Optimization Method and System for Segmented Heat Treatment Process of Drag Bits Based on Temperature Field Simulation

Abstract

The present invention discloses an optimization method and system for the segmented heat treatment process of a rock drill based on temperature field simulation, including: constructing a three-dimensional temperature field simulation model of the rock drill based on the properties of the rock drill; obtaining the gradient temperature distribution characteristics and dividing the rock drill axially into a head strengthening section, a transition temperature equalizing section, and a tail slow cooling section; based on historical heat treatment data, calculating the temperature-stress evolution curves of each segment through transient thermal-structural coupling analysis and constructing an ideal temperature change model of the rock drill; performing heat treatment processing on each segment of the rock drill; calculating the error between the stress data of each segment of the rock drill and the temperature-stress evolution curves of each segment and feeding it back to the control system; dynamically adjusting the induction heating parameters to perform closed-loop temperature control on each segment. The advantages of the present invention are as follows: through axial multi-segment zoning temperature control, dynamic feedback adjustment, and simulation model prediction, the precise matching of the temperature-stress field in the heat treatment process of the rock drill is achieved, breaking through the bottleneck of the imbalance between strength and toughness in the traditional process.

Description

Technical Field

[0001] The present invention relates to process optimization, and particularly to an optimization method and system for the segmented heat treatment process of a drag bit based on temperature field simulation. Background Art

[0002] As a key tool for oil and gas drilling, the drag bit is long-term subjected to the high pressure, impact and abrasion of complex underground formations, and its performance is directly related to the drilling efficiency and production cost. The traditional heat treatment process usually adopts the conventional path of quenching + tempering, and improves the surface hardness through martensite phase transformation, but there is a tendency of microcracks caused by quenching stress concentration, and it is difficult to synchronously optimize the toughness of the core.

[0003] With the large-scale development of deep wells, ultra-deep wells and shale gas horizontal wells, the working temperature of the drill bit rises from 200°C to over 400°C. The cutting teeth treated by the conventional process have problems such as a decrease in high-temperature red hardness and spalling of the wear-resistant layer. The industry has tried to introduce dual-frequency induction heating and gradient temperature control technologies to improve the distribution of the hardened layer, and explore composite strengthening means such as boronizing and physical vapor deposition, but there are defects such as poor process stability and high energy consumption. Summary of the Invention

[0004] In order to improve the existing optimization method and system for the heat treatment process of a drag bit, an optimization method and system for the segmented heat treatment process of a drag bit based on temperature field simulation are provided. This method realizes the precise matching of the temperature-stress field in the heat treatment process of the drag bit through axial multi-segment zoning temperature control, dynamic feedback regulation, and simulation model prediction and real-time closed-loop control, breaking through the bottleneck of the imbalance between strength and toughness of the traditional process.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] An optimization method for the segmented heat treatment process of a drag bit based on temperature field simulation, comprising:

[0007] Establish a three-dimensional temperature field simulation model of the drag bit including the non-linear properties of the material based on the geometric parameters, material parameters and phase transformation kinetics parameters of the drag bit;

[0008] Based on the distributed thermocouple array, obtain the gradient temperature distribution characteristics, and divide the drag bit along the axis into a head strengthening section, a transition temperature equalizing section and a tail slow cooling section;

[0009] Based on historical heat treatment data, calculate the temperature-stress evolution curves of each segment through transient thermo-structural coupling analysis, train the model, and construct an ideal temperature change model for each segment during the heat treatment process of the drag bit;

[0010] Based on the ideal temperature change model, perform heat treatment processing on each segment of the drag bit, and collect the temperature data of each segment in real time through the distributed thermocouple array monitoring system;

[0011] Based on the stress data of each segment of the drag bit under the acquired temperature data of each segment, calculate the error between it and the temperature-stress evolution curve of each segment and feedback it to the control system;

[0012] Dynamically adjust the induction heating parameters based on the error size for the real-time temperature field data, and perform temperature closed-loop control for each segment.

[0013] Preferably, the establishment of a three-dimensional temperature field simulation model of the drag bit including the nonlinear properties of the material based on the geometric parameters, material parameters, and phase change kinetics parameters of the drag bit specifically includes:

[0014] Based on the three-dimensional structure of the drag bit, generate a parametric model using the Cartesian coordinate system through CAD software;

[0015] Obtain the thermal physical properties parameters and phase change kinetics parameters of the material;

[0016] Set the heat radiation-convection coupling boundary conditions based on the variation law of the surface heat flux movement of the drag bit;

[0017] Based on the above steps, construct a three-dimensional temperature field simulation model of the drag bit.

[0018] Preferably, the acquisition of the gradient temperature distribution characteristics based on the distributed thermocouple array and the division of the drag bit into a head strengthening section, a transition temperature equalizing section, and a tail slow cooling section specifically includes:

[0019] Arrange a temperature measurement point along the axial direction of the drag bit, and arrange 4 sensors in the circumferential direction of each cross-section to monitor the radial temperature uniformity;

[0020] Calculate the temperature change rate based on the measured temperature data, and construct a nonlinear gradient temperature change function based on the temperature change rate;

[0021] Based on the nonlinear gradient temperature change function, divide the drag bit into a head strengthening section, a transition temperature equalizing section, and a tail slow cooling section.

[0022] Preferably, the calculation of the temperature-stress evolution curve of each segment through transient thermal-structural coupling analysis based on historical heat treatment data, training the model, and constructing an ideal temperature change model for each segment during the heat treatment process of the drag bit specifically includes:

[0023] Based on historical heat treatment data, perform transient thermal-structural coupling modeling, obtain the boundary conditions, and set the solver;

[0024] Based on the transient thermal-structural coupling model, simulate the typical working conditions of each segment and output the temperature-stress evolution curve function of each segment;

[0025] Input the obtained temperature-stress evolution curve function data into the neural network model for training, and perform reverse solution on the ideal temperature model through Pareto front analysis to construct the ideal temperature change model for each stage in the heat treatment process of the auger drill.

[0026] Preferably, based on the ideal temperature change model, heat treatment is carried out on each stage of the auger drill, and the temperature data of each segment is collected in real time through the distributed thermocouple array monitoring system, which specifically includes:

[0027] Based on the obtained ideal temperature change model, discretize the ideal temperature curve into a time-temperature matrix, write it into the heat treatment equipment through the OPC UA protocol, and establish a three-dimensional coordinate mapping to bind the theoretical model coordinates with the physical positions of the actual thermocouples;

[0028] Based on the distributed thermocouple array, collect the temperature data of each segment in real time.

[0029] Preferably, based on the stress data of each segment of the auger drill under the obtained temperature data of each segment, calculate the error size between it and the temperature-stress evolution curve of each segment and feedback it to the control system, which specifically includes:

[0030] Realize the clock synchronization of the thermocouple array and the strain sensor through the IEEE 1588 PTP protocol;

[0031] Based on the obtained temperature data and stress data, perform cubic spline interpolation to uniformly process the timestamps;

[0032] Establish a coordinate mapping table between the temperature sensor and the strain gauge to ensure that each temperature measurement point corresponds to 3 strain gauges;

[0033] Obtain the stress data within the current segments through the stress calculation model;

[0034] Based on the obtained stress data of each segment, perform non-linear alignment with the temperature-stress evolution curve of each segment, calculate the regularized cumulative distance matrix, and extract the minimum path error value;

[0035] Based on the obtained error value data, feedback it to the possible perception system.

[0036] Preferably, based on the error size, dynamically adjust the induction heating parameters for the real-time temperature field data, and perform closed-loop temperature control on each segment, which specifically includes:

[0037] Based on the obtained error size data of each segment, perform error classification evaluation to determine whether to make adjustments;

[0038] Based on the segments that need to be adjusted, calculate the required power for each segment according to the error distribution, and dynamically adjust the frequency through the skin depth formula;

[0039] Multi-segment collaborative closed-loop control is carried out through decoupled control architecture design.

[0040] Furthermore, an optimized system for the segmented heat treatment process of a drag bit based on temperature field simulation is proposed, including:

[0041] Temperature field simulation modeling module: The temperature field simulation modeling module is mainly used to construct a three-dimensional non-linear temperature field model based on the geometry, material and phase change parameters of the drag bit;

[0042] Temperature gradient zoning module: The temperature gradient zoning module is mainly used to divide the head strengthening section, transition uniform temperature section and tail slow cooling section through the monitoring data of the distributed thermocouple array;

[0043] Transient coupling analysis module: The transient coupling analysis module is mainly used to train a thermal-structural coupling model using historical data to generate the ideal temperature-stress evolution curves of each segment;

[0044] Real-time data acquisition module: The real-time data acquisition module is mainly used to realize the synchronous acquisition and mapping of temperature data through the distributed thermocouple array and the OPC UA protocol;

[0045] Error calculation and feedback module: The error calculation and feedback module is mainly used to calculate the temperature-stress error using a stress calculation model and a non-linear alignment algorithm and feedback it to the control system;

[0046] Dynamic closed-loop control module: The dynamic closed-loop control module is mainly used to dynamically adjust the induction heating parameters based on the error classification and skin depth formula to achieve multi-segment collaborative temperature control;

[0047] Processor: The processor is mainly used for the calculation process of each formula and the construction and calculation process of each model.

[0048] Compared with the prior art, the advantages of the present invention are:

[0049] By establishing a three-dimensional temperature field simulation model that integrates the non-linear characteristics of materials and phase change kinetics, combined with the axial gradient zoning temperature control strategy and real-time feedback mechanism, the accuracy and stability of the drag bit heat treatment process are significantly improved. Based on the axial segmented monitoring of the distributed thermocouple array, differential temperature control can be implemented for the head strengthening section of the drill bit (which needs to be rapidly quenched to obtain high-hardness martensite), the transition uniform temperature section (which needs to balance the phase change stress), and the tail slow cooling section (which needs to retain retained austenite to improve toughness), effectively solving the hardness-toughness mismatch problem caused by traditional integral heat treatment. Through the transient thermal-structural coupling model trained with historical data, the temperature-stress evolution path of each segment can be accurately predicted, and the induction heating frequency, power and cooling medium flow rate are dynamically adjusted in combination with the stress error feedback collected in real time, greatly reducing the temperature field fluctuation range, thereby suppressing quenching cracks and optimizing the carbide dispersion. Brief Description of the Drawings

[0050] Figure 1 It is a schematic diagram of the method proposed by the present invention;

[0051] Figure 2 It is a schematic diagram for constructing the temperature field simulation model proposed by the present invention;

[0052] Figure 3 It is a schematic diagram of the sectionalization of the auger drill proposed by the present invention;

[0053] Figure 4 It is a schematic diagram for constructing the ideal temperature change model proposed by the present invention;

[0054] Figure 5 It is a schematic diagram of the heat treatment proposed by the present invention;

[0055] Figure 6 It is a schematic diagram of the error calculation and feedback proposed by the present invention;

[0056] Figure 7 It is a schematic diagram of the temperature closed-loop control proposed by the present invention

[0057] Figure 8 It is an architecture diagram of the electronic device in this solution;

[0058] Figure 9 It is a schematic diagram of the structure of the computer-readable storage medium in this solution. Detailed Embodiments

[0059] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0060] The optimized system for the sectional heat treatment process of the auger drill based on temperature field simulation includes:

[0061] Temperature field simulation modeling module: The temperature field simulation modeling module is mainly used to construct a three-dimensional non-linear temperature field model based on the geometry, material and phase change parameters of the auger drill;

[0062] Temperature gradient partitioning module: The temperature gradient partitioning module is mainly used to divide the head strengthening section, the transition temperature equalizing section and the tail slow cooling section through the monitoring data of the distributed thermocouple array;

[0063] Transient coupling analysis module: The transient coupling analysis module is mainly used to train the thermal-structural coupling model using historical data to generate the ideal temperature-stress evolution curves of each section;

[0064] Real-time data acquisition module: The real-time data acquisition module is mainly used to achieve synchronous acquisition and mapping of temperature data through a distributed thermocouple array and the OPC UA protocol;

[0065] Error calculation and feedback module: The error calculation and feedback module is mainly used to calculate the temperature-stress error using a stress calculation model and a non-linear alignment algorithm and feedback it to the control system;

[0066] Dynamic closed-loop control module: The dynamic closed-loop control module is mainly used to dynamically adjust the induction heating parameters based on error classification and the skin depth formula to achieve multi-segment collaborative temperature control;

[0067] Processor: The processor is mainly used for the calculation process of each formula and the construction and calculation process of each model.

[0068] Refer to Figure 1 As shown, the optimized method for the segmented heat treatment process of a rock drill based on temperature field simulation includes:

[0069] Step 1: Based on the geometric parameters, material parameters, and phase transformation kinetics parameters of the rock drill, establish a three-dimensional temperature field simulation model of the rock drill that includes the non-linear properties of the material;

[0070] Step 2: Based on a distributed thermocouple array, obtain the gradient temperature distribution characteristics, and divide the rock drill along the axial direction into a head strengthening section, a transition temperature equalization section, and a tail slow cooling section;

[0071] Step 3: Based on historical heat treatment data, calculate the temperature-stress evolution curves of each segment through transient thermal-structural coupling analysis, train the model, and construct an ideal temperature change model for each segment during the heat treatment process of the rock drill;

[0072] Step 4: Based on the ideal temperature change model, perform heat treatment processing on each segment of the rock drill, and use a distributed thermocouple array monitoring system to collect the temperature data of each segment in real time;

[0073] Step 5: Based on the stress data of each segment of the rock drill under the temperature data of each segment obtained, calculate the error between it and the temperature-stress evolution curve of each segment and feedback it to the control system;

[0074] Step 6: Dynamically adjust the induction heating parameters based on the error magnitude for the real-time temperature field data, and perform closed-loop temperature control on each segment.

[0075] Refer to Figure 2 As shown, the establishment of a three-dimensional temperature field simulation model of the rock drill that includes the non-linear properties of the material based on the geometric parameters, material parameters, and phase transformation kinetics parameters of the rock drill specifically includes:

[0076] Based on the three-dimensional structure of the rock drill, use CAD software to generate a parametric model using a Cartesian coordinate system;

[0077] Obtain the thermophysical property parameters and phase change kinetic parameters of the material;

[0078] Set the coupled heat radiation - convection boundary condition based on the variation law of the heat flux movement on the surface of the drag bit;

[0079] Based on the above steps, construct a three - dimensional temperature field simulation model of the drag bit.

[0080] Specifically, adopt the Cartesian coordinate system and define the geometric parameters of the drag bit: the drill bit diameter D, the total length L, the helix angle , the edge length l, and the number of spiral flutes N;

[0081] The phase change volume fraction f is described by the Johnson - Mehl - Avrami equation, and the formula is:

[0082]

[0083] When setting the coupled heat radiation - convection boundary condition, based on the Stefan - Boltzmann law, obtain the radiative heat flux density, and the formula is:

[0084]

[0085] Among them, is the emissivity, is the ambient temperature.

[0086] Refer to Figure 3 As shown, based on the distributed thermocouple array, obtain the gradient temperature distribution characteristics, and divide the drag bit along the axial direction into a head strengthening section, a transition uniform temperature section, and a tail slow cooling section, specifically including:

[0087] Arrange a temperature measurement point along the axial direction of the drag bit, and arrange 4 sensors in the circumferential direction of each cross - section to monitor the radial temperature uniformity;

[0088] Calculate the temperature change rate based on the measured temperature data, and construct a non - linear gradient temperature change function based on the temperature change rate;

[0089] Based on the non - linear gradient temperature change function, divide the drag bit into a head strengthening section, a transition uniform temperature section, and a tail slow cooling section.

[0090] Specifically, arrange m temperature measurement cross - sections at equal intervals along the axis of the drag bit (the interval is ), and arrange 4 sensors evenly in the circumferential direction of each cross - section (the angular interval is 90°);

[0091] Collect time - series temperature data , where i = 1, 2, …, m is the cross - section number, j = 1, 2, 3, 4 is the sensor number in the circumferential direction, are discrete time points ( is the sampling interval);

[0092] For each sensor data, calculate the instantaneous temperature change rate: Calculate the axial average temperature change rate: ;

[0093] By analyzing of the gradient, determine the critical point of the sudden change in the temperature change rate, and based on the critical point, conduct segmented division, where the head strengthening section: corresponding to the drill cutting area, with high heat flux density and large temperature gradient; the transition uniform temperature section: dominated by heat conduction, and the temperature distribution tends to be uniform; the tail slow cooling section: far from the heat source, mainly dissipating heat by natural convection;

[0094] Calculate the temperature standard deviation of each cross-section for segmented verification. The formula is:

[0095]

[0096] Refer to Figure 4 As shown, based on historical heat treatment data, calculate the temperature-stress evolution curves of each segment through transient thermo-structural coupling analysis, train the model, and construct the ideal temperature change model for each segment during the heat treatment process of the cross bit, specifically including:

[0097] Based on historical heat treatment data, conduct transient thermo-structural coupling modeling, obtain the boundary conditions, and set the solver;

[0098] Based on the transient thermo-structural coupling model, simulate the typical working conditions of each segment and output the temperature-stress evolution curve functions of each segment;

[0099] Input the obtained temperature-stress evolution curve function data into the neural network model for training, and conduct reverse solution of the ideal temperature model through Pareto front analysis to construct the ideal temperature change model for each segment during the heat treatment process of the cross bit.

[0100] Specifically, during the transient thermo-structural coupling modeling process, the formula of the transient heat conduction equation is:

[0101]

[0102] Among them, is the density, is the specific heat capacity, is the thermal conductivity, is the internal heat source;

[0103] The structural equilibrium equation considering thermal strain is:

[0104]

[0105] Among them, is the stress tensor, is the strain tensor, is the elastic matrix, is the coefficient of thermal expansion;

[0106] When configuring the solver, select implicit time integration (such as the Newmark-β method), adaptive control of the time step, and for the coupled solution strategy, select direct coupling (such as COMSOL) or sequential coupling (thermal analysis first and then structural analysis);

[0107] Divide the auger into multiple sections (such as heating section, insulation section, cooling section), apply typical process parameters to each section, and extract the temperature T(t) and stress (t) data varying with time, and fit it into an explicit function;

[0108] Through Pareto optimization, minimize the residual stress and maximize the hardness to optimize the multi-objective problem;

[0109] Predict the optimal process parameter combination through a neural network, and back-calculate the temperature curve. The formula is:

[0110]

[0111] Among them, is the inverse mapping of the neural network. Input the ideal temperature model into the coupled simulation to verify whether the stress and hardness meet the standards.

[0112] Refer to Figure 5 As shown, based on the ideal temperature change model, perform heat treatment on each section of the auger, and use the distributed thermocouple array monitoring system to collect the temperature data of each section in real time. Specifically include:

[0113] Based on the obtained ideal temperature change model, discretize the ideal temperature curve into a time-temperature matrix, write it into the heat treatment equipment through the OPC UA protocol, and establish a three-dimensional coordinate mapping to bind the theoretical model coordinates with the physical positions of the actual thermocouples;

[0114] Based on the distributed thermocouple array, collect the temperature data of each section in real time.

[0115] Specifically, discretize the continuous ideal temperature model at the time step into a time-temperature matrix , and the format is as follows:

[0116]

[0117] Among them, , is determined by the device control cycle;

[0118] A heat treatment device based on segments generates an independent matrix for each segment;

[0119] During the binding process of three-dimensional coordinate mapping and thermocouple position, establish the mapping relationship from theoretical coordinates to thermocouple position:

[0120]

[0121] where is the coordinate transformation function;

[0122] Based on PID control, adjust the heating power according to the deviation between the measured temperature and the set value , the formula is:

[0123]

[0124] If the temperature of a certain segment continues to deviate, update , and rewrite it into the OPC UA node.

[0125] Refer to Figure 6 As shown, based on the stress data of each segment of the auger under the obtained temperature data of each segment, calculate the error size between it and the temperature-stress evolution curve of each segment and feedback it to the control system, specifically including:

[0126] Realize the clock synchronization of the thermocouple array and the strain sensor through the IEEE 1588 PTP protocol;

[0127] Based on the obtained temperature data and stress data, perform cubic spline interpolation to uniformly process the timestamps;

[0128] Establish a coordinate mapping table between the temperature sensor and the strain gauge to ensure that each temperature measurement point corresponds to 3 strain gauges;

[0129] Obtain the stress data within the current segments through the stress calculation model;

[0130] Based on the obtained stress data of each segment, perform non-linear alignment with the temperature-stress evolution curve of each segment, calculate the regularized cumulative distance matrix, and extract the minimum path error value;

[0131] Based on the obtained error value data, feedback it to the possible perception system.

[0132] Specifically, when uniformly processing the timestamps by cubic spline interpolation, sort the temperature data and stress data by time respectively, and construct a spline function. The formula is:

[0133]

[0134] where the coefficient , , , Solve through boundary conditions;

[0135] Similarly, construct a stress spline function;

[0136] Calculate interpolation at a unified timestamp:

[0137]

[0138] Insufficient number of strain gauges around the thermocouple or too far distance will cause mapping failure. Therefore, the temperature-strain gauge coordinate mapping problem is solved by increasing the strain gauge density or using inverse distance weighted (IDW) interpolation;

[0139] At the same time, the O(nm) complexity of the long sequence DTW leads to poor real-time performance and high computational complexity. It can sacrifice accuracy for speed by using constrained DTW (such as Sakoe-Chiba Band to limit the path search range) or downsampling or segmented DTW.

[0140] See Figure 7 As shown, the induction heating parameters are dynamically adjusted based on the error size of the real-time temperature field data. The specific temperature closed-loop control for each segment includes:

[0141] Based on the error size data obtained for each segment, conduct error classification evaluation to determine whether to make adjustments;

[0142] Based on the segments that need to be adjusted, calculate the required power for each segment according to the error distribution, and dynamically adjust the frequency through the skin depth formula;

[0143] Conduct multi-segment collaborative closed-loop control through decoupled control architecture design.

[0144] Specifically, define an error threshold, normalize and weight the errors of each segment, and calculate the adjustment priority. The skin depth formula is:

[0145]

[0146] Among them, is the resistivity, is the magnetic permeability, is the current frequency. By adjusting control the heating depth to make the temperature gradient match the error distribution;

[0147] The power compensation formula during frequency modulation does not consider the non-linearity of eddy current loss. It can introduce an eddy current loss coefficient or inversely infer the actual energy distribution through an infrared thermal imager. The resistivity varies with temperature, resulting in Calculate the deviation so that the skin depth mismatches with the material properties, and solve the problem by updating the material parameters online and dynamically adjusting based on the feedback of the impedance analyzer.

[0148] Furthermore, the method according to the embodiment of the present application can also be implemented by means of Figure 8 the architecture of the electronic device shown. As Figure 8 shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to the network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, can store the method and system for optimizing the segmented heat treatment process of the auger based on temperature field simulation provided by the present application. The electronic device 500 may also include a terminal interface 508. Of course, Figure 8 the architecture shown is only exemplary. When implementing different devices, one or more components in the Figure 8 shown electronic device can be omitted according to actual needs.

[0149] Figure 9 is a schematic diagram of the structure of a computer-readable storage medium provided by an embodiment of the present application. As Figure 9 shown, it is a computer-readable storage medium 600 according to an embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are run by a processor, the method and system for optimizing the segmented heat treatment process of the auger based on temperature field simulation according to the embodiment of the present application described with reference to the above drawings can be executed. The storage medium 600 includes but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory, etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0150] It should be noted that: the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0151] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments.

[0152] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation, characterized in that: include: Based on the geometric parameters, material parameters and phase change kinetic parameters of the drill, a three-dimensional temperature field simulation model of the drill including the nonlinear properties of the material is established; Based on the distributed thermocouple array, the gradient temperature distribution characteristics are obtained, and the drill bit is divided into the head strengthening section, the transition temperature section and the tail slow cooling section along the axial direction; Based on historical heat treatment data, the temperature-stress evolution curve of each segment is calculated through transient thermal-structural coupling analysis, the model is trained, and the ideal temperature change model of each segment during the heat treatment process of the drill is constructed; Based on the ideal temperature change model, each section of the drill bit is heat treated, and the temperature data of each section is collected in real time through the distributed thermocouple array monitoring system. Based on the obtained stress data of each segment of the drill under the temperature data of each segment, the error between the stress data and the temperature-stress evolution curve of each segment is calculated and fed back to the control system; The induction heating parameters are dynamically adjusted based on the real-time temperature field data based on the error size, and the temperature of each segment is closed-loop controlled.

2. The method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation according to claim 1 is characterized in that: The establishment of a three-dimensional temperature field simulation model of a drill including nonlinear properties of the material based on the drill geometric parameters, material parameters and phase change kinetic parameters specifically includes: Based on the three-dimensional structure of the drill, a parametric model is generated using the Carl coordinate system through CAD software; Obtain material thermophysical properties and phase change kinetic parameters; The thermal radiation-convection coupling boundary condition is set based on the variation law of heat flow on the drill surface; Based on the above steps, a three-dimensional temperature field simulation model of the drill is constructed.

3. The method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation according to claim 1 is characterized in that: The method of obtaining the gradient temperature distribution characteristics based on the distributed thermocouple array and dividing the drill bit axially into a head strengthening section, a transition temperature equalization section and a tail slow cooling section specifically includes: A temperature measuring point is arranged along the axial direction of the drill bit, and 4 sensors are arranged in the circumferential direction of each section to monitor the radial temperature uniformity; Calculating the temperature change rate based on the measured temperature data, and constructing a nonlinear gradient temperature change function based on the temperature change rate; Based on the nonlinear gradient temperature change function, the drill bit is divided into the head strengthening section, the transition temperature equalization section and the tail slow cooling section.

4. The method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation according to claim 1 is characterized in that: The method of calculating the temperature-stress evolution curve of each segment through transient thermal-structural coupling analysis based on historical heat treatment data, training the model, and constructing the ideal temperature change model of each segment during the heat treatment of the drill specifically includes: Based on historical heat treatment data, transient thermal-structural coupling modeling is performed, boundary conditions are obtained, and solvers are set up; Based on the transient thermal-structural coupling model, the typical working conditions of each segment are simulated, and the temperature-stress evolution curve function of each segment is generated; The acquired temperature-stress evolution curve function data is input into the neural network model for training, and the ideal temperature model is reversely solved through Pareto front analysis to construct the ideal temperature change model of each section in the heat treatment process of Zhiluo drill.

5. The method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation according to claim 1 is characterized in that: Based on the ideal temperature change model, the heat treatment process is performed on each segment of the drill bit, and the temperature data of each segment is collected in real time through the distributed thermocouple array monitoring system, which specifically includes: Based on the acquired ideal temperature change model, the ideal temperature curve is discretized into a time-temperature matrix, written into the heat treatment equipment through the OPC UA protocol, and a three-dimensional coordinate mapping is established to bind the theoretical model coordinates with the actual physical position of the thermocouple; Based on the distributed thermocouple array, the temperature data of each segment is collected and acquired in real time.

6. The method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation according to claim 1 is characterized in that: The method of calculating the error between the stress data of each segment of the drill bit under the temperature data of each segment and the temperature-stress evolution curve of each segment and feeding back the error to the control system specifically includes: The clock synchronization between the thermocouple array and the strain sensor is achieved through the IEEE 1588 PTP protocol; Based on the acquired temperature data and stress data, cubic spline interpolation is performed to uniformly process the timestamps; Establish a coordinate mapping table between the temperature sensor and the strain gauge to ensure that each temperature measurement point corresponds to three strain gauges; Obtain stress data in each current segment through the stress solution model; Based on the acquired stress data of each segment, nonlinear alignment is performed with the temperature-stress evolution curve of each segment, and the regularized cumulative distance matrix is ​​calculated to extract the minimum path error value; Based on the obtained error value data, it is fed back to the possible perception system.

7. The method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation according to claim 1 is characterized in that: The method of dynamically adjusting the induction heating parameters based on the real-time temperature field data based on the error size and controlling the temperature closed loop of each segment specifically includes: Based on the obtained error size data of each segment, error classification assessment is performed to determine whether adjustments should be made; Based on the segments that need to be adjusted, the required power of each segment is calculated according to the error distribution, and the frequency is dynamically adjusted through the skin depth formula; Multi-stage collaborative closed-loop control is performed through the design of decoupling control architecture.

8. A system for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation, used to implement the method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation as claimed in any one of claims 1 to 7, characterized in that: include: Temperature field simulation modeling module: The temperature field simulation modeling module is mainly used to build a three-dimensional nonlinear temperature field model based on the drill geometry, material and phase change parameters; Temperature gradient partition module: The temperature gradient partition module is mainly used to divide the head strengthening section, transition temperature equalization section and tail slow cooling section through the distributed thermocouple array monitoring data; Transient coupling analysis module: The transient coupling analysis module is mainly used to train the thermal-structural coupling model using historical data to generate the ideal temperature-stress evolution curve of each segment; Real-time data acquisition module: The real-time data acquisition module is mainly used to realize the synchronous acquisition and mapping of temperature data through the distributed thermocouple array and the OPC UA protocol; Error calculation feedback module: The error calculation feedback module is mainly used to calculate the temperature-stress error using the stress solution model and the nonlinear alignment algorithm and feed it back to the control system; Dynamic closed-loop control module: The dynamic closed-loop control module is mainly used to dynamically adjust the induction heating parameters based on the error classification and skin depth formula to achieve multi-stage coordinated temperature control; Processor: The processor is mainly used for the calculation process of each formula and the construction calculation process of each model.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for optimizing the segmented heat treatment process of the drill bit based on temperature field simulation as described in any one of claims 1-7.

10. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, the method for optimizing the segmented heat treatment process of a drill bit based on temperature field simulation described in any one of claims 1 to 7 is implemented.

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