Foundation steel bar stress release heat treatment method

Abstract

The invention provides a foundation steel bar stress release heat treatment method, and belongs to the technical field of steel bar stress release. Constructing a segmented heating game optimization model taking energy consumption minimization as an upper-layer target and stress release efficiency maximization as a lower-layer target, determining terminal temperature parameters of three heating stages, and executing a segmented heating program by adopting a decreasing heating rate; the heat preservation time is calculated through a stress release kinetic equation, the change of residual stress is monitored in real time through an ultrasonic detector, heat preservation is ended when the residual stress release rate reaches 85%, the cooling process is optimized through a dynamic programming algorithm, and sectional cooling to the room temperature is achieved; and finally, the treatment effect is verified through ultrasonic detection, optimization treatment is automatically executed again when the treatment effect does not reach the standard, and the technical problem that in the prior art, foundation steel bar stress release heat treatment is low in efficiency is solved.

Description

Technical Field

[0001] This invention belongs to the field of steel reinforcement stress relief technology, and more specifically, relates to a method for stress relief heat treatment of foundation steel reinforcement. Background Technology

[0002] As the primary load-bearing material in building structures, reinforcing steel generates significant residual stress during its manufacturing processes, including rolling, welding, and forming. This residual stress affects the mechanical properties and service life of the steel. Traditional stress-relieving heat treatment techniques employ fixed heating rates and holding times, releasing residual stress through high-temperature annealing. In current engineering applications, conventional heat treatment processes rely primarily on empirical formulas to determine heating parameters, employing a single heating rate. Holding times are typically estimated based on material thickness and composition, and this method is widely used for residual stress relief in building reinforcing steel and prestressed steel. However, traditional heat treatment methods suffer from problems in practical applications, including a lack of precise control over the heating process, long processing times, and uneven stress release. In particular, they cannot be personalized for different initial stress states of the reinforcing steel, resulting in low processing efficiency. In other words, existing technologies suffer from low efficiency in stress-relieving heat treatment of foundation reinforcing steel. Summary of the Invention

[0003] In view of this, the present invention provides a method for stress relief heat treatment of foundation steel bars, which can solve the technical problem of low efficiency in the existing stress relief heat treatment of foundation steel bars.

[0004] This invention is implemented as follows: A method for stress-relieving heat treatment of basic reinforcing steel bars includes the following steps: Pre-treatment and testing of the reinforcing steel bars; measurement of the initial residual stress distribution within the reinforcing steel bars using ultrasonic non-destructive testing technology; recording the thickness of the oxide layer and material composition on the surface of the reinforcing steel bars; placing the reinforcing steel bars in an atmosphere-protected furnace, setting the furnace atmosphere to a nitrogen or argon protective atmosphere; constructing a segmented heating game optimization model for heating control, the segmented heating game optimization model including an upper-level model aiming to minimize energy consumption and a lower-level model aiming to maximize stress-relieving efficiency. The objective function of the upper-level model is the minimum product of heating power and time, and the objective function of the lower-level model is the maximum residual stress release per unit time. The two objective functions are correlated through a temperature gradient coupling term, and the end temperatures of the first heating stage, the second heating stage, and the initial temperature of the holding stage are obtained by solving the problem; executing the segmented heating program based on temperature parameters; calculating the holding time and analyzing the stress-relieving process using the stress-relieving kinetic equation, and performing holding treatment at the initial temperature of the holding stage; optimizing the cooling process using a dynamic programming algorithm; and performing final testing after cooling.

[0005] Specifically, the pretreatment and testing step involves measuring the initial residual stress distribution inside the steel bar using ultrasonic non-destructive testing technology, and recording the thickness and material composition of the oxide layer on the steel bar surface to obtain the initial residual stress value, oxide layer thickness value, and material composition data.

[0006] Specifically, the atmosphere protection furnace is set to a nitrogen or argon protective atmosphere, with the atmosphere pressure controlled within the range of 0.8 to 1.2 standard atmospheres, and the furnace temperature uniformity controlled within ±3℃.

[0007] The segmented heating game optimization model refers to a two-layer optimization model established using game theory to determine the key temperature nodes in the heating process. The upper-layer model is constrained by the heating power not exceeding the rated power of the furnace body, and the lower-layer model is constrained by the heating rate not exceeding the material's tolerance limit.

[0008] The endpoint temperature of the first heating stage refers to the target temperature value of the first heating stage calculated by the segmented heating game optimization model.

[0009] The endpoint temperature of the second heating stage refers to the target temperature value of the second heating stage calculated by the segmented heating game optimization model.

[0010] The starting temperature of the heat preservation stage refers to the temperature value at the beginning of the heat preservation process, calculated by the segmented heating game optimization model.

[0011] Specifically, the segmented heating program consists of the following steps: in the first heating stage, the temperature is increased at a rate of 15°C / min to the end temperature of the first heating stage and held for 30 minutes; in the second heating stage, the temperature is increased at a rate of 8°C / min to the end temperature of the second heating stage; and in the third heating stage, the temperature is increased at a rate of 5°C / min to the starting temperature of the holding stage.

[0012] The stress release kinetic equation describes the physical process of residual stress changing with time in steel reinforcement under high temperature conditions. The inputs include the initial temperature of the heat preservation stage, steel reinforcement material composition data, initial residual stress value, steel reinforcement geometry and heat preservation time. The outputs are the residual stress release rate and residual stress distribution state.

[0013] Specifically, the heat preservation process involves heat preservation at the initial temperature of the heat preservation stage, with the residual stress changes monitored in real time using an ultrasonic stress detector during the heat preservation process, and the heat preservation stage ending when the residual stress release rate reaches 85%.

[0014] The ultrasonic stress detector refers to a detection device that measures the changes in the internal stress state of steel bars in real time using ultrasonic sensors, and establishes a monitoring model based on the relationship between the propagation speed of ultrasonic waves in materials and the stress state.

[0015] The residual stress release rate refers to the percentage reduction in residual stress of the steel bar after heat treatment, and is calculated as the ratio of the difference in residual stress before and after treatment to the initial residual stress value.

[0016] Specifically, the dynamic programming algorithm optimizes the cooling process by treating the cooling process as a time-series decision problem, taking the avoidance of thermal stress as a constraint, minimizing the cooling time as the optimization objective, and performing segmented cooling treatment. In the early stage, the cooling is rapidly reduced to the critical cooling temperature at a rate of 20℃ / min, and in the later stage, the cooling is slowly reduced to room temperature at a rate of 3℃ / min.

[0017] The critical cooling temperature refers to the key temperature point at which new thermal stress is avoided during the cooling process, and is calculated using the stress release kinetic equation.

[0018] Specifically, the final testing step involves using ultrasonic testing technology to measure the final residual stress level of the steel bar, calculating the residual stress release rate, returning to the segmented heating game optimization model calculation and subsequent processing steps when the residual stress release rate is below 80%, and completing the heat treatment process when the residual stress release rate reaches above 80%.

[0019] Prior to the pretreatment and testing steps, the process includes cleaning the surface of the reinforcing bars to remove surface contaminants and loose oxide layers, ensuring the accuracy of ultrasonic testing and a clean environment within the atmosphere protection furnace.

[0020] This invention constructs a segmented heating game-theoretic optimization model with the dual objectives of minimizing energy consumption and maximizing stress release efficiency. It achieves coordinated optimization of the upper and lower model layers through a temperature gradient coupling term, determining the optimal end temperature of the heating stage and the starting temperature of the holding period, significantly improving the efficiency of stress release treatment. This method overcomes the shortcomings of traditional fixed heating rate methods, such as long processing time and uneven stress release. It achieves multi-objective optimization of the heating process through game theory algorithms, greatly shortening the processing cycle and improving the uniformity of stress release. Furthermore, this invention combines stress release kinetic equations for real-time monitoring and dynamic adjustment, and uses ultrasonic detection technology to accurately measure and control the residual stress state. The process is terminated promptly when the residual stress release rate reaches a preset value, avoiding time waste caused by over-processing and solving the technical problem of low efficiency in the stress release heat treatment of foundation steel reinforcement. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0023] like Figure 1 The diagram shown is a flowchart of a method for stress relief heat treatment of foundation steel bars provided by the present invention. This method includes the following steps:

[0024] S01. Pre-treatment and testing of the reinforcing bars: The initial residual stress distribution inside the reinforcing bars is measured by ultrasonic non-destructive testing technology, and the thickness and material composition of the oxide layer on the surface of the reinforcing bars are recorded to obtain the initial residual stress value, oxide layer thickness value and material composition data.

[0025] S02. Place the steel bars in the atmosphere-protected furnace, set the atmosphere inside the furnace to nitrogen or argon, control the atmosphere pressure within the range of 0.8 to 1.2 standard atmospheres, and control the temperature uniformity inside the furnace within ±3℃.

[0026] S03. Construct a segmented heating game optimization model for heating control. The segmented heating game optimization model includes an upper-level model with the goal of minimizing energy consumption and a lower-level model with the goal of maximizing stress release efficiency. The objective function of the upper-level model is the minimum product of heating power and time, and the objective function of the lower-level model is the maximum residual stress release per unit time. The two objective functions are correlated through a temperature gradient coupling term. Solve the equation to obtain the end temperature of the first heating stage, the end temperature of the second heating stage, and the starting temperature of the holding stage.

[0027] S04. Based on the temperature parameters obtained in step S03, execute a segmented heating program. In the first heating stage, the temperature is increased to the end temperature of the first heating stage at a rate of 15℃ / min and held for 30 minutes. In the second heating stage, the temperature is increased to the end temperature of the second heating stage at a rate of 8℃ / min. In the third heating stage, the temperature is increased to the start temperature of the holding stage at a rate of 5℃ / min.

[0028] S05. Calculate the heat preservation time and analyze the stress release process using the stress release kinetic equation. Perform heat preservation treatment at the initial temperature of the heat preservation stage. Monitor the changes in residual stress in real time using an ultrasonic stress detector during the heat preservation process. End the heat preservation stage when the residual stress release rate reaches 85%.

[0029] S06. The cooling process is optimized by using dynamic programming algorithm. The cooling process is regarded as a time series decision problem. The constraint is to avoid the generation of thermal stress. The optimization objective is to minimize the cooling time. The segmented cooling process is performed. In the early stage, the temperature is rapidly cooled to the critical cooling temperature at a rate of 20℃ / min. In the later stage, the temperature is slowly cooled to room temperature at a rate of 3℃ / min.

[0030] S07. After cooling is completed, a final test is performed. Ultrasonic testing technology is used to measure the final residual stress level of the steel bar and calculate the residual stress release rate. When the residual stress release rate is less than 80%, return to step S03 to re-execute the segmented heating game optimization model calculation and subsequent processing steps. When the residual stress release rate reaches more than 80%, the heat treatment process is completed.

[0031] The segmented heating game optimization model is a two-layer optimization model established using game theory to determine key temperature nodes during the heating process. The upper-layer model's constraint is that the heating power does not exceed the furnace's rated power, while the lower-layer model's constraint is that the heating rate does not exceed the material's tolerance limit. The endpoint temperature of the first heating stage refers to the target temperature value calculated by the segmented heating game optimization model for the first heating stage. The endpoint temperature of the second heating stage refers to the target temperature value calculated by the same model. The starting temperature of the heat preservation stage refers to the temperature value at the beginning of the heat preservation treatment, calculated by the same model. The stress release kinetic equation describes the physical process of residual stress changing with time in steel reinforcement under high-temperature conditions. Inputs include the starting temperature of the heat preservation stage, steel reinforcement material composition data, initial residual stress value, steel reinforcement geometry, and heat preservation time. Outputs are the residual stress release rate and residual stress distribution. The ultrasonic stress detector is a detection device that measures the changes in the internal stress state of steel reinforcement in real time using ultrasonic sensors. A monitoring model is established based on the relationship between the propagation speed of ultrasonic waves in the material and the stress state. The residual stress release rate refers to the percentage reduction in residual stress in the steel reinforcement after heat treatment, calculated as the ratio of the difference in residual stress before and after treatment to the initial residual stress value. The critical cooling temperature is the key temperature point at which new thermal stress is avoided during the cooling process, calculated using the stress release kinetic equation.

[0032] The specific implementation methods of the above steps are described in detail below.

[0033] The specific implementation of step S01 involves comprehensive pre-treatment testing and analysis of the reinforcing steel using ultrasonic non-destructive testing technology. First, an ultrasonic flaw detector is used to clean the surface of the reinforcing steel, removing surface oil and impurities to ensure testing accuracy. Then, an ultrasonic transducer with a frequency of 2.5MHz to 5MHz is used to perform dual-mode scanning of the reinforcing steel using both longitudinal and transverse waves, measuring the change in the propagation speed of ultrasonic waves within the steel. A stress distribution model is established based on the linear relationship between sound velocity and stress state. Next, eddy current testing technology is used to measure the thickness of the oxide layer on the surface of the reinforcing steel. The detection frequency is set to 100kHz, and the oxide layer thickness is calculated by measuring the change in the coupling coefficient between the eddy current sensor and the steel surface, with a reference threshold range of 5μm to 50μm. Simultaneously, X-ray fluorescence spectrometry is used to quantitatively analyze the material composition of the reinforcing steel, focusing on the content of major elements such as carbon, silicon, manganese, phosphorus, and sulfur, establishing a material composition database. Finally, the initial residual stress value, oxide layer thickness value, and material composition data are entered into the system database, providing basic parameters for subsequent heat treatment process optimization. The purpose of this step is to obtain initial state information of the reinforcing steel, ensuring the targetedness and effectiveness of the heat treatment process.

[0034] The specific implementation of step S02 involves establishing a stable protective atmosphere within the atmosphere-protected furnace. First, the furnace body is evacuated to reduce the internal pressure to below 1 Pa, eliminating oxygen and moisture from the air. Then, high-purity nitrogen or argon gas is introduced into the furnace via a mass flow controller, with a purity requirement of 99.99% or higher. The furnace atmosphere pressure is monitored in real-time by a pressure sensor, precisely controlled within the range of 0.8 to 1.2 standard atmospheres. Next, the furnace heating and temperature control systems are activated, employing a multi-point temperature measurement scheme. At least six thermocouple sensors are placed at different locations within the furnace, and a proportional-integral-derivative (PID) control algorithm is used to adjust the heating power distribution, ensuring that the furnace temperature uniformity is controlled within ±3℃. Simultaneously, an atmosphere circulation system is configured, using a fan to drive the protective gas to circulate within the furnace at a velocity controlled between 0.5 m / s and 1.2 m / s, ensuring sufficient contact between the steel reinforcement surface and the protective atmosphere. The purpose of this step is to create an oxidation-free high-temperature treatment environment, preventing oxidation of the steel reinforcement during heating and ensuring the quality of the heat treatment.

[0035] The specific implementation of step S03 involves constructing a game theory-based two-layer optimization model to determine the key temperature parameters of the heating process. First, the objective function of the upper-layer model is established, with the optimization objective being to minimize the product of heating power and time, constrained by the heating power not exceeding 80% of the furnace's rated power. The optimal power allocation strategy is solved using the Lagrange multiplier method. Then, the objective function of the lower-layer model is constructed, with the optimization objective being to maximize the release of residual stress per unit time, constrained by the heating rate not exceeding the material's tolerance limit of 15℃ / min. The optimal heating rate is found using a gradient descent algorithm. Next, a temperature gradient coupling relationship is established between the two objective functions, using the temperature gradient as a connecting variable to achieve information exchange between the upper and lower-layer models. An iterative algorithm is used to solve the model. The initial temperature is set to room temperature (25℃), the reference range for the final temperature of the first heating stage is 200℃ to 350℃, the reference range for the final temperature of the second heating stage is 450℃ to 600℃, and the reference range for the initial temperature of the holding stage is 650℃ to 800℃. Finally, the Nash equilibrium solution is obtained using game theory, yielding the temperature parameter combination that simultaneously achieves the relative optimality of both objective functions. The purpose of this step is to determine the optimal heating temperature node through mathematical optimization methods, achieving a harmonious balance between minimizing energy consumption and maximizing stress release efficiency.

[0036] The specific implementation of step S04 involves executing a precise segmented heating program based on the calculation results of the game optimization model. First, the first heating stage is initiated, with the heating rate precisely controlled at 15℃ / min using a proportional-integral-derivative (PID) temperature controller. Real-time monitoring of furnace temperature changes is maintained. When the temperature reaches the endpoint of the first heating stage, the system automatically switches to a constant-temperature control mode, holding the temperature for 30 minutes to ensure a more uniform temperature distribution within the steel reinforcement. Then, the second heating stage is executed, adjusting the temperature controller parameters to reduce the heating rate to 8℃ / min. This slower heating method reduces thermal stress. When the temperature reaches the endpoint of the second heating stage, the third heating stage begins. Next, the third heating stage is implemented, further reducing the heating rate to 5℃ / min. More precise temperature control prevents excessive temperature gradients within the steel reinforcement. The heating process ends when the temperature reaches the starting temperature of the holding stage. Throughout the heating process, a feedforward and feedback composite control strategy is employed. Temperature data is collected in real-time using thermocouple sensors, and the controller automatically adjusts the heating power output based on temperature deviations. The purpose of this step is to create optimal temperature conditions for subsequent stress relief treatment by using a phased heating control strategy to ensure heating efficiency while avoiding thermal shock damage to the steel bars.

[0037] The specific implementation of step S05 is based on the stress release kinetic equation to accurately calculate the heat preservation time and monitor the stress release process in real time. First, a stress release kinetic model is established, with input parameters including the initial temperature of the heat preservation stage, the carbon equivalent value from the steel reinforcement material composition data, the initial residual stress value, and geometric parameters such as the diameter and length of the steel reinforcement. Then, the Arrhenius equation is used to describe the relationship between temperature and stress release rate, and an exponential function model is used to predict the residual stress release rate under different heat preservation times, with a reference heat preservation time range of 30 to 180 minutes. Next, an ultrasonic stress detector is configured for real-time monitoring, using the pulse-echo method to measure the change in ultrasonic wave propagation time in the steel reinforcement, and establishing a quantitative relationship between ultrasonic wave propagation parameters and stress state through the acoustoelastic effect. Then, the detection frequency is set to 10MHz, and stress measurement is performed every 5 minutes, recording the time change curve of residual stress through a data acquisition system. When the residual stress release rate reaches 85%, an automatic heat preservation end signal is issued, and the control system switches to the cooling program. The purpose of this step is to ensure that stress release achieves the expected effect through a combination of theoretical calculation and real-time monitoring, avoiding material performance degradation caused by overheating.

[0038] The specific implementation of step S06 involves using a dynamic programming algorithm for global optimization control of the cooling process. First, the cooling process is modeled as a multi-stage decision problem, with each time period as a decision stage, the cooling rate as the decision variable, and the avoidance of thermal stress as the state constraint. A dynamic programming recursive equation is established with the shortest cooling time as the optimization objective. Then, the temperature field distribution and thermal stress distribution of the reinforcing steel at different cooling rates are calculated using finite element analysis, determining the critical cooling temperature to be between 300℃ and 400℃. Next, a segmented cooling strategy is designed. In the initial stage, forced air cooling is used at a rate of 20℃ / min for rapid cooling, with high-speed fans blowing cooling gas onto the surface of the reinforcing steel. When the temperature drops to the critical cooling temperature, the system switches to natural cooling mode. Then, a slow cooling stage is implemented, shutting down the forced cooling system and adjusting the furnace atmosphere flow rate to slowly cool to room temperature at a rate of 3℃ / min, avoiding excessively rapid cooling that could lead to new residual stress. During the cooling process, multi-point temperature monitoring ensures that the temperature difference between different parts of the reinforcing steel does not exceed 50℃, and a fuzzy control algorithm is used to dynamically adjust the cooling parameters based on temperature feedback information. The purpose of this step is to design the optimal cooling path through an optimized algorithm, ensuring cooling efficiency while avoiding the generation of new thermal stress during the cooling process, thus ensuring the stability of the heat treatment effect.

[0039] The specific implementation of step S07 involves a comprehensive quality inspection and effect evaluation of the cooled steel bars. First, after the steel bars have completely cooled to room temperature, the residual stress distribution inside the steel bars is remeasured using the same ultrasonic testing technology as in step S01. The residual stress release rate is calculated by comparing the stress data before and after treatment. Then, a stress release rate calculation model is established, using the ratio of the difference in residual stress before and after treatment to the initial residual stress value as the evaluation criterion. When the calculation result shows that the residual stress release rate is less than 80%, the heat treatment effect is deemed unqualified. Next, the reprocessing procedure is initiated. The system automatically returns to step S03 to re-execute the calculation of the segmented heating game optimization model, adjusts the optimization parameters according to the current residual stress state, calculates new temperature node parameters, and then sequentially executes the subsequent heating, holding, and cooling treatment steps. When the residual stress release rate reaches 80% or more, the heat treatment process is deemed successfully completed. Laser marking technology is used to mark the treatment batch and quality grade information on the surface of the steel bars. Finally, a quality traceability file is established to record the process parameters, test data, and quality results of the entire heat treatment process, providing data support for subsequent quality control and process improvement. The purpose of this step is to ensure that the heat treatment effect meets the technical requirements through rigorous quality testing, and to establish a sound quality assurance system and traceability mechanism.

[0040] It should be noted that the embodiments of this invention employ a two-layer optimization model based on game theory to guide the temperature rise process control. Traditional heat treatment methods typically use fixed temperature rise curves or empirical parameters, failing to achieve an optimal balance between energy consumption control and stress release efficiency. This game theory optimization model constructs an upper-level model aiming to minimize energy consumption and a lower-level model aiming to maximize stress release efficiency, organically linking the two seemingly contradictory objective functions through a temperature gradient coupling term, thus achieving multi-objective collaborative optimization. This method can dynamically determine the optimal temperature node based on the specific material properties and initial stress state of the reinforcing steel, avoiding the problems of thermal stress concentration caused by excessively rapid heating or energy waste caused by excessively slow heating in traditional methods.

[0041] This invention employs an ultrasonic stress detector to monitor residual stress changes in real time, and combines this with stress release kinetic equations for process analysis and control. This represents a significant technological advancement compared to traditional static heat treatment processes. Traditional methods often rely on preset time parameters for blind heat preservation, failing to accurately grasp the actual stress release process. This technology, by establishing a monitoring model based on the relationship between ultrasonic wave propagation speed and stress state, can acquire real-time information on the dynamic changes in stress distribution within the reinforcing steel, and accurately determine the termination time of the heat preservation process based on the calculation results of the stress release kinetic equations. This closed-loop control mechanism ensures the accuracy and controllability of the heat treatment process, avoiding over-treatment or under-treatment.

[0042] The cooling stage employs dynamic programming for process optimization, transforming the cooling process into a time-series decision problem—a significant improvement over traditional, simple cooling methods. Traditional cooling methods typically use natural cooling or forced cooling at a single rate, which can easily generate new thermal stresses during temperature gradient changes, affecting the heat treatment effect. This technology, through dynamic programming, uses the avoidance of thermal stress as a constraint and the minimization of cooling time as the optimization objective to achieve segmented intelligent cooling control. Rapid cooling in the early stage effectively solidifies the released stress state, while slow cooling in the later stage avoids the re-accumulation of stress caused by sudden temperature changes, ensuring the stability and durability of the heat treatment effect.

[0043] The three key technological approaches described above form a complete intelligent heat treatment control system, and their synergistic effect generates significant technological advantages. The segmented heating game optimization model provides a scientific temperature control strategy for the entire heat treatment process; real-time monitoring and dynamic regulation technology ensures the accuracy and adaptability of process execution; and dynamic planning cooling optimization technology guarantees the stability of the treatment effect. These three technologies work together to form a closed-loop intelligent optimization control system covering the entire process from heating and holding to cooling. Compared to traditional empirical heat treatment methods, this collaborative technology system can adaptively adjust treatment parameters according to the specific characteristics and conditions of different reinforcing bars, achieving personalized and precise heat treatment. Through multi-level optimization algorithms and real-time feedback mechanisms, the entire system not only improves the efficiency and quality of stress release but also significantly reduces energy consumption and processing time, representing an important direction for the intelligent and precise development of reinforcing bar heat treatment technology.

[0044] Specifically, the principle of this invention is as follows: The technical solution of this invention can solve the technical problem of low efficiency in the stress release heat treatment of foundation steel bars. Its core lies in realizing intelligent control of the treatment process through a two-layer optimization model established by game theory. The upper-layer model aims to minimize energy consumption, while the lower-layer model aims to maximize stress release efficiency. The two objective functions are linked through a temperature gradient coupling term to ensure that the best stress release effect is achieved in the shortest time. The segmented heating strategy adopts a decreasing heating rate based on the stress release characteristics of steel bars in different temperature ranges. In the low-temperature stage, a faster heating rate is used to quickly reach the effective stress release temperature range, while in the high-temperature stage, a slower heating rate is used to ensure the sufficiency and uniformity of stress release, avoiding the time waste caused by the traditional single heating rate. The introduction of the stress release kinetic equation makes the determination of the holding time more accurate. By monitoring the changes in residual stress in real time, the holding stage is ended in time when the release rate reaches 85%, which significantly shortens the treatment cycle compared to the traditional empirical holding time. The cooling process optimized by the dynamic programming algorithm adopts a segmented cooling strategy. Rapid cooling in the early stage shortens the overall processing time, while slow cooling in the later stage avoids the generation of new thermal stress, ensuring processing effectiveness while improving overall efficiency. The entire process achieves closed-loop control through ultrasonic detection technology. Pre-process detection determines initial parameters, process monitoring enables dynamic adjustment, and final detection verifies the processing effect. When requirements are not met, the optimization process is automatically re-executed, ensuring a balance between high efficiency and high quality.

[0045] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0046] The specific implementation of step S01 involves establishing a mathematical model of stress distribution using ultrasonic non-destructive testing technology. The relationship between the propagation speed of ultrasonic waves inside the steel reinforcement and the residual stress state is expressed as:

[0047] v = v0 + K σ σ+K T (T-T0)+ε1;

[0048] In the formula, v is the actual propagation speed of ultrasound in steel reinforcement, in m / s; v0 is the reference propagation speed of ultrasound in steel reinforcement under stress-free conditions, taken as 5900 m / s; K σ The acoustic elastic modulus has a value range of 4.5 × 10⁻⁶. -6 ~6.2×10 -6 Pa -1 σ represents the residual stress inside the reinforcing steel bar, in Pa; K Tε is the temperature coefficient, taken as 1.2 m / (s·℃); T is the current measurement temperature, in ℃; T0 is the reference temperature of 25℃; ε1 is the measurement error term, ranging from ±0.05 m / s. The eddy current calculation formula for the oxide layer thickness on the surface of the reinforcing steel is:

[0049] In the formula, d ox μ is the oxide layer thickness in μm; μ0 is the free magnetic permeability (4π × 10⁻⁶). -7 H / m; ω is the angular frequency of the eddy current detection, with a value of 2π×100×10. 3 rad / s; σ ox The conductivity of the oxide layer is 10. 4 ~10 6 S / m; Z1 is the impedance value without oxide layer, in Ω; Z2 is the impedance value with oxide layer, in Ω; ε2 is the eddy current detection error term, ranging from ±2μm. The parameter acquisition method is as follows: v0 is obtained using standard sample calibration, including step 1: selecting a stress-free standard steel bar sample; step 2: measuring the ultrasonic propagation velocity 10 times under a constant temperature environment of 25℃ and taking the average value. K σ The results were obtained through a loading test, including step 1: applying a known stress load to a standard specimen; and step 2: measuring the change in ultrasonic velocity under different stresses and performing linear fitting.

[0050] The specific implementation of step S02 involves establishing a precise control model for the pressure and temperature inside the atmosphere-protected furnace. The furnace atmosphere pressure control equation is as follows:

[0051]

[0052] In the formula, P(t) is the actual pressure inside the furnace at time t, in Pa; P set The pressure value is set within the range of 80,000 to 120,000 Pa; P meas (t) represents the pressure value measured at time t; K p K is the proportional control coefficient, with a value of 0.8; i This is the integral control coefficient, with a value of 0.02s. -1 ε3 represents the pressure control error term, ranging from ±500 Pa. Multi-point temperature feedback control is used for furnace temperature uniformity control, and the temperature distribution function is:

[0053]

[0054] In the formula, T avg The average temperature inside the furnace; n is the number of temperature measuring points, with a value of 6; T i ε is the temperature value at the i-th measuring point; ε4 is the temperature measurement error term, with a range of ±1℃.

[0055] The specific implementation of step S03 involves constructing a two-level optimization mathematical model based on game theory. The upper-level model aims to minimize energy consumption, and its objective function is expressed as:

[0056]

[0057] In the formula, F1 is the objective function value of the upper-level model; P h (t) represents the heating power at time t, in kW; t total ΔT represents the total heating time; λ1 is the temperature gradient penalty coefficient, with a value of 0.05; ΔT j Let εj represent the temperature gradient at stage j; ε5 represent the error term of the upper-level model, ranging from ±0.1. The lower-level model aims to maximize stress release efficiency, and its objective function is expressed as:

[0058]

[0059] In the formula, F2 is the objective function value of the lower-level model; v h This is the heating rate, expressed in °C / min; λ is the stress release rate at time t; λ2 is the heating rate penalty coefficient, with a value of 0.02; v h，j Let ε be the heating rate in stage j; ε6 is the error term of the lower-level model, ranging from ±0.05. The two objective functions are related through a temperature gradient coupling term, and the coupling relationship is expressed as:

[0060]

[0061] In the formula, G couple ε is the coupling function; α is the first-order coupling coefficient with a value of 0.6; β is the second-order coupling coefficient with a value of 0.3; ε7 is the coupling error term with a range of ±0.02.

[0062] The specific implementation of step S04 is the same as described above, and will not be repeated in detail here.

[0063] The specific implementation of step S05 is based on mathematical modeling of the heat preservation process using the stress release kinetic equation. The stress release kinetic equation is expressed as:

[0064]

[0065] In the formula, σ res (t) represents the residual stress value at time t, in MPa; A arr This is the Arrhenius frequency factor, with a value range of 10. 8 ~10 12 s -1 Q actThe stress release activation energy ranges from 150 to 250 kJ / mol; R is the gas constant, 8.314 J / (mol·K); T hold The insulation temperature is expressed in Kelvin (K); σ max εa represents the maximum releasable stress value; m is the stress exponent, ranging from 0.8 to 1.2; n is the release mechanism exponent, ranging from 1.5 to 2.5; ε8 is the error term in the kinetic equation, ranging from ±0.5 MPa. The real-time calculation formula for the residual stress release rate is:

[0066]

[0067] In the formula, η rel (t) represents the residual stress release rate at time t; σ ini ε is the initial residual stress value; ε9 is the error term for the release rate calculation, ranging from ±1%. The parameter acquisition method is as follows: A arr The frequency factor (Q) was obtained through high-temperature tensile testing, including step 1: preparing a standard tensile specimen; step 2: conducting stress relaxation tests at different temperatures; and step 3: obtaining the frequency factor by fitting the Arrhenius equation. act The stress release test was conducted using a variable temperature method, which included the following steps: Step 1: Stress release test at multiple temperature points; Step 2: Plotting the relationship curve between lnk and 1 / T; Step 3: Calculating the activation energy using the slope of the straight line.

[0068] The specific implementation of step S06 involves using a dynamic programming algorithm to establish an optimized mathematical model of the cooling process. The dynamic programming state transition equation is expressed as:

[0069]

[0070] In the formula, V k (T k The temperature of stage k is T. k The optimal value function at time; v c C(T) represents the cooling rate, expressed in °C / min. k v c f(T) is the cost function for the k-th stage; k v c ) is the state transition function, expressed as f(T) k v c ) = T k -v c Δt k ;ε 10 This represents the dynamic programming error term, ranging from ±0.1. The cost function is defined as the weighted sum of cooling time and thermal stress risk:

[0071] C(T k v c)=w1Δt k +w2σ thermal (T k v c )+ε 11 ;

[0072] In the formula, w1 is the time weighting coefficient, with a value of 0.7; Δt k σ represents the cooling time for stage k, in minutes; w2 is the thermal stress weighting coefficient, with a value of 0.3; thermal (T k v c ) is the thermal stress function; ε 11 This represents the error term of the cost function, ranging from ±0.05. The formula for calculating thermal stress is:

[0073] σ thermal =Eα th ΔT grad (1-ν) -1 +ε 12 ;

[0074] In the formula, E is the elastic modulus of the steel reinforcement, with a value of 200 GPa; α th The coefficient of thermal expansion is 1.2 × 10⁻⁶. -5 / ℃;ΔT grad ν represents the temperature gradient; ν is Poisson's ratio, with a value of 0.3; ε 12 This represents the error term in thermal stress calculations, ranging from ±5 MPa. The formula for calculating the critical cooling temperature is:

[0075]

[0076] In the formula, T crit σ is the critical cooling temperature, expressed in °C. yield γ represents the yield strength of the steel reinforcement, taken as 400 MPa; cool ε is the cooling gradient coefficient, with a value of 1.5; 16 This is the error term for the critical temperature calculation, with a range of ±5℃.

[0077] The specific implementation of step S07 involves establishing a quantitative evaluation model for the residual stress release rate. The final formula for calculating the residual stress release rate is:

[0078]

[0079] In the formula, η final σ represents the final residual stress release rate. final The final residual stress value after heat treatment is expressed in MPa; ε 13 The final evaluation error term has a range of ±1%. The quality judgment function is expressed as follows:

[0080]

[0081] In the formula, Q judge For quality assessment results, 1 indicates pass, and 0 indicates fail; ε 14 The error term is defined, with a range of ±0.01. When the judgment result is unqualified, the parameter adjustment formula for reprocessing is:

[0082] T new，j =T old，j +K adj (80%-η) final )+ε 15 ;

[0083] In the formula, T new，j The adjusted target temperature for stage j; T old，j The target temperature for the j-th stage of the previous processing; K adj The adjustment coefficient is ε, with a value of 5℃ / %. 15 An error term is added to the parameter adjustment, with a range of ±2℃. The parameter is obtained as follows: σ final The method of obtaining the data using ultrasonic testing includes the following steps: Step 1: After the steel bar is cooled to room temperature, it is left to stand for 2 hours; Step 2: Ultrasonic testing is performed using the same method as in step S01; Step 3: The average residual stress value after processing is calculated.

[0084] It should be noted that in this embodiment, the formula for the propagation speed of ultrasound is v = v0 + K. σ σ+K T The principle of (T-T0)+ε1 is based on the acoustoelastic effect, that is, there is a linear relationship between the propagation speed of ultrasound in a material and the internal stress state of the material. This formula, by establishing a quantitative mathematical model of sound velocity and stress, achieves non-destructive and accurate measurement of residual stress inside steel bars. Compared with traditional destructive stress testing methods, this formula enables real-time online monitoring of the stress state of steel bars, avoiding damage to the steel structure, and also utilizes the temperature compensation term K. T (T-T0) eliminates the influence of ambient temperature on measurement accuracy, improving stress detection accuracy to the MPa level and providing a reliable data basis for the accurate formulation of subsequent heat treatment process parameters.

[0085] Oxide layer thickness calculation formula Based on the eddy current detection principle, a logarithmic relationship model between thickness and impedance change is established by measuring the attenuation characteristics of the electromagnetic field in the oxide layer. The advantage of this formula is that it enables precise quantitative measurement of the oxide layer thickness on the steel rebar surface. Compared to traditional visual estimation or simple thickness gauge methods, this mathematical model achieves measurement accuracy down to the micrometer level, ensuring accurate assessment of the oxide layer state during heat treatment. This avoids deviations in heat treatment process parameters caused by inaccurate oxide layer thickness estimation, thereby guaranteeing the consistency and repeatability of stress release effects.

[0086] Pressure control equation By employing proportional-integral control theory and real-time feedback adjustment, precise control of the furnace atmosphere pressure is achieved. Compared to traditional open-loop control, this control model effectively manages furnace pressure fluctuations within ±500Pa, preventing steel bar oxidation caused by atmosphere protection failure. This ensures the entire heat treatment process is conducted in a stable protective atmosphere, significantly improving the stability and controllability of heat treatment quality.

[0087] Upper-level objective function in a two-level optimization model in game theory and lower-level objective function A coordinated optimization framework for minimizing energy consumption and maximizing stress release efficiency was constructed. Compared with traditional single-objective optimization methods, this two-level game model achieves this through coupling functions. The solution achieves a balance between two mutually constraining objectives, ensuring both high efficiency in stress release and economic efficiency in energy consumption. This allows the heat treatment process to achieve the expected stress release effect while saving more than 70% of the energy required by traditional methods.

[0088] Stress release kinetic equation Based on the Arrhenius theory and the mechanism of plastic deformation of materials, a differential equation model of residual stress changing with time and temperature was established. The advantage of this kinetic equation is its ability to accurately predict the stress release process under different temperature and time conditions. Compared to traditional empirical methods for determining holding time, this mathematical model enables scientific calculation and real-time adjustment of holding time, avoiding material performance degradation due to overheating or incomplete stress release due to underheating. This allows for stress release rate control accuracy within ±2%.

[0089] Dynamic Programming Cooling Optimization Model By modeling the cooling process as a multi-stage decision problem, and combining it with the cost function C(T) k v c )=w1Δt k +w2σ thermal (T k v c )+ε11 and thermal stress calculation model σ thermal =Eα th ΔT grad (1-v) -1 +ε 12 This model achieves global optimization of the cooling path. Compared to the traditional fixed cooling rate method, this optimization model can dynamically adjust the cooling strategy according to the real-time temperature state of the steel reinforcement, ensuring cooling efficiency while keeping thermal stress to a minimum, effectively preventing the generation of new residual stress during the cooling process, and significantly improving the stability and consistency of the overall heat treatment effect.

[0090] To better understand and implement this invention, Example 2 of a specific application scenario is provided below: A construction team undertook the task of stress relief treatment for the reinforced concrete foundation of a key node in a 750kV ultra-high voltage transmission line project. Located in a mountainous area with complex geological conditions, the foundation reinforcement was subjected to enormous mechanical and environmental stresses. Traditional reinforcement accumulated significant residual stress after rolling, transportation, and storage, severely affecting its fatigue performance and long-term stability. The construction team decided to use a stress relief heat treatment method for HRB400 grade reinforcement with batch number GTJ-750-2025. The reinforcement had a diameter of 32mm, a length of 12m, and consisted of 216 pieces.

[0091] The construction team first executed the pre-treatment and testing procedure in step S01. Representative steel reinforcement samples were selected for ultrasonic non-destructive testing, using a 3.5MHz ultrasonic transducer to perform dual-mode scanning of the steel reinforcement in both longitudinal and transverse waves. During the testing process, the construction team used the acoustoelastic coefficient K... σ 5.2×10 -6 Pa -1 The reference propagation speed v0 is 5900 m / s, and the temperature coefficient K is... T The initial residual stress distribution inside the steel bar was calculated by measuring the ultrasonic propagation velocity, which was 1.2 m / (s·℃). Simultaneously, eddy current testing technology was used to measure the oxide layer thickness on the steel bar surface. The detection frequency was set to 100 kHz, and the oxide layer thickness was calculated by the change in the coupling coefficient between the eddy current sensor and the steel bar surface. X-ray fluorescence spectroscopy was used to quantitatively analyze the steel bar material composition, focusing on the content of major elements such as C, Si, Mn, P, and S. The test results are shown in Table 1.

[0092] Table 1. Inspection Results of Reinforcing Steel Pretreatment

[0093] Testing items Measured values Standard range Detection status Initial residual stress 285MPa <350MPa qualified Oxide layer thickness 28μm 5~50μm qualified Carbon content 0.22％ 0.20～0.25％ qualified Silicon content 0.35％ 0.20～0.80％ qualified manganese content 1.45％ 1.20～1.60％ qualified Phosphorus content 0.025％ ≤0.045％ qualified Sulfur content 0.018％ ≤0.045％ qualified

[0094] Test data shows that the initial residual stress level of the reinforcing steel is high, reaching 285 MPa, exceeding the recommended value of 220 MPa for safe use, requiring stress relief heat treatment. The oxide layer thickness of 28 μm is within the normal range, and the material composition meets the technical requirements of HRB400 grade reinforcing steel.

[0095] Next, the construction team executed step S02, the atmosphere protection furnace setup procedure. An 8m³ / h furnace was selected. 3 The vacuum atmosphere protection furnace first undergoes a vacuum process, reducing the internal pressure to 0.8 Pa. Then, high-purity N2 (99.99%) is introduced into the furnace via a mass flow controller. The furnace atmosphere pressure is monitored in real-time by a pressure sensor and precisely controlled at 0.95 atmospheres. The furnace heating and temperature control systems are activated. Six K-type thermocouple sensors are positioned at different locations within the furnace, and a proportional-integral-derivative (PID) control algorithm is used to adjust the heating power distribution, ensuring that the furnace temperature uniformity is controlled within ±2℃. An atmosphere circulation system is configured, using a variable frequency fan to drive the protective gas to circulate within the furnace at a velocity controlled at 0.8 m / s, ensuring sufficient contact between the steel reinforcement surface and the protective atmosphere. The furnace environmental parameters are shown in Table 2.

[0096] Table 2 Environmental Control Parameters for Atmosphere Protection Furnace

[0097] Control parameters Setting value actual value Control accuracy Control status Atmospheric pressure 0.95 atm 0.948 atm ±0.01atm Stablize Atmosphere purity 99.99％ 99.98％ ±0.01％ qualified Temperature uniformity ±3℃ ±1.8℃ ±0.5℃ excellent airflow speed 0.8m / s 0.82m / s ±0.1m / s Stablize vacuum degree <1Pa 0.8Pa ±0.2Pa qualified

[0098] The environmental control of the atmosphere protection furnace met the expected requirements, providing an ideal oxidation-free environment for subsequent heat treatment processes.

[0099] The construction team then proceeded to step S03, constructing a segmented heating game-theoretic optimization model. An upper-level model was established with the goal of minimizing energy consumption, setting the temperature gradient penalty coefficient λ1 to 0.05. The lower-level model aimed to maximize stress release efficiency, setting the heating rate penalty coefficient λ2 to 0.02. The two objective functions were linked through a temperature gradient coupling term, with a first-order coupling coefficient α of 0.6 and a second-order coupling coefficient β of 0.3. The initial temperature was set at 25℃, the reference range for the final temperature of the first heating stage was 200–350℃, the reference range for the final temperature of the second heating stage was 450–600℃, and the reference range for the initial temperature of the holding stage was 650–800℃. The Nash equilibrium solution was obtained using game theory, yielding the temperature parameter combination that simultaneously achieves the relatively optimal balance between the two objective functions. The calculation results of the game-theoretic optimization model are shown in Table 3.

[0100] Table 3. Calculation Results of the Segmented Heating Game Optimization Model

[0101] Optimize parameters Calculation results Constraints Optimization effect The final temperature of the first heating stage 320℃ 200～350℃ Energy consumption reduced by 12% The final temperature of the second heating stage 520℃ 450～600℃ Efficiency increased by 8% Insulation stage start temperature 720℃ 650～800℃ Overall Optimal upper-level model objective function value 245.6 <300 Satisfying constraints Lower-level model objective function value 0.82 >0.7 Satisfying constraints Game equilibrium convergence times 15 times <20 times Fast convergence

[0102] The game-theoretic optimization model successfully converged, yielding the optimal combination of temperature parameters: the final temperature of the first heating stage was 320℃, the final temperature of the second heating stage was 520℃, and the starting temperature of the heat preservation stage was 720℃.

[0103] Next, the construction team executed the segmented heating procedure in step S04. The first heating stage was initiated, with the heating rate precisely controlled at 15℃ / min using a proportional-integral-derivative (PID) temperature controller. Temperature changes at six measuring points inside the furnace were monitored in real time. When the average temperature reached 320℃, the system automatically switched to a constant temperature control mode, maintaining the temperature for 30 minutes to ensure a more uniform temperature distribution within the steel reinforcement. The second heating stage was then executed, adjusting the temperature controller parameters to reduce the heating rate to 8℃ / min. When the temperature reached 520℃, the third heating stage commenced. In the third heating stage, the heating rate was further reduced to 5℃ / min, and the heating process ended when the temperature reached 720℃. The entire heating process employed a feedforward and feedback composite control strategy. Temperature data was collected in real time using thermocouple sensors, and the controller automatically adjusted the heating power output based on the temperature deviation. The control parameters for the segmented heating process are shown in Table 4.

[0104] Table 4 Control Parameters for Segmented Heating Process

[0105]

[0106] The temperature control accuracy of the segmented heating process meets the process requirements, and the temperature deviation at each stage is within the allowable range, creating the best temperature conditions for subsequent stress relief treatment.

[0107] The construction team then performed step S05, monitoring stress release during the insulation process. A stress release dynamics model was established, and the Arrhenius frequency factor A was set. arr 8.5×10 10 s -1 Stress release activation energy Q act The gas concentration is 195 kJ / mol, the gas constant R is 8.314 J / (mol·K), and the holding temperature T is... hold The stress level is 993K. The stress exponent m is 1.0, the release mechanism exponent n is 2.0, and the maximum releaseable stress value σ is... max The stress was set at 300 MPa. An ultrasonic stress detector was used for real-time monitoring. The pulse-echo method was employed to measure the propagation time of ultrasonic waves in the steel reinforcement. The detection frequency was set to 10 MHz, and stress measurements were taken every 5 minutes. The residual stress time-varying curve was recorded through a data acquisition system. When the residual stress release rate reached 85%, an automatic end-of-insulation signal was issued. The stress release monitoring data during the insulation process are shown in Table 5.

[0108] Table 5. Data on stress release monitoring during the thermal insulation process

[0109] Insulation time Residual stress value Stress relief rate Release rate Monitoring status 0min 285MPa 0％ 0MPa / min benchmark value 30min 248MPa 13％ 1.23 MPa / min normal 60min 198MPa 31％ 1.67 MPa / min normal 90min 156MPa 45％ 1.40 MPa / min normal 120min 125MPa 56％ 1.03 MPa / min normal 150min 102MPa 64％ 0.77 MPa / min normal 180min 85MPa 70％ 0.57 MPa / min normal 210min 72MPa 75％ 0.43 MPa / min normal 240min 62MPa 78％ 0.33 MPa / min normal 270min 54MPa 81％ 0.27 MPa / min normal 300min 48MPa 83％ 0.20 MPa / min normal 330min 43MPa 85％ 0.15MPa / min Meets standards

[0110] After 330 minutes of heat preservation, the residual stress release rate reached 85%, decreasing from the initial 285 MPa to 43 MPa, demonstrating a significant stress release effect.

[0111] The construction team continued to optimize the cooling process using the dynamic programming algorithm in step S06. The cooling process was modeled as a multi-stage decision problem, with the time weighting coefficient w1 set to 0.7, the thermal stress weighting coefficient w2 set to 0.3, the steel reinforcement elastic modulus E set to 200 GPa, and the thermal expansion coefficient α... th 1.2×10 -5 / ℃, Poisson's ratio ν is 0.3. The critical cooling temperature was calculated to be 350℃ using finite element analysis. A segmented cooling strategy was designed, initially using forced air cooling at a rate of 20℃ / min to rapidly cool to 350℃, then switching to natural cooling mode at a rate of 3℃ / min to slowly cool to 25℃. During the cooling process, multi-point temperature monitoring was used to ensure that the temperature difference between different parts of the steel bar did not exceed 50℃, and a fuzzy control algorithm was used to dynamically adjust the cooling parameters based on temperature feedback information. The control parameters for the cooling process are shown in Table 6.

[0112] Table 6 Cooling Process Control Parameters

[0113]

[0114]

[0115] Temperature control during the cooling process meets process requirements, avoids generating new thermal stress during cooling, and ensures the stability of heat treatment effect.

[0116] Finally, the construction team performed the final inspection and quality assessment in step S07. After the steel bars had completely cooled to room temperature, the residual stress distribution inside the steel bars was remeasured using the same ultrasonic testing technology as in step S01. The residual stress release rate was calculated by comparing the stress data before and after treatment, and a calculation model for the stress release rate was established. When the calculation results showed that the residual stress release rate was 83%, the heat treatment effect was deemed qualified. Laser marking technology was used to mark the batch number GTJ-750-2025 and quality grade A information on the surface of the steel bars. A quality traceability file was established to record the process parameters, test data, and quality results of the entire heat treatment process. The final test results are shown in Table 7:

[0117] Table 7 Comparison of Final Detection Results

[0118] Testing items Values ​​before processing Processed values degree of improvement Quality rating Residual stress 285MPa 48MPa 83% release Grade A Yield strength 420MPa 425MPa 1.2% increase Stablize tensile strength 580MPa 582MPa 0.3% increase Stablize elongation 16.5％ 17.2％ 4.2% increase improve Fatigue life <![CDATA[2.8×10 5 Next <![CDATA[3.4×10 5 Next 21% increase Significant

[0119] After heat treatment, the residual stress of the steel bars decreased from 285 MPa to 48 MPa, with a residual stress release rate of 83%, exceeding the technical requirement of 80%. Simultaneously, the mechanical properties of the steel bars were improved, with yield strength and tensile strength remaining relatively stable, elongation increasing, and fatigue life significantly improved.

[0120] The construction team applied the treated steel reinforcement to the foundation construction of key nodes in the 750kV ultra-high voltage transmission line project. During the subsequent six months of construction monitoring, the reinforcement demonstrated excellent stability and reliability, with no cracking, deformation, or other quality issues caused by residual stress. Stress monitoring data after the foundation concrete pouring showed good synergy between the reinforcement and concrete, and the overall stability of the foundation met the stringent technical requirements of the ultra-high voltage transmission line.

[0121] Traditional methods for stress relief in reinforcing steel bars primarily employ natural aging and simple heat annealing. Natural aging requires placing the steel bars at room temperature for months or even years, releasing residual stress through a slow creep and relaxation process. However, this method is extremely time-consuming and has limited effectiveness, typically releasing only 20%–30% of the residual stress. While simple heat annealing can shorten the processing time, the lack of precise temperature control and optimized process parameters often leads to a decline in the mechanical properties of the steel bars, with a residual stress relief rate of only 60%–70%. This invention improves the residual stress relief rate by 13%–15% compared to traditional methods. By applying a segmented heating game optimization model, a balance is achieved between minimizing energy consumption and maximizing stress relief efficiency, avoiding the high energy consumption and low efficiency problems of traditional methods. Real-time monitoring of the stress relief kinetic equations ensures precise control of the stress relief process, avoiding overheating or insufficient treatment problems common in traditional methods. The cooling process optimized by a dynamic programming algorithm effectively avoids the generation of new thermal stress during cooling, ensuring the stability and reliability of the heat treatment effect. This invention provides a more scientific, efficient, and reliable technical solution for stress relief treatment of reinforcing bars in power engineering.

[0122] It should be noted that the variables involved in this invention are explained in detail in Tables 8 and 9.

[0123] Table 8. Variable Explanation Table (Part 1)

[0124]

[0125]

[0126] Table 9. Variable Explanation Table (Part Two)

[0127]

[0128] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for stress-relieving heat treatment of foundation steel reinforcement, characterized in that, Includes the following steps: The reinforcing bars are pre-treated and tested. The initial residual stress distribution inside the reinforcing bars is measured by ultrasonic non-destructive testing technology, and the thickness and material composition of the oxide layer on the surface of the reinforcing bars are recorded. The reinforcing bars are placed in an atmosphere-protected furnace, and the atmosphere inside the furnace is set to nitrogen or argon. A segmented heating game optimization model is constructed for heating control. The segmented heating game optimization model includes an upper-level model with the goal of minimizing energy consumption and a lower-level model with the goal of maximizing stress release efficiency. The objective function of the upper-level model is the minimum product of heating power and time, and the objective function of the lower-level model is the maximum residual stress release per unit time. The two objective functions are correlated through a temperature gradient coupling term. The end temperature of the first heating stage, the end temperature of the second heating stage, and the starting temperature of the holding stage are obtained by solving the problem. A segmented heating program is executed based on temperature parameters; the holding time and stress release process are calculated and analyzed using the stress release kinetic equation; and the holding process is carried out at the initial temperature of the holding stage. The cooling process is optimized using a dynamic programming algorithm; After cooling is complete, a final inspection will be conducted.

2. The method for stress relief heat treatment of foundation steel bars according to claim 1, characterized in that, The pretreatment and testing steps specifically involve measuring the initial residual stress distribution inside the steel bar using ultrasonic non-destructive testing technology, and recording the thickness and material composition of the oxide layer on the steel bar surface to obtain the initial residual stress value, oxide layer thickness value, and material composition data.

3. The method for stress relief heat treatment of foundation steel bars according to claim 2, characterized in that, Specifically, the atmosphere inside the furnace is set to a nitrogen or argon protective atmosphere, the atmosphere pressure is controlled within the range of 0.8 to 1.2 standard atmospheres, and the temperature uniformity inside the furnace is controlled within ±3℃.

4. The method for stress relief heat treatment of foundation steel bars according to claim 3, characterized in that, The segmented heating game optimization model refers to a two-layer optimization model established using game theory to determine the key temperature nodes in the heating process. The upper-layer model is constrained by the heating power not exceeding the rated power of the furnace body, and the lower-layer model is constrained by the heating rate not exceeding the material's tolerance limit.

5. The method for stress relief heat treatment of foundation steel bars according to claim 4, characterized in that, The endpoint temperature of the first heating stage refers to the target temperature value of the first heating stage calculated by the segmented heating game optimization model.

6. The method for stress relief heat treatment of foundation steel bars according to claim 5, characterized in that, The endpoint temperature of the second heating stage refers to the target temperature value of the second heating stage calculated by the segmented heating game optimization model.

7. The method for stress relief heat treatment of foundation steel bars according to claim 6, characterized in that, The starting temperature of the heat preservation stage refers to the temperature value at the beginning of the heat preservation process, calculated by the segmented heating game optimization model.

8. The method for stress relief heat treatment of foundation steel bars according to claim 7, characterized in that, The segmented heating program consists of the following steps: in the first heating stage, the temperature is increased at a rate of 15°C / min to the end temperature of the first heating stage and held for 30 minutes; in the second heating stage, the temperature is increased at a rate of 8°C / min to the end temperature of the second heating stage; and in the third heating stage, the temperature is increased at a rate of 5°C / min to the starting temperature of the holding stage.

9. The method for stress relief heat treatment of foundation steel bars according to claim 8, characterized in that, The stress release kinetic equation is used to describe the physical process of the change of residual stress in steel reinforcement materials over time under high temperature conditions. The inputs include the initial temperature of the heat preservation stage, the material composition data of the steel reinforcement, the initial residual stress value, the geometric dimensions of the steel reinforcement, and the heat preservation time. The outputs are the residual stress release rate and the residual stress distribution state.

10. The method for stress relief heat treatment of foundation steel bars according to claim 9, characterized in that, The heat preservation process involves heat preservation at the initial temperature during the heat preservation stage, with the residual stress changes monitored in real time using an ultrasonic stress detector during the heat preservation process, and the heat preservation stage ending when the residual stress release rate reaches 85%.

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