A stress relief system for the pick teeth of a coal mining machine drum

Abstract

The present invention discloses a coal mining machine drum pick stress relief system, which constructs a drum pick stress distribution model according to the on-site working conditions, and then integrates the corresponding relationship between the on-site working environment, the drum pick stress distribution, and the working mode of the stress relief system into a database and substitutes it into the BP neural network for training. Finally, the stress relief system performs stress relief on the drum pick according to the output result and provides real-time feedback on the stress relief situation. The system can effectively improve the stress relief efficiency, save resources, and reduce the safety hazards caused by stress relief on site.

Description

Technical Field

[0001] The invention relates to the technical field of drum pick stress elimination, and in particular to a drum pick stress elimination system for a coal mining machine. Background Art

[0002] During the operation of the coal mining machine, its picks are in direct contact with the coal seam, which is often mixed with hard minerals such as quartz and pyrite. When the drum rotates, the picks evenly distributed on the outer surface of the drum in a spiral line are in a periodic impact coal breaking state, accompanied by extrusion shearing, crushing and other stages. During this period, the picks are in a highly cutting and grinding state, and the stress on the picks is aggravated. The existence of stress is the main reason that affects the life of the picks and causes the failure of the picks. After the pick fails, the coal needs to be scraped by the spiral blades and squeezed by the pick body or the pick seat. This not only easily increases coal dust, but also easily causes sparks on the drum, which is particularly dangerous in high-gas mines. In addition, the failure of the picks of the coal mining machine can easily reduce the pushing rate of the bracket and the scraper, causing the working face to advance too slowly.

[0003] Aging treatment can eliminate the internal stress of the workpiece, stabilize the structure and size, improve the mechanical properties, etc. At present, there are four aging treatment methods: natural aging, thermal aging, vibration aging and ultrasonic impact aging. Among them, thermal aging is the most widely used stress relief method. Summary of the invention

[0004] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a coal mining machine drum pick stress relief system, which can effectively improve the stress relief efficiency, save resources, and reduce the potential safety hazards caused by stress relief on site.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0006] The present invention provides a coal mining machine drum pick stress relief system, comprising the following steps:

[0007] Step M1. Collect relevant working parameters according to the working conditions of the drum pick on site, including the rotation speed, temperature, stress condition, elastic modulus, and thermal expansion coefficient of the drum pick;

[0008] Step M2. Substituting the corresponding parameters into the inherent stress mathematical model to obtain the values ​​of compressive plastic stress and tensile plastic stress;

[0009] Step M3. Adding the compressive plastic stress and the tensile plastic stress in the drum pick to obtain the final inherent stress;

[0010] Step M4. Integrate the obtained inherent stress, and then correct it according to the field experimental sampling to obtain the final mathematical model of transverse and longitudinal inherent stress distribution and the mathematical model of transverse and longitudinal bending stress distribution;

[0011] Step M5. Perform multiple experiments to obtain a database of the corresponding relationship between the on-site working environment, the stress distribution of the drum pick, and the working mode of the stress relief system;

[0012] Step M6. Substitute the database into the BP neural network to train its weights, and then extract the roller pick phase parameters required for stress elimination as input information based on the internal relationship, so as to obtain the setting of the working mode of the stress elimination system;

[0013] Step M7. The industrial PC controls the stress relief system to relieve the stress of the drum pick according to the output data of the BP neural network;

[0014] Step M8. At the same time, the infrared detection device in the stress relief system monitors the stress relief situation in real time, and feeds back the stress changes to the industrial control PC to fine-tune the working parameters of the stress relief system in real time until the stress relief is completed.

[0015] Preferably, in step M2, the step of obtaining the values ​​of compressive plastic stress and tensile plastic stress according to corresponding data includes:

[0016] The internal stress distribution of the drum pick is the comprehensive result of two factors: one is the influence of the force and friction heat on the drum pick during operation, and the other is the stress change inside the drum pick when the work stops. The total stress inside the drum pick during the whole process includes elastic stress, plastic stress, thermal stress and metal phase change stress, which can be expressed as follows:

[0017] ε=ε e +ε p +ε t +ε x (10)

[0018] The inherent stress during the working process of the drum pick is also called inelastic stress, that is,

[0019] ε * =ε-ε e =ε p +ε t +ε x (11)

[0020] Among them, ε represents the total stress, ε* is the inelastic stress, and ε e is the elastic stress, ε p is the plastic stress, ε t is the thermal stress, ε x is the metal phase transition stress;

[0021] When the drum pick is not working, the thermal stress is considered to be zero; under the premise of ignoring the metal phase transformation stress, the residual plastic stress is equal to the inherent stress, that is,

[0022] ε * =ε p (12)

[0023] The tensile plastic stress, compressive plastic stress and tensile plastic stress are calculated by the following formula:

[0024] ε p1 =-a(T max -T 0 ) (13)

[0025]

[0026] Among them, ε p1 is the compressive plastic stress, ε p2 is the tensile plastic stress, a is the thermal expansion coefficient, T max is the maximum friction heat that can be achieved when the drum pick is working, T 0 is room temperature, T 2max The maximum change value of the internal temperature of the drum pick when it stops working, σ y is the yield stress, E is the elastic modulus, A is the cross-sectional area of ​​the drum pick, L is the width of the inherent stress, k z is the elastic coefficient of the drum pick.

[0027] Preferably, step M4 comprises:

[0028] A set of stress values ​​is used to characterize the stress distribution mathematical model of the entire drum pick. The stress distribution mathematical model of the section perpendicular to the drum pick includes four parts: transverse and longitudinal inherent stress, transverse and longitudinal bending stress. The inherent stress is integrated and calculated. Finally, the obtained model is corrected through multiple on-site sampling experiments. The specific stress distribution mathematical model is as follows:

[0029]

[0030]

[0031]

[0032]

[0033] In the formula, is the total transverse inherent stress, is the total longitudinal inherent stress, is the total transverse bending stress, is the total longitudinal bending stress, is the lateral component of the inherent stress, is the longitudinal component of the inherent stress, x is the side direction of the roller pick, y is the height direction of the roller pick, z is the direction perpendicular to the side direction of the roller pick, h is the thickness of the front section of the roller pick, c, d, f, g are the compensation coefficients of transverse and longitudinal inherent stresses, and transverse and longitudinal bending stresses, respectively.

[0034] Preferably, in step M6, the database is substituted into the BP neural network to train its weights, and the specific analysis is as follows:

[0035] The operation mechanism of BP neural network is divided into two parts, namely, the forward transmission of network input information and the reverse propagation of network calculation error;

[0036] The specific training and application steps of BP neural network are as follows;

[0037] (1) Determination of network topology and initialization of related parameters;

[0038] (2) BP neural network learning: the on-site working environment data information is transmitted from the input layer to the output layer via the hidden layer. The output layer calculates and outputs the stress distribution of the drum pick of the process network;

[0039] (3) Calculate the error between the network data drum pick stress distribution and the expected value in the database, and propagate the error information back to the input layer, while adjusting the weights and thresholds between each layer;

[0040] (4) Iterate steps (2) and (3) to gradually reduce the calculation error until the error reaches the set target error or the number of iterations reaches the set maximum number;

[0041] (5) Obtaining the optimal weights and thresholds;

[0042] (6) Extract the on-site working parameters of the test sample, calculate the stress distribution of the test sample with the help of the optimal weights and thresholds obtained in step (5), and finally obtain the corresponding working mode of the stress relief system based on the obtained stress distribution.

[0043] Preferably, in step M7, the stress elimination system includes an industrial PC, a multi-degree-of-freedom platform, an induction heating device and an infrared detection device; the industrial PC runs a BP neural network and outputs corresponding parameter settings for controlling the multi-degree-of-freedom platform, the induction heating device and the infrared detection device; the multi-degree-of-freedom platform drives the induction heating device to perform different degrees of stress elimination on other different positions according to the stress distribution of different roller pick teeth; the induction heating device controls the heating temperature change to achieve different degrees of stress elimination; the infrared detection device can monitor the stress changes of the roller pick teeth in real time and feedback the fine-tuning parameters.

[0044] The beneficial effects of the present invention are:

[0045] 1. The present invention collects relevant parameters of the drum pick working on site and establishes a mathematical model of stress distribution of the drum pick based on the collected parameters;

[0046] 2. The present invention compares the constructed stress distribution mathematical model with the actual situation, corrects the error of the theoretical model, and makes the desired model more accurate in describing the actual stress distribution;

[0047] 3. The present invention establishes a database of the corresponding relationship between the on-site working conditions, the stress distribution of the drum pick, and the working mode of the stress relief system;

[0048] 4. The present invention substitutes the database into the BP neural network training, and the stress distribution can be obtained by directly inputting the relevant data on site, and at the same time, a reasonable working mode setting of the stress relief system can be given;

[0049] 5. The present invention uses electromagnetic induction heating to eliminate the stress of the drum pick. The induction heating heat source has high heating efficiency, is easy to control and has no collateral effects. It is highly controllable, has high thermal efficiency, is energy-saving and clean, and has an obvious stress elimination effect.

[0050] 6. The present invention finally uses an infrared detection device to further ensure the working effect of the stress relief system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 A method flow chart of a coal mining machine drum pick stress relief system provided by the present invention.

[0053] Figure 2 A flow chart of the method for constructing a model of roller pick stress distribution provided by the present invention.

[0054] Figure 3 This is a flow chart of the method for using the BP neural network provided by the present invention.

[0055] Figure 4 A schematic diagram of a stress relief system according to an embodiment of the present invention.

[0056] Figure numerals: 1. industrial control PC, 2. multi-degree-of-freedom platform, 3. induction heating device, 4. infrared detection device, 5. drum pick. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] like Figures 1 to 3 As shown, a coal mining machine drum pick stress relief system,

[0059] The following steps are involved:

[0060] Step M1. Collect relevant working parameters according to the working conditions of the drum pick on site, including the rotation speed, temperature, stress condition, elastic modulus, and thermal expansion coefficient of the drum pick;

[0061] Step M2. Substituting the corresponding parameters into the inherent stress mathematical model to obtain the values ​​of compressive plastic stress and tensile plastic stress;

[0062] The steps for obtaining the values ​​of compressive plastic stress and tensile plastic stress based on the corresponding data include:

[0063] The internal stress distribution of the drum pick is the comprehensive result of two factors: one is the influence of the force and friction heat on the drum pick during operation, and the other is the stress change inside the drum pick when the work stops. The total stress inside the drum pick during the whole process includes elastic stress, plastic stress, thermal stress and metal phase change stress, which can be expressed as follows:

[0064] ε=ε e +ε p +ε t +ε x (19)

[0065] The inherent stress during the working process of the drum pick is also called inelastic stress, that is,

[0066] ε * =ε-ε e =ε p +ε t +ε x (20)

[0067] Among them, ε represents the total stress, ε* is the inelastic stress, and ε e is the elastic stress, ε p is the plastic stress, εt is the thermal stress, ε x is the metal phase transition stress;

[0068] When the drum pick is not working, the thermal stress is considered to be zero; under the premise of ignoring the metal phase transformation stress, the residual plastic stress is equal to the inherent stress, that is,

[0069] ε * =ε p (twenty one)

[0070] The tensile plastic stress, compressive plastic stress and tensile plastic stress are calculated by the following formula:

[0071] ε p1 =-a(T max -T 0 ) (twenty two)

[0072]

[0073] Among them, ε p1 is the compressive plastic stress, ε p2 is the tensile plastic stress, a is the thermal expansion coefficient, T max is the maximum friction heat that can be achieved when the drum pick is working, T 0 is room temperature, T 2max The maximum change value of the internal temperature of the drum pick when it stops working, σ y is the yield stress, E is the elastic modulus, A is the cross-sectional area of ​​the drum pick, L is the width of the inherent stress, k z is the elastic coefficient of the drum pick.

[0074] Step M3. Adding the compressive plastic stress and the tensile plastic stress in the drum pick to obtain the final inherent stress;

[0075] Step M4. Integrate the obtained inherent stress, and then correct it according to the field experimental sampling to obtain the final mathematical model of transverse and longitudinal inherent stress distribution and the mathematical model of transverse and longitudinal bending stress distribution;

[0076] Step M4 includes:

[0077] A set of stress values ​​is used to characterize the stress distribution mathematical model of the entire drum pick. The stress distribution mathematical model of the section perpendicular to the drum pick includes four parts: transverse and longitudinal inherent stress, transverse and longitudinal bending stress. The inherent stress is integrated and calculated. Finally, the obtained model is corrected through multiple on-site sampling experiments. The specific stress distribution mathematical model is as follows:

[0078]

[0079]

[0080]

[0081]

[0082] In the formula, is the total transverse inherent stress, is the total longitudinal inherent stress, is the total transverse bending stress, is the total longitudinal bending stress, is the lateral component of the inherent stress, is the longitudinal component of the inherent stress, x is the side direction of the roller pick, y is the height direction of the roller pick, z is the direction perpendicular to the side direction of the roller pick, h is the thickness of the front section of the roller pick, c, d, f, g are the compensation coefficients of transverse and longitudinal inherent stresses, and transverse and longitudinal bending stresses, respectively.

[0083] Step M5. Perform multiple experiments to obtain a database of the corresponding relationship between the on-site working environment, the stress distribution of the drum pick, and the working mode of the stress relief system;

[0084] Step M6. Substitute the database into the BP neural network to train its weights, and then extract the roller pick phase parameters required for stress elimination as input information based on the internal relationship, so as to obtain the setting of the working mode of the stress elimination system;

[0085] Substitute the database into the BP neural network to train its weights. The specific analysis is as follows:

[0086] The operation mechanism of BP neural network is divided into two parts, namely, the forward transmission of network input information and the reverse propagation of network calculation error;

[0087] The specific training and application steps of BP neural network are as follows;

[0088] (1) Determination of network topology and initialization of related parameters;

[0089] (2) BP neural network learning: the on-site working environment data information is transmitted from the input layer to the output layer via the hidden layer. The output layer calculates and outputs the stress distribution of the drum pick of the process network;

[0090] (3) Calculate the error between the network data drum pick stress distribution and the expected value in the database, and propagate the error information back to the input layer, while adjusting the weights and thresholds between each layer;

[0091] (4) Iterate steps (2) and (3) to gradually reduce the calculation error until the error reaches the set target error or the number of iterations reaches the set maximum number;

[0092] (5) Obtaining the optimal weights and thresholds;

[0093] (6) Extract the on-site working parameters of the test sample, calculate the stress distribution of the test sample with the help of the optimal weights and thresholds obtained in step (5), and finally obtain the corresponding working mode of the stress relief system based on the obtained stress distribution.

[0094] Step M7. The industrial PC controls the stress relief system to relieve the stress of the drum pick according to the output data of the BP neural network;

[0095] The stress elimination system includes an industrial control PC, a multi-degree-of-freedom platform, an induction heating device and an infrared detection device; the industrial control PC runs a BP neural network and outputs corresponding parameter settings for controlling the multi-degree-of-freedom platform, the induction heating device and the infrared detection device; the multi-degree-of-freedom platform drives the induction heating device to eliminate stresses of different degrees at other different positions according to the stress distribution of different roller pick teeth; the induction heating device controls the heating temperature change to achieve different degrees of stress elimination; the infrared detection device can monitor the stress changes of the roller pick teeth in real time and provide feedback for fine-tuning parameters.

[0096] Step M8. At the same time, the infrared detection device in the stress relief system monitors the stress relief situation in real time, and feeds back the stress changes to the industrial control PC to fine-tune the working parameters of the stress relief system in real time until the stress relief is completed.

[0097] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A coal mining machine drum pick stress relief system, characterized in that: The following steps are involved: Step M1. Collect relevant working parameters according to the working conditions of the drum pick on site, including the rotation speed, temperature, stress condition, elastic modulus and thermal expansion coefficient of the drum pick; Step M2. Substituting the corresponding parameters into the inherent stress mathematical model to obtain the values ​​of compressive plastic stress and tensile plastic stress; Step M3. Adding the compressive plastic stress and the tensile plastic stress in the drum pick to obtain the final inherent stress; Step M4. Integrate the obtained inherent stress, and then correct it according to the field experimental sampling to obtain the final mathematical model of transverse and longitudinal inherent stress distribution and the mathematical model of transverse and longitudinal bending stress distribution; Step M5. Perform multiple experiments to obtain a database of the corresponding relationship between the on-site working environment, the stress distribution of the drum pick, and the working mode of the stress relief system; Step M6. Substitute the database into the BP neural network to train its weights, and then extract the roller pick parameters required for stress relief as input information based on the database of the correspondence between the three in step M5, and obtain the setting of the working mode of the stress relief system; Step M7. The industrial PC controls the stress relief system to relieve the stress of the drum pick according to the output data of the BP neural network; Step M8. At the same time, the infrared detection device in the stress relief system monitors the stress relief situation in real time, and feeds back the stress changes to the industrial control PC to fine-tune the working parameters of the stress relief system in real time until the stress relief is completed.

2. A coal mining machine drum pick stress relief system according to claim 1, characterized in that: In step M2, the step of obtaining the values ​​of compressive plastic stress and tensile plastic stress according to corresponding data includes: The internal stress distribution of the drum pick is the comprehensive result of two factors: one is the influence of the force and friction heat on the drum pick during operation, and the other is the stress change inside the drum pick when the work stops. The total stress inside the drum pick during the whole process includes elastic stress, plastic stress, thermal stress and metal phase change stress, which can be expressed as follows: e=e e +e p +e t +e x (1) The inherent stress during the working process of the drum pick is also called inelastic stress, that is, e * =e-e e =e p +e t +e x (2) Among them, ε represents the total stress, ε* is the inelastic stress, and ε e is the elastic stress, ε p is the plastic stress, ε t is the thermal stress, ε x is the metal phase transition stress; When the drum pick is not working, the thermal stress is considered to be zero; under the premise of ignoring the metal phase transformation stress, the residual plastic stress is equal to the inherent stress, that is, e * =e p (3) The tensile plastic stress, compressive plastic stress and tensile plastic stress are calculated by the following formula: ε p1 =-a(T max -T0) (4) Among them, ε p1 is the compressive plastic stress, ε p2 is the tensile plastic stress, α is the thermal expansion coefficient, T max is the maximum friction heat that can be achieved when the drum pick is working, T0 is room temperature, T 2max The maximum change value of the internal temperature of the drum pick when it stops working, σ y is the yield stress, E is the elastic modulus, A is the cross-sectional area of ​​the drum pick, L is the width of the inherent stress, k z is the elastic coefficient of the drum pick.

3. A coal mining machine drum pick stress relief system according to claim 1, characterized in that: Step M4 includes: A set of stress values ​​is used to characterize the stress distribution mathematical model of the entire drum pick. The stress distribution mathematical model of the section perpendicular to the drum pick includes four parts: transverse and longitudinal inherent stress, transverse and longitudinal bending stress. The inherent stress is integrated and calculated. Finally, the obtained model is corrected through multiple on-site sampling experiments. The specific stress distribution mathematical model is as follows: In the formula, is the total transverse inherent stress, is the total longitudinal inherent stress, is the total transverse bending stress, is the total longitudinal bending stress, is the lateral component of the inherent stress, is the longitudinal component of the inherent stress, x is the side direction of the roller pick, y is the height direction of the roller pick, z is the direction perpendicular to the side direction of the roller pick, h is the thickness of the front section of the roller pick, c, d, f, g are the compensation coefficients of transverse and longitudinal inherent stresses, and transverse and longitudinal bending stresses, respectively.

4. A coal mining machine drum pick stress relief system according to claim 1, characterized in that: In step M6, the database is substituted into the BP neural network to train its weights. The specific analysis is as follows: The operation mechanism of BP neural network is divided into two parts, namely, the forward transmission of network input information and the reverse propagation of network calculation error; The specific training and application steps of BP neural network are as follows; (1) Determination of network topology and initialization of related parameters; (2) BP neural network learning: the on-site working environment data information is transmitted from the input layer to the output layer via the hidden layer. The output layer calculates and outputs the stress distribution of the drum pick of the process network; (3) Calculate the error between the network data drum pick stress distribution and the expected value in the database, and propagate the error information back to the input layer, while adjusting the weights and thresholds between each layer; (4) Iterate steps (2) and (3) to gradually reduce the calculation error until the error reaches the set target error or the number of iterations reaches the set maximum number; (5) Obtaining the optimal weights and thresholds; (6) Extract the on-site working parameters of the test sample, calculate the stress distribution of the test sample with the help of the optimal weights and thresholds obtained in step (5), and finally obtain the corresponding working mode of the stress relief system based on the obtained stress distribution.

5. A coal mining machine drum pick stress relief system according to claim 1, characterized in that: In step M7, the stress elimination system includes an industrial PC, a multi-degree-of-freedom platform, an induction heating device and an infrared detection device; the industrial PC runs a BP neural network and outputs corresponding parameter settings for controlling the multi-degree-of-freedom platform, the induction heating device and the infrared detection device; the multi-degree-of-freedom platform drives the induction heating device to perform different degrees of stress elimination on other different positions according to the stress distribution of different roller pick teeth; the induction heating device controls the heating temperature change to achieve different degrees of stress elimination; the infrared detection device monitors the stress changes of the roller pick teeth in real time and feeds back the fine-tuning parameters.

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