Workpiece surface residual stress optimization method and device based on multi-parameter acquisition

Abstract

The invention provides a workpiece surface residual stress optimization method and device based on multi-parameter collection, and relates to the technical field of cutting machining optimizing.The workpiece surface residual stress optimization method comprises the steps that cutting force data and cutting temperature data are collected through a three-axis cutting force sensor and an infrared temperature sensor respectively, and the feeding speed and the cooling liquid flow are adjusted in real time through a PID controller; secondly, a workpiece residual stress model is established through finite element analysis, and technological parameters are optimized through a genetic algorithm; and finally, in combination with data of a strain sensor, generating stress deviation according to the stress generated by finite element analysis and the actual stress, and further adjusting the cutting force and the cutting temperature through a PID controller based on the stress deviation so as to achieve the purpose of optimizing the residual stress on the surface of the workpiece.

Description

Technical Field

[0001] The present invention relates to the technical field of cutting process optimization, and in particular to a method and device for optimizing residual stress on a workpiece surface based on multi-parameter acquisition. Background Art

[0002] In modern manufacturing, precision parts are widely used in aerospace, automobile manufacturing, precision instruments and other fields. However, the residual stress generated in the actual processing process has an important influence on the performance and performance of the parts. Excessive residual stress may cause deformation, cracks and even failure of the workpiece, affecting product quality and safety. Traditional residual stress control methods mainly rely on experience and trial and error, lack of systematic parameter adjustment and optimization methods, and it is difficult to achieve effective control of residual stress. With the increasing demand for high-precision and high-reliability parts, there is an urgent need for a stress optimization method based on real-time monitoring and automatic adjustment to improve the stability of the workpiece processing technology and product quality.

[0003] In the prior art, publication number CN115392076B discloses a method for optimizing the residual stress of a turning workpiece based on cutting force and cutting temperature, which creates and runs a turning finite element model, extracts the cutting temperature, cutting force and residual stress in the stable turning stage, creates a residual stress prediction model based on the cutting temperature, cutting force and residual stress, uses an optimization algorithm to obtain the optimized value of the residual stress on the turning surface, and the corresponding optimal values ​​of the cutting temperature and cutting force, and obtains the stress distribution of the workpiece under the working state without considering the residual stress of the workpiece through finite element simulation, divides the workpiece into a near-surface partition affected by the residual stress and an internal partition not affected by the residual stress according to the thickness of the residual stress influence layer, and uses an optimization algorithm to obtain the optimized value of the residual stress for the near-surface partition, and the corresponding optimal values ​​of the cutting temperature and cutting force.

[0004] The main problem with the above method is that after obtaining the optimal values ​​of cutting force and cutting temperature, there is a lack of real-time monitoring and feedback mechanism. During the processing, the properties of the workpiece material may change. If the cutting parameters cannot be adjusted in real time, these uncertainties will affect the processing quality and cause uneven residual stress distribution, which in turn affects the stress optimization effect.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a method and device for optimizing residual stress on the workpiece surface based on multi-parameter acquisition, so as to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: A method for optimizing residual stress on workpiece surface based on multi-parameter acquisition, the specific steps comprising: Step 1: Set the acquisition frequency to 1000 Hz, and use the three-axis cutting force sensor to simultaneously collect the cutting forces in the x, y, and z directions, and generate the real-time collected cutting forces based on the cutting forces in the three directions; Step 2: Establish a relationship model between the cutting force and cutting parameters collected in real time, determine the target cutting force according to the material characteristics and processing requirements, and adjust the cutting parameters in real time through a PID controller based on the deviation between the current cutting force collected in real time and the target cutting force, wherein the cutting parameter is the feed speed; Step 3: Collect cutting temperature and ambient temperature through infrared temperature sensor, keep the acquisition frequency of infrared temperature sensor and three-axis cutting force sensor consistent, determine target cutting temperature according to material thermal characteristics and coolant characteristics, adjust coolant flow rate through PID controller based on the deviation between current cutting temperature collected in real time and target cutting temperature, and establish thermal fluid dynamic equation of cutting temperature and coolant flow rate; Step 4: Perform finite element analysis to simulate the influence of different machining paths and process parameters on the stress distribution of the workpiece, extract the stress components in the x, y, and z directions in the simulation results, generate a fitness function reflecting the residual stress on the workpiece surface based on the stress components in the x, y, and z directions, take the minimum residual stress on the workpiece surface of the fitness function as the optimization goal, optimize the values ​​of the process parameters through a genetic algorithm, and set the minimum value of the residual stress on the workpiece surface as the ideal residual stress, wherein the process parameters include machining accuracy, feed speed, and coolant flow rate; Step 5: Collect strain values ​​through strain sensors, generate actual residual stress based on the elastic modulus of the material, generate stress deviation based on the ideal residual stress and actual residual stress, and adjust the cutting force and cutting temperature based on the stress deviation through the PID algorithm.

[0008] Furthermore, the formula for generating the cutting force collected in real time is: ; in, represents the cutting force, represents the cutting force in the x direction, represents the cutting force in the y direction, Represents the cutting force in the z direction.

[0009] Furthermore, the formula for establishing the relationship model between cutting force and cutting depth is: ; in, represents the cutting force, represents the material constant, Indicates the cutting depth, Indicates the feed speed, Represents the empirical coefficient.

[0010] Furthermore, the formula for adjusting the cutting parameters in real time by the PID controller is: ; in, Indicates the adjustment amount of feed speed, Indicates the deviation between the current cutting force and the target cutting force, Indicates the proportional coefficient for controlling the feed speed, Indicates the integral coefficient for controlling the feed speed, Indicates the differential coefficient for controlling the feed rate.

[0011] Furthermore, the principle on which the thermofluid dynamics equation is based is: The formula for regulating the coolant flow rate by the PID controller is: ; in, Indicates the adjustment amount of the coolant flow rate, Indicates the deviation between the current cutting temperature and the target cutting temperature. Indicates the proportional coefficient for adjusting the coolant flow rate, Indicates the integral coefficient for adjusting the coolant flow rate, Indicates the integral coefficient for adjusting the coolant flow; The thermofluid dynamics equation is: ; in, Indicates the cutting temperature, Indicates the ambient temperature, represents the cutting heat flow, Indicates the coolant density, Indicates the specific heat capacity of the coolant.

[0012] Furthermore, the formula for generating the objective function reflecting the residual stress on the workpiece surface is: ; in, Represents the residual stress on the workpiece surface. represents the stress component in the x direction, represents the stress component in the y direction, represents the stress component in the z direction.

[0013] Furthermore, the principle for adjusting the cutting force and cutting temperature by the PID algorithm is as follows: The formula used to generate the actual residual stress is: ; in, represents the actual residual stress, represents the elastic modulus, Indicates strain; The formula used to generate the stress deviation is: ; in, represents the stress deviation, represents the ideal residual stress; The cutting force adjustment is based on the formula: ; in, Indicates the adjustment value of the cutting force, Represents the proportional coefficient for adjusting the cutting force, Indicates the integral coefficient for adjusting the cutting force, It represents the differential coefficient for adjusting the cutting force; The formula for adjusting the cutting temperature is: ; in, Indicates the adjustment value of cutting temperature, Indicates the proportional coefficient for adjusting the cutting temperature, Indicates the integral coefficient for adjusting the cutting temperature, Indicates the differential coefficient for adjusting the cutting temperature.

[0014] The present invention also provides a workpiece surface residual stress optimization device based on multi-parameter acquisition, and the device is used to implement the above-mentioned workpiece surface residual stress optimization method based on multi-parameter acquisition, which specifically includes: The data acquisition module is used to set the acquisition frequency to 1000 Hz, and simultaneously collect the cutting forces in the three directions of x, y, and z through a three-axis cutting force sensor, and generate the real-time collected cutting forces according to the cutting forces in the three directions; A cutting force optimization module is used to establish a relationship model between the cutting force and cutting parameters collected in real time, determine the target cutting force according to the material characteristics and processing requirements, and adjust the cutting parameters in real time through a PID controller based on the deviation between the current cutting force collected in real time and the target cutting force, wherein the cutting parameters are feed speeds; The cutting temperature optimization module collects cutting temperature and ambient temperature through infrared temperature sensors, keeps the collection frequency of infrared temperature sensors consistent with that of three-axis cutting force sensors, determines the target cutting temperature according to the thermal characteristics of the material and the characteristics of the coolant, adjusts the coolant flow rate through a PID controller based on the deviation between the current cutting temperature collected in real time and the target cutting temperature, and establishes a thermal fluid dynamics equation for the cutting temperature and the coolant flow rate; A comprehensive analysis module is used to perform finite element analysis, simulate the influence of different machining paths and process parameters on the stress distribution of the workpiece, extract the stress components in the x, y, and z directions in the simulation results, generate a fitness function reflecting the residual stress on the workpiece surface based on the stress components in the x, y, and z directions, take the minimum residual stress on the workpiece surface of the fitness function as the optimization goal, optimize the values ​​of the process parameters through a genetic algorithm, and set the minimum value of the residual stress on the workpiece surface as the ideal residual stress, wherein the process parameters include machining accuracy, feed speed, and coolant flow rate; The stress adjustment module is used to collect strain values ​​through strain sensors, generate actual residual stress in combination with the elastic modulus of the material, generate stress deviation according to the ideal residual stress and the actual residual stress, and adjust the cutting force and cutting temperature through the PID algorithm based on the stress deviation.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention collects cutting forces in three directions of x, y and z, comprehensively monitors the distribution of cutting forces in three-dimensional space, establishes a relationship model between cutting forces and cutting parameters, and uses a PID controller for dynamic adjustment, so that the cutting parameters can respond to changes in cutting forces in real time, maintain the cutting forces within an optimal range, and improve the stability and consistency of the cutting process; the PID controller adjusts the coolant flow rate according to real-time temperature data, and establishes a thermal fluid dynamics equation between the cutting temperature and the coolant flow rate, so that the cutting temperature can be controlled more accurately, and by simultaneously monitoring and controlling the cutting force and the cutting temperature in real time, it is ensured that both mechanical stress and thermal stress in the cutting process are effectively controlled, thereby further reducing the residual stress of the workpiece.

[0016] The present invention also optimizes multiple process parameters through finite element analysis and genetic algorithm to find the optimal parameter combination, thereby controlling the cutting force and cutting temperature and reducing the residual stress of the workpiece, which can not only meet the processing requirements of high precision and high reliability, but also improve production efficiency and product quality. After the residual stress is determined through finite element analysis and genetic algorithm, it is compared with the actual residual stress collected to generate a stress deviation, and the cutting force and cutting temperature are adjusted through the PID algorithm based on the stress deviation. This closed-loop control can correct the processing parameters in real time, improve the processing accuracy and stability, and reduce the stress of the workpiece. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of a method flow of an embodiment of the present invention; Figure 2 Schematic diagram of a device module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0019] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0020] Example: See also Figure 1 , the present invention provides a technical solution: A method for optimizing residual stress on workpiece surface based on multi-parameter acquisition, the specific steps comprising: Step 1: Set the acquisition frequency to 1000 Hz, and use the three-axis cutting force sensor to simultaneously collect the cutting forces in the x, y, and z directions, and generate the real-time collected cutting forces based on the cutting forces in the three directions; In this embodiment, the formula for generating the cutting force collected in real time is: ; in, represents the cutting force, represents the cutting force in the x direction, represents the cutting force in the y direction, Represents the cutting force in the z direction.

[0021] Based on the Pythagorean theorem, the magnitude of the total vector force can be calculated by the square root of the sum of the squares of its components in each direction. The cutting force is applied to the workpiece and the tool in different directions. In three-dimensional space, it can be regarded as forces in the x, y, and z directions. The total effect of these forces is expressed by an equivalent total force. The cutting force reflects the effect of the combined effect of the forces in these three directions.

[0022] Step 2: Establish a relationship model between the cutting force and cutting parameters collected in real time, determine the target cutting force according to the material characteristics and processing requirements, and adjust the cutting parameters in real time through a PID controller based on the deviation between the current cutting force collected in real time and the target cutting force, wherein the cutting parameter is the feed speed; In this embodiment, the formula for establishing the relationship model between cutting force and cutting depth is: ; in, represents the cutting force, represents the material constant, Indicates the cutting depth, Indicates the feed speed, Represents the empirical coefficient.

[0023] The material constant is a constant related to the material properties of the workpiece, reflecting the response characteristics of the material to the cutting force. The material constants of different materials are determined based on experiments or experience; The cutting depth refers to the cutting depth of the tool on the workpiece surface, which directly affects the thickness of the cutting layer. The greater the cutting depth, the more material is removed per unit time, resulting in an increase in cutting force. Therefore, the cutting depth is proportional to the cutting force. The feed rate indicates the speed at which the tool moves relative to the workpiece, which affects the deformation rate of the material during the cutting process. As the feed rate increases, the width of the cutting layer increases and the cutting force increases.

[0024] The formula for real-time adjustment of cutting parameters by PID controller is: ; in, Indicates the feed speed adjustment amount, Indicates the deviation between the current cutting force and the target cutting force, Indicates the proportional coefficient for controlling the feed speed, Indicates the integral coefficient for controlling the feed speed, Indicates the differential coefficient for controlling the feed rate.

[0025] The PID controller consists of three parts: proportional control, integral control and differential control, which process the current value, cumulative value and change rate of the error respectively, and make control adjustments based on this information. Proportional control is used to generate a control signal based on the current error size; integral control is used to generate a control signal based on the cumulative value of the error to eliminate long-term steady-state errors; differential control is used to generate a control signal based on the rate of change of the error, suppress rapid changes in the error, and improve the response speed and stability of the system.

[0026] The proportional coefficient, integral coefficient and differential coefficient of the PID controller can be adjusted through the empirical adjustment method. Turn off the integral control and differential control, start from a smaller set value, gradually increase the proportional coefficient, and observe the system response until the system reaches a stable but not excessively oscillating state, that is, the system has neither continuous large-scale oscillations nor excessive steady-state errors during the response process. On the basis of determining the proportional coefficient, gradually increase the integral coefficient to avoid excessive oscillation caused by integral saturation. When the system responds too slowly or the integral is saturated, appropriately reduce the integral coefficient until the slow response and integral saturation disappear, and record the integral coefficient at this time; on the basis of the adjusted proportional coefficient and integral coefficient, gradually increase the differential coefficient until the high-frequency noise is not significantly amplified.

[0027] Step 3: Collect cutting temperature and ambient temperature through infrared temperature sensor, keep the acquisition frequency of infrared temperature sensor and three-axis cutting force sensor consistent, determine target cutting temperature according to material thermal characteristics and coolant characteristics, adjust coolant flow rate through PID controller based on the deviation between current cutting temperature collected in real time and target cutting temperature, and establish thermal fluid dynamic equation of cutting temperature and coolant flow rate; In this embodiment, the principle on which the thermal fluid dynamics equation is established is: The formula for regulating the coolant flow rate by the PID controller is: ; in, Indicates the adjustment amount of the coolant flow rate, Indicates the deviation between the current cutting temperature and the target cutting temperature. Indicates the proportional coefficient for adjusting the coolant flow rate, Indicates the integral coefficient for adjusting the coolant flow rate, Indicates the integral coefficient for adjusting the coolant flow; The thermofluid dynamics equation is: ; in, Indicates the cutting temperature, Indicates the ambient temperature, represents the cutting heat flow, Indicates the coolant density, Indicates the specific heat capacity of the coolant.

[0028] The above thermal fluid dynamics equation is based on the principle of energy balance. The heat taken away by the coolant is used to balance the heat generated during the cutting process to achieve the purpose of regulating the cutting temperature. It indicates that during the cutting process, the temperature of the coolant rises due to the absorption of the heat generated by the cutting. The main influencing factor of the cutting temperature is the coolant flow rate. The greater the coolant flow rate, the lower the temperature of the coolant rises due to the absorption of the heat generated by the cutting. The ambient temperature indicates the temperature of the processing environment, which can also be regarded as the initial temperature of the coolant entering the workpiece area. The cutting heat flow rate indicates the heat generated by cutting per unit time.

[0029] Step 4: Perform finite element analysis to simulate the influence of different machining paths and process parameters on the stress distribution of the workpiece, extract the stress components in the x, y, and z directions in the simulation results, generate a fitness function reflecting the residual stress on the workpiece surface based on the stress components in the x, y, and z directions, take the minimum residual stress on the workpiece surface of the fitness function as the optimization goal, optimize the values ​​of the process parameters through a genetic algorithm, and set the minimum value of the residual stress on the workpiece surface as the ideal residual stress, wherein the process parameters include machining accuracy, feed speed, and coolant flow rate; In this embodiment, the elastic modulus, Poisson's ratio, density, thermal conductivity and other necessary mechanical properties of the workpiece are imported into the finite element analysis software ANSYS, the model is divided into a tetrahedral finite element grid, the adjusted feed speed and coolant flow generated in steps 1 to 3 are applied to the cutting area, and the stress magnitudes in the x, y and z directions are obtained; In this embodiment, the formula for generating the objective function reflecting the residual stress on the workpiece surface is: ; in, Represents the residual stress on the workpiece surface. represents the stress component in the x direction, represents the stress component in the y direction, represents the stress component in the z direction.

[0030] The principle for optimizing the values ​​of process parameters through genetic algorithms is: Each set of process parameter values ​​is regarded as an individual of the genetic algorithm, and the fitness function of each individual is calculated. The individual is selected by the roulette algorithm, and the selected individuals are arranged from high to low according to the fitness. The adjacent individuals are extracted to form parents, and genes are exchanged between the parent individuals to generate offspring individuals. Genes are randomly selected from the offspring individuals for change, and the fitness of the individual is recalculated. The above operation is repeated until the preset number of iterations is reached, and the process parameters corresponding to the individual with the highest fitness are selected as the values ​​of the process parameters; The steps to select individuals with high fitness through the roulette algorithm are: Calculate the sum of the fitness of all individuals. For each individual, divide its fitness value by the sum of the fitness of all individuals to generate the probability of selection of the corresponding individual. Build a cumulative probability table based on the selection probability of each individual, and generate a random number between 0 and 1. Select the corresponding individual in the cumulative probability table based on the random number. This can maintain the diversity of the population while selecting individuals with higher fitness. The genes represent process parameters including machining accuracy, feed speed, and coolant flow rate.

[0031] Step 5: Collect strain values ​​through strain sensors, generate actual residual stress based on the elastic modulus of the material, generate stress deviation based on the ideal residual stress and actual residual stress, and adjust the cutting force and cutting temperature based on the stress deviation through the PID algorithm.

[0032] In this embodiment, the cutting force is too large, resulting in large plastic deformation and thermal stress on the workpiece surface, increasing residual stress, and the cutting force is too small, resulting in low material removal efficiency, vibration and unstable processing, affecting the surface quality; If the cutting temperature is too high, the material on the surface of the workpiece will soften and recrystallize, and the residual stress will increase. If the temperature is too low, the material may become more brittle, cracking and uneven deformation may occur. In this embodiment, the principle for adjusting the cutting force and cutting temperature by the PID algorithm is: The formula used to generate the actual residual stress is: ; in, represents the actual residual stress, represents the elastic modulus, Indicates strain; The formula used to generate the stress deviation is: ; in, represents the stress deviation, represents the ideal residual stress; The cutting force adjustment is based on the formula: ; in, Indicates the adjustment value of the cutting force, Represents the proportional coefficient for adjusting the cutting force, Indicates the integral coefficient for adjusting the cutting force, It represents the differential coefficient for adjusting the cutting force; The formula for adjusting the cutting temperature is: ; in, Indicates the adjustment value of cutting temperature, Indicates the proportional coefficient for adjusting the cutting temperature, Indicates the integral coefficient for adjusting the cutting temperature, Indicates the differential coefficient for adjusting the cutting temperature.

[0033] See also Figure 2 The present invention also provides a workpiece surface residual stress optimization device based on multi-parameter acquisition, and the device is used to implement the above-mentioned workpiece surface residual stress optimization method based on multi-parameter acquisition, which specifically includes: The data acquisition module is used to set the acquisition frequency to 1000 Hz, and simultaneously collect the cutting forces in the three directions of x, y, and z through a three-axis cutting force sensor, and generate the real-time collected cutting forces according to the cutting forces in the three directions; A cutting force optimization module is used to establish a relationship model between the cutting force and cutting parameters collected in real time, determine the target cutting force according to the material characteristics and processing requirements, and adjust the cutting parameters in real time through a PID controller based on the deviation between the current cutting force collected in real time and the target cutting force, wherein the cutting parameters are feed speeds; The cutting temperature optimization module collects cutting temperature and ambient temperature through infrared temperature sensors, keeps the collection frequency of infrared temperature sensors consistent with that of three-axis cutting force sensors, determines the target cutting temperature according to the thermal characteristics of the material and the characteristics of the coolant, adjusts the coolant flow rate through a PID controller based on the deviation between the current cutting temperature collected in real time and the target cutting temperature, and establishes a thermal fluid dynamics equation for the cutting temperature and the coolant flow rate; A comprehensive analysis module is used to perform finite element analysis, simulate the influence of different machining paths and process parameters on the stress distribution of the workpiece, extract the stress components in the x, y, and z directions in the simulation results, generate a fitness function reflecting the residual stress on the workpiece surface based on the stress components in the x, y, and z directions, take the minimum residual stress on the workpiece surface of the fitness function as the optimization goal, optimize the values ​​of the process parameters through a genetic algorithm, and set the minimum value of the residual stress on the workpiece surface as the ideal residual stress, wherein the process parameters include machining accuracy, feed speed, and coolant flow rate; The stress adjustment module is used to collect strain values ​​through strain sensors, generate actual residual stress in combination with the elastic modulus of the material, generate stress deviation according to the ideal residual stress and the actual residual stress, and adjust the cutting force and cutting temperature through the PID algorithm based on the stress deviation.

[0034] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0035] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0036] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0037] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for optimizing residual stress on workpiece surface based on multi-parameter acquisition, characterized in that: The specific steps include: Step 1: Set the acquisition frequency to 1000 Hz, and use the three-axis cutting force sensor to simultaneously collect the cutting forces in the x, y, and z directions, and generate the real-time collected cutting forces based on the cutting forces in the three directions; Step 2: Establish a relationship model between the cutting force and cutting parameters collected in real time, determine the target cutting force according to the material characteristics and processing requirements, and adjust the cutting parameters in real time through a PID controller based on the deviation between the current cutting force collected in real time and the target cutting force, wherein the cutting parameter is the feed speed; Step 3: Collect cutting temperature and ambient temperature through infrared temperature sensor, keep the acquisition frequency of infrared temperature sensor and three-axis cutting force sensor consistent, determine target cutting temperature according to material thermal characteristics and coolant characteristics, adjust coolant flow rate through PID controller based on the deviation between current cutting temperature collected in real time and target cutting temperature, and establish thermal fluid dynamic equation of cutting temperature and coolant flow rate; Step 4: Perform finite element analysis to simulate the influence of different machining paths and process parameters on the stress distribution of the workpiece, extract the stress components in the x, y, and z directions in the simulation results, generate a fitness function reflecting the residual stress on the workpiece surface based on the stress components in the x, y, and z directions, take the minimum residual stress on the workpiece surface of the fitness function as the optimization goal, optimize the values ​​of the process parameters through a genetic algorithm, and set the minimum value of the residual stress on the workpiece surface as the ideal residual stress, wherein the process parameters include machining accuracy, feed speed, and coolant flow rate; Step 5: Collect strain values ​​through strain sensors, generate actual residual stress based on the elastic modulus of the material, generate stress deviation based on the ideal residual stress and actual residual stress, and adjust the cutting force and cutting temperature based on the stress deviation through the PID algorithm.

2. The method for optimizing residual stress on workpiece surface based on multi-parameter acquisition according to claim 1, characterized in that: The formula for generating the real-time collected cutting force in step 1 is: ； in, represents the cutting force, represents the cutting force in the x direction, represents the cutting force in the y direction, Represents the cutting force in the z direction.

3. The method for optimizing residual stress on workpiece surface based on multi-parameter acquisition according to claim 1, characterized in that: The formula for establishing the cutting force and cutting depth relationship model in step 2 is: ； in, represents the cutting force, represents the material constant, Indicates the cutting depth, Indicates the feed speed, Represents the empirical coefficient.

4. The method for optimizing residual stress on workpiece surface based on multi-parameter acquisition according to claim 1, characterized in that: The formula used by the PID controller to adjust the cutting parameters in real time in step 2 is: ； in, Indicates the feed speed adjustment amount, Indicates the deviation between the current cutting force and the target cutting force, Indicates the proportional coefficient for controlling the feed speed, Indicates the integral coefficient for controlling the feed speed, Indicates the differential coefficient for controlling the feed rate.

5. The method for optimizing residual stress on workpiece surface based on multi-parameter acquisition according to claim 1, characterized in that: The principle on which the thermal fluid dynamics equation is established in step 3 is: The formula for regulating the coolant flow rate by the PID controller is: ； in, Indicates the adjustment amount of the coolant flow rate, Indicates the deviation between the current cutting temperature and the target cutting temperature. Indicates the proportional coefficient for adjusting the coolant flow rate, Indicates the integral coefficient for adjusting the coolant flow rate, Indicates the integral coefficient for adjusting the coolant flow; The thermofluid dynamics equation is: ； in, Indicates the cutting temperature, Indicates the ambient temperature, represents the cutting heat flow, Indicates the coolant density, Indicates the specific heat capacity of the coolant.

6. The method for optimizing residual stress on workpiece surface based on multi-parameter acquisition according to claim 1, characterized in that: The formula for generating the objective function reflecting the residual stress on the workpiece surface in step 4 is: ； in, Represents the residual stress on the workpiece surface. represents the stress component in the x direction, represents the stress component in the y direction, represents the stress component in the z direction.

7. The method for optimizing residual stress on workpiece surface based on multi-parameter acquisition according to claim 1, characterized in that: The principle for adjusting the cutting force and cutting temperature by the PID algorithm in step 5 is: The formula used to generate the actual residual stress is: ； in, represents the actual residual stress, represents the elastic modulus, Indicates strain; The formula used to generate the stress deviation is: ； in, represents the stress deviation, represents the ideal residual stress; The cutting force adjustment is based on the formula: ； in, Indicates the adjustment value of the cutting force, Represents the proportional coefficient for adjusting the cutting force, Indicates the integral coefficient for adjusting the cutting force, Indicates the differential coefficient for adjusting the cutting force; The formula for adjusting the cutting temperature is: ； in, Indicates the adjustment value of cutting temperature, Indicates the proportional coefficient for adjusting the cutting temperature, Indicates the integral coefficient for adjusting the cutting temperature, Indicates the differential coefficient for adjusting the cutting temperature.

8. A workpiece surface residual stress optimization device based on multi-parameter acquisition, characterized in that: The device is used to implement the workpiece surface residual stress optimization method based on multi-parameter acquisition according to any one of claims 1 to 7, and specifically comprises: The data acquisition module is used to set the acquisition frequency to 1000 Hz, and simultaneously collect the cutting forces in the three directions of x, y, and z through a three-axis cutting force sensor, and generate the real-time collected cutting forces according to the cutting forces in the three directions; A cutting force optimization module is used to establish a relationship model between the cutting force and cutting parameters collected in real time, determine the target cutting force according to the material characteristics and processing requirements, and adjust the cutting parameters in real time through a PID controller based on the deviation between the current cutting force collected in real time and the target cutting force, wherein the cutting parameters are feed speeds; The cutting temperature optimization module collects cutting temperature and ambient temperature through infrared temperature sensors, keeps the collection frequency of infrared temperature sensors consistent with that of three-axis cutting force sensors, determines the target cutting temperature according to the thermal characteristics of the material and the characteristics of the coolant, adjusts the coolant flow rate through a PID controller based on the deviation between the current cutting temperature collected in real time and the target cutting temperature, and establishes a thermal fluid dynamics equation for the cutting temperature and the coolant flow rate; A comprehensive analysis module is used to perform finite element analysis, simulate the influence of different machining paths and process parameters on the stress distribution of the workpiece, extract the stress components in the x, y, and z directions in the simulation results, generate a fitness function reflecting the residual stress on the workpiece surface based on the stress components in the x, y, and z directions, take the minimum residual stress on the workpiece surface of the fitness function as the optimization goal, optimize the values ​​of the process parameters through a genetic algorithm, and set the minimum value of the residual stress on the workpiece surface as the ideal residual stress, wherein the process parameters include machining accuracy, feed speed, and coolant flow rate; The stress adjustment module is used to collect strain values ​​through strain sensors, generate actual residual stress in combination with the elastic modulus of the material, generate stress deviation according to the ideal residual stress and the actual residual stress, and adjust the cutting force and cutting temperature through the PID algorithm based on the stress deviation.

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