of gear materials or gear cutting samples was carried out. However, there are very few reports on micro-magnetic nondestructive testing of gear materials for yield strength and impact energy.

[0004] Because micromagnetic signals are affected by various factors, it is difficult to use a limited number of magnetic parameters to perform high-precision testing of mechanical properties. To improve the ability of micromagnetic technology to detect mechanical properties, it is generally necessary to integrate multiple micromagnetic signals for quantitative detection of mechanical properties. For example, “Xiucheng L, Ruihuan Z, Bin W, et al. Quantitative Prediction of Surface Hardness in 12CrMoV Steel Plate Based on Magnetic Barkhausen Noise and Tangential Magnetic Field Measurements[J]. Journal of Nondestructive Evaluation, 2018, 37(2): 38.”, but so far, there are no reports on the use of micromagnetic technology to directly perform nondestructive testing of the mechanical properties of cast steel large gear bodies. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for non-destructive testing of the mechanical properties of cast steel large gears using micromagnetic technology. This method integrates four types of micromagnetic signals (Barkhausen noise, tangential magnetic field strength, incremental permeability, and multi-frequency eddy currents) and utilizes a three-axis motion mechanism carrying sensors to perform mechanical property testing on the cast steel large gear body.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for non-destructive testing of the mechanical properties of a cast steel gear using micromagnetic technology is disclosed. The method involves attaching a micromagnetic sensor to the tooth profile of the cast steel gear to detect four micromagnetic signals: Barkhausen noise, tangential magnetic field strength, incremental permeability, and multi-frequency eddy currents, extracting 41 magnetic parameters. Then, the distribution curves of these 41 magnetic parameters along the tooth thickness direction are plotted, and the coefficient of variation of the data in each curve is calculated to evaluate the spatial uniformity of the mechanical properties of the cast steel gear tooth profile. Finally, the measured 41 magnetic parameters and calibration experimental data of the mechanical properties are input into a neural network algorithm to train a neural network model, predict the mechanical properties to be tested, and plot the distribution curves of the mechanical properties along the tooth thickness direction.

[0008] The steps for building a neural network model are as follows:

[0009] 1) Collect furnace-fed samples of cast steel large gears and process them into flat tensile specimens and impact specimens;

[0010] 2) Micro-magnetic signals, including Barkhausen noise, tangential magnetic field strength, incremental permeability, and multi-frequency eddy currents, were detected on plate tensile and impact specimens using micro-magnetic sensors, and 41 magnetic parameters were extracted.

[0011] 3) The surface hardness and yield strength of the plate tensile specimens are determined by indentation method and tensile test method, and the impact energy of the impact specimens is determined by impact test method.

[0012] 4) Input the calibration experimental data of 41 magnetic parameters and mechanical performance indicators measured in all specimens into the neural network algorithm to train the neural network model.

[0013] The mechanical properties include surface hardness, yield strength, and impact energy.

[0014] The micro magnetic sensor is mounted on a three-axis motion mechanism. The three-axis motion mechanism travels along the rack and pinion guide rail and reaches the designated position of the cast steel gear. The three-axis motion mechanism clamps the micro magnetic sensor and scans along the tooth thickness direction of the cast steel gear.

[0015] Beneficial effects: The method of the present invention utilizes the non-destructive characteristics of micro-magnetic technology to directly perform online detection of the mechanical properties of cast steel gears. It can not only quantitatively predict the mechanical properties of the tooth profile of cast steel gears, but also obtain qualitative and quantitative evaluation results of the uniformity of mechanical property distribution along the tooth thickness direction through scanning. Attached Figure Description

[0016] Figure 1 Micromagnetic testing device for the mechanical properties of tooth profiles;

[0017] Figure 2 : Micro-magnetic detection sensor for tooth profile surface;

[0018] Figure 3 Four types of micro-magnetic signals;

[0019] Figure 4 Statistical results of the coefficient of variation of magnetic parameters;

[0020] Figure 5 Results of mechanical property testing in the tooth thickness direction;

[0021] Figure 6 : The process of establishing a quantitative prediction model for micromagnetic properties;

[0022] In the diagram: 1-Rack and pinion guide; 2-Three-axis motion mechanism; 3-Cast steel large gear. Detailed Implementation

[0023] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0024] This invention proposes a method for non-destructive testing of the mechanical properties of cast steel large gears using micro-magnetic technology. The method utilizes a micro-magnetic sensor on the tooth profile surface to detect the tooth profile surface of the cast steel large gear, and uses multiple micro-magnetic parameters of four simultaneously measured micro-magnetic signals (Barkhausen noise, tangential magnetic field strength, incremental permeability, and multi-frequency eddy current) to evaluate the mechanical properties of the cast steel large gear body.

[0025] Specifically, the method involves detecting four types of micro-magnetic signals (Barkhausen noise, tangential magnetic field strength, incremental permeability, and multi-frequency eddy current) on the tooth profile surface of a cast steel gear using a micro-magnetic sensor, extracting 41 magnetic parameters. These parameters are then scanned along the tooth thickness direction using a three-axis motion mechanism equipped with the micro-magnetic sensor to obtain the distribution curves of each magnetic parameter along the tooth thickness direction. This allows for non-destructive testing of the mechanical properties of the cast steel gear, including:

[0026] (1) Qualitative evaluation: The distribution curves of 41 magnetic parameters along the tooth thickness direction were plotted, and the coefficient of variation (standard deviation divided by mean) of the data in each curve was calculated to evaluate the spatial distribution uniformity of the mechanical properties of the tooth profile of the cast steel large gear. The physical meanings of the 41 magnetic parameters are as follows:

[0027] 1) The maximum value M of the Barkhausen noise butterfly curve max ;

[0028] 2) Mean value M of the envelope of the Barkhausen noise butterfly curve within a single magnetization period mean ;

[0029] 3) The intercept M of the Barkhausen noise butterfly curve with the vertical axis r ;

[0030] 4) The butterfly curve of Barkhausen noise in M max The corresponding tangential magnetic field strength H cm ;

[0031] 5) The butterfly curve of Barkhausen noise in M max The peak width DH25m corresponds to 25% at this position;

[0032] 6) The butterfly curve of Barkhausen noise in M max The peak width DH50m corresponds to 50% of the peak width.

[0033] 7) The butterfly curve of Barkhausen noise in M max The peak width DH75m corresponds to 75% of the peak width.

[0034] 8) The amplitude A3 of the third harmonic of the tangential magnetic field strength;

[0035] 9) The amplitude A5 of the 5th harmonic of the tangential magnetic field strength;

[0036] 10) The amplitude A7 of the 7th harmonic of the tangential magnetic field strength;

[0037] 11) The phase P3 of the third harmonic of the tangential magnetic field strength;

[0038] 12) The phase P5 of the 5th harmonic of the tangential magnetic field strength;

[0039] 13) The phase P7 of the 7th harmonic of the tangential magnetic field strength;

[0040] 14) The sum of the amplitudes of the 3rd, 5th and 7th harmonic components of the tangential magnetic field strength, UHS;

[0041] 15) Distortion factor K of tangential magnetic field strength;

[0042] 16) The amplitude H of the tangential magnetic field strength harmonic signal at the first zero-crossing point co ;

[0043] 17) The time-domain amplitude H of the signal at the zero-crossing point of the tangential magnetic field ro ;

[0044] 18) The amplitude V of the excitation voltage signal of the electromagnet mag ;

[0045] 19) The maximum value μ of the incremental permeability butterfly curve max ;

[0046] 20) Mean μ of the incremental permeability butterfly curve within a single magnetization period mean ;

[0047] 21) The intercept μ of the incremental permeability butterfly curve with the vertical axis r ;

[0048] 22) The incremental permeability butterfly curve at μ max The corresponding tangential magnetic field strength H at that location cu ;

[0049] 23) The incremental permeability butterfly curve at 25% μ max The corresponding peak width is DH25μ;

[0050] 24) The incremental permeability butterfly curve at 50% μ max The corresponding peak width is DH50μ;

[0051] 25) The incremental permeability butterfly curve at 75% μ max The corresponding peak width is DH75μ;

[0052] 26) The real parts Re1, Re2, Re3 and Re4 of the four frequency eddy current signals;

[0053] 27) The imaginary parts Im1, Im2, Im3 and Im4 of the four frequency eddy current signals;

[0054] 28) The amplitudes of the four frequency eddy current signals: Mag1, Mag2, Mag3, and Mag4;

[0055] 29) The phases Ph1, Ph2, Ph3 and Ph4 of the four frequency eddy current signals;

[0056] (2) Quantitative evaluation: The 41 magnetic parameters measured at each scanning position are input into the neural network model to predict the mechanical performance indicators to be tested (such as surface hardness, yield strength and impact energy, etc.), and the distribution curve of mechanical performance indicators along the tooth thickness direction is plotted. The steps for establishing the neural network model are as follows:

[0057] 2.1) Collect furnace-fed samples of cast steel large gears and process them into flat tensile specimens and impact specimens;

[0058] 2.2) Micro-magnetic signals (Barkhausen noise, tangential magnetic field strength, incremental permeability, and eddy current) were detected on the tensile and impact specimens of the flat plate using micro-magnetic sensors, and 41 magnetic parameters were extracted.

[0059] 2.3) The surface hardness and yield strength of the plate tensile specimens were determined by indentation method and tensile test method, and the impact energy of the impact specimens was determined by impact test method.

[0060] 2.4) Input the calibration experimental data of 41 magnetic parameters and mechanical performance indicators measured in all specimens into the neural network algorithm to train the neural network model.

[0061] The apparatus used in the detection method of this invention includes a micromagnetic detection instrument, a micromagnetic sensor for the tooth profile, and a triaxial motion mechanism. For example... Figure 1 The image shows a micro-magnetic detection device for the mechanical properties of tooth profiles. This device mainly consists of a rack guide 1 and a three-axis motion mechanism 2, on which a micro-magnetic sensor is mounted. The three-axis motion mechanism 2 travels along the rack guide 1 to the designated position on the cast steel gear 3 (the point where the tooth profile surface contacts the detection surface of the micro-magnetic sensor). The three-axis motion mechanism 2 then holds the micro-magnetic sensor and scans along the tooth thickness direction of the cast steel gear 3.

[0062] In practice, firstly, the cast steel large gear 3 is processed into flat tensile and impact specimens using the furnace-fed sample. The surface hardness, yield strength, and impact energy of the flat and impact specimens are measured using indentation, tensile, and impact testing methods. Then, a micro-magnetic sensor on the tooth profile surface is used to detect the micro-magnetic signals of the processed flat and impact specimens, extracting 41 magnetic parameters. Secondly, the distribution curves of the 41 magnetic parameters along the tooth thickness direction are plotted, and the coefficient of variation of the data in each curve is calculated to evaluate the spatial uniformity of the mechanical properties of the cast steel large gear 3 tooth profile surface. Finally, the measured 41 magnetic parameters and the calibration experimental data of the mechanical properties are input into a neural network algorithm to train a neural network model, predicting the mechanical properties to be measured (such as surface hardness, yield strength, and impact energy), and plotting the distribution curves of the mechanical properties along the tooth thickness direction.

[0063] like Figure 2 As shown, this is a micro-magnetic sensor for the tooth profile surface. When the micro-magnetic sensor scans the tooth profile surface, an AC signal is passed through the excitation coil to provide an external magnetic field, magnetizing the component under test. This allows the detection coil to simultaneously detect the Barkhausen noise signal, incremental permeability signal, and multi-frequency eddy current impedance signal. The Hall element can detect the tangential magnetic field strength signal. The four detected micro-magnetic signals are as follows: Figure 3 As shown.

[0064] like Figure 4 The figure shows the statistical results of the coefficient of variation of the magnetic parameters. The distribution curves of the magnetic parameters Pi (i≤41) ​​along the tooth thickness direction are plotted, and the coefficient of variation of the data in each curve is statistically analyzed. This can be used to evaluate the uniformity of the mechanical properties of the cast steel large gear 3.

[0065] like Figure 5 The figure shows the test results of mechanical properties (surface hardness, yield strength, and impact energy, etc.) in the tooth thickness direction. Forty-one magnetic parameters measured at each scanning position are input into a neural network model to predict the mechanical property indices to be tested, and the distribution curves of the mechanical property indices along the tooth thickness direction are plotted.

[0066] like Figure 6 The diagram illustrates the process of establishing a micro-magnetic quantitative prediction model for the mechanical properties. First, samples of the cast steel large gear 3 were collected during the furnace run, and flat plate tensile and impact specimens were prepared. Then, micro-magnetic signals from the flat plate tensile and impact specimens were detected using a micro-magnetic sensor, and 41 magnetic parameters were extracted. Next, the surface hardness, yield strength, and impact energy of the flat plate and impact specimens were measured using indentation and tensile testing methods. Finally, the calibrated experimental data of the 41 measured magnetic parameters and mechanical property indicators were input into a neural network algorithm to train the neural network model, thereby obtaining the predicted mechanical properties of the cast steel large gear 3.

[0067] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for non-destructive testing of the mechanical properties of a cast steel large gear body using micro-magnetic technology, characterized in that, A micro-magnetic sensor was attached to the tooth profile of a cast steel gear to detect four micro-magnetic signals: Barkhausen noise, tangential magnetic field strength, incremental permeability, and multi-frequency eddy currents, and 41 magnetic parameters were extracted. Then, a three-axis motion mechanism carrying the micro-magnetic sensor was used to scan along the tooth thickness direction, and the distribution curves of the 41 magnetic parameters along the tooth thickness direction were plotted. The coefficient of variation of the data in each curve was calculated to evaluate the spatial distribution uniformity of the mechanical properties of the cast steel gear tooth profile. The physical meanings of the 41 magnetic parameters are as follows: 1) The maximum value M of the Barkhausen noise butterfly curve max ; 2) Mean value M of the envelope of the Barkhausen noise butterfly curve within a single magnetization period mean ; 3) The intercept M of the Barkhausen noise butterfly curve with the vertical axis r ; 4) The butterfly curve of Barkhausen noise in M max The corresponding tangential magnetic field strength H cm ; 5) The butterfly curve of Barkhausen noise in M max The peak width DH25m corresponds to 25% at this position; 6) The butterfly curve of Barkhausen noise in M max The peak width DH50m corresponds to 50% of the peak width. 7) The butterfly curve of Barkhausen noise in M max The peak width DH75m corresponds to 75% of the peak width. 8) The amplitude A3 of the third harmonic of the tangential magnetic field strength; 9) The amplitude A5 of the 5th harmonic of the tangential magnetic field strength; 10) The amplitude A7 of the 7th harmonic of the tangential magnetic field strength; 11) The phase P3 of the third harmonic of the tangential magnetic field strength; 12) The phase P5 of the 5th harmonic of the tangential magnetic field strength; 13) The phase P7 of the 7th harmonic of the tangential magnetic field strength; 14) The sum of the amplitudes of the 3rd, 5th and 7th harmonic components of the tangential magnetic field strength, UHS; 15) Distortion factor K of tangential magnetic field strength; 16) The amplitude H of the tangential magnetic field strength harmonic signal at the first zero-crossing point co ; 17) The time-domain amplitude H of the signal at the zero-crossing point of the tangential magnetic field ro ; 18) The amplitude V of the excitation voltage signal of the electromagnet mag ; 19) The maximum value μ of the incremental permeability butterfly curve max ; 20) Mean μ of the incremental permeability butterfly curve within a single magnetization period mean ; 21) The intercept μ of the incremental permeability butterfly curve with the vertical axis r ; 22) The incremental permeability butterfly curve at μ max The corresponding tangential magnetic field strength H at that location cu ; 23) The incremental permeability butterfly curve at 25% μ max The corresponding peak width is DH25μ; 24) The incremental permeability butterfly curve at 50% μ max The corresponding peak width is DH50μ; 25) The incremental permeability butterfly curve at 75% μ max The corresponding peak width is DH75μ; 26) The real parts Re1, Re2, Re3 and Re4 of the four frequency eddy current signals; 27) The imaginary parts Im1, Im2, Im3 and Im4 of the four frequency eddy current signals; 28) The amplitudes of the four frequency eddy current signals: Mag1, Mag2, Mag3, and Mag4; 29) The phases Ph1, Ph2, Ph3 and Ph4 of the four frequency eddy current signals; Finally, the calibration experimental data of the 41 measured magnetic parameters and mechanical performance indicators were input into the neural network algorithm to train the neural network model, predict the mechanical performance indicators to be measured, and plot the distribution curve of the mechanical performance indicators along the tooth thickness direction. The steps for establishing the neural network model are as follows: 1) Collect furnace-fed samples of cast steel large gears and process them into flat tensile specimens and impact specimens; 2) Micro-magnetic sensors were used to detect Barkhausen noise, tangential magnetic field strength, incremental permeability, and multi-frequency eddy currents in flat tensile and impact specimens, and 41 magnetic parameters were extracted. 3) The surface hardness and yield strength of the plate tensile specimens are determined by indentation method and tensile test method, and the impact energy of the impact specimens is determined by impact test method. 4) Input the calibration experimental data of 41 magnetic parameters and mechanical performance indicators measured in all specimens into the neural network algorithm to train the neural network model.

2. The method for non-destructive testing of the mechanical properties of a cast steel large gear body using micromagnetic technology as described in claim 1, characterized in that, The mechanical properties include surface hardness, yield strength, and impact energy.

3. The method for non-destructive testing of the mechanical properties of a cast steel large gear body using micromagnetic technology as described in claim 1, characterized in that, The micro magnetic sensor is mounted on a three-axis motion mechanism. The three-axis motion mechanism travels along the rack and pinion guide rail and reaches the designated position of the cast steel gear. The three-axis motion mechanism clamps the micro magnetic sensor and scans along the tooth thickness direction of the cast steel gear.

Images