Internal meshing powerful gear honing process parameter collaborative optimization method facing tooth profile error and energy consumption

Abstract

The invention discloses an internal meshing powerful gear honing process parameter collaborative optimization method for tooth profile error and energy consumption, which comprises the following steps of: firstly, analyzing a multi-axis linkage processing material removal mechanism of an internal meshing powerful gear honing process, and clarifying multi-axis energy consumption composition and time interval characteristics of the gear honing process; secondly, a gear honing process tooth profile error and energy consumption coupling model driven by mechanism data in a hybrid mode is established, and a coupling action mechanism of the gear honing process tooth profile error and energy consumption is revealed; and finally, an internal meshing powerful gear honing process parameter collaborative optimization model facing tooth profile errors and energy consumption is constructed, optimization solution is conducted based on an improved multi-target raccoon optimization algorithm, an internal meshing powerful gear honing process parameter optimal combination scheme is obtained, and high-precision, efficient and energy-saving machining of the internal meshing powerful gear honing process is achieved.

Description

Technical Field

[0001] This invention relates to the field of metal cutting machine tool machining, specifically to a method for synergistic optimization of tooth profile error and energy consumption in internal meshing high-strength honing processes. Background Technology

[0002] Internal meshing high-strength honing can create unique tooth surface textures and residual compressive stress, making it a key machining process for high-speed, low-noise gears in electric drive transmission systems for new energy vehicles. This process involves multi-axis linkage and complex tool movement trajectories; errors in each axis's motion can lead to changes in the workpiece tooth profile, thus affecting machining accuracy. Furthermore, internal meshing high-strength honing is a dual-spindle drive process, with high spindle speeds for both the tool and workpiece during honing, resulting in a rated total power of 60-80 kW and significant energy-saving potential. Therefore, researching collaborative optimization methods for internal meshing high-strength honing process parameters, addressing tooth profile errors and energy consumption, is of great significance for high-precision, high-efficiency, and energy-saving machining of high-speed gears for new energy vehicles.

[0003] Current research on improving the accuracy and energy efficiency of CNC machine tools mostly focuses on turning and milling processes. A few scholars have paid attention to modeling the machining accuracy and energy consumption of processes such as gear hobbing and gear grinding, but few have comprehensively considered the coordinated control of machining accuracy and energy consumption. Internal meshing high-strength honing processes involve complex multi-axis linkage trajectories, and even small motion errors can lead to significant changes in the tooth profile of the workpiece. Furthermore, the spindle speed during honing is high, with a rated total power of 60-80 kW, indicating significant energy-saving potential. There is a coupling effect between honing accuracy and energy consumption; improving one indicator will affect the other. Therefore, this invention starts with a collaborative optimization method for internal meshing high-strength honing process parameters oriented towards tooth profile error and energy consumption. It analyzes the processing mechanism and energy consumption characteristics of internal meshing high-strength honing process, establishes a coupling model of tooth profile error and energy consumption driven by mechanism data, reveals the coupling mechanism of tooth profile error and energy consumption in honing process, constructs a collaborative optimization model for honing process parameters oriented towards tooth profile error and energy consumption, and uses an improved multi-objective raccoon optimization algorithm for optimization solution, proposing a collaborative control method for tooth profile error and energy consumption in internal meshing high-strength honing process. Summary of the Invention

[0004] This invention provides a collaborative optimization method for internal meshing high-strength honing process parameters, which addresses tooth profile error and energy consumption. By adjusting and optimizing process parameters, the method reduces workpiece tooth profile shape deviation and machining energy consumption in the internal meshing high-strength honing process.

[0005] The technical solution adopted to achieve the purpose of this invention is as follows: a method for collaborative optimization of internal meshing high-strength honing process parameters oriented towards tooth profile error and energy consumption, comprising the following steps:

[0006] Step 1: Analyze the material removal mechanism of multi-axis linkage machining in the internal meshing high-strength honing process, and clarify the multi-axis energy consumption composition and time characteristics of the honing process.

[0007] Step 2: Establish a coupling model of tooth profile error and energy consumption in honing process driven by hybrid mechanism data, and reveal the coupling mechanism of tooth profile error and energy consumption in honing process;

[0008] Step 3: Construct a collaborative optimization model for internal meshing high-strength honing process parameters oriented towards tooth profile error and energy consumption, and perform optimization and solution based on the improved multi-objective raccoon optimization algorithm.

[0009] Preferably, the analysis of the material removal mechanism and energy consumption characteristics of the multi-axis linkage machining process in step 1 is as follows:

[0010] (1) The internal meshing high-strength honing machine structure proposed in this invention is as follows: Figure 1 As shown, the multi-axis linkage machining process and material removal mechanism of the internal meshing high-strength honing process are analyzed. The honing process involves six axes: the honing wheel spindle, the workpiece spindle, the honing wheel axial feed axis, the honing wheel radial feed axis, the axis angle adjustment axis, and the honing wheel oscillation axis. Under the radial feed motion of the honing wheel, the abrasive grains on the surface of the honing wheel apply honing pressure to the gear tooth surface of the workpiece. With the axial reciprocating feed motion of the honing wheel and the continuous rotation of the honing wheel and the workpiece, the abrasive grains on the surface of the honing wheel continuously slide (elastic deformation stage), plow (plastic deformation stage), and cut (material removal stage) on the gear tooth surface of the workpiece, thereby removing the material from the gear tooth surface.

[0011] (2) Analyze the multi-axis energy consumption composition and time characteristics of the internal meshing high-strength honing process. According to the energy consumption composition characteristics, the processing energy consumption of the internal meshing high-strength honing process consists of auxiliary system energy consumption, no-load energy consumption, material removal energy consumption, and additional load energy consumption, which can be expressed as:

[0012]

[0013] In the formula, E honing Energy consumption for honing process; E au The energy consumption of auxiliary systems is present throughout the entire honing process. This energy consumption includes power-related auxiliary system components (CNC system, lighting, etc.) and machining-related auxiliary system components (hydraulic system, cooling motor, etc.); E u This represents the no-load energy consumption, which consists of the no-load energy consumption of each motion axis; E mr E represents the energy consumption for material removal, reflecting the energy consumption generated during the meshing of the honing wheel and the workpiece gear to remove tooth surface material; add Additional load energy consumption refers to the load loss caused by the increase in honing force and torque during honing.

[0014] The power curve of the internal meshing high-strength honing process is as follows: Figure 2 As shown, based on the energy consumption characteristics during different time periods, the energy consumption of the internal meshing high-strength honing process includes standby energy consumption, air cutting energy consumption, and cutting energy consumption, which can be expressed as:

[0015]

[0016] In the formula, E st E air E cut These represent the standby energy consumption, air cutting energy consumption, and cutting energy consumption of the honing process, respectively.

[0017] Standby power consumption refers to the energy consumption generated during operations such as clamping, pre-honing inspection, and disassembly of the workpiece gear. It consists of the energy consumption of power-related auxiliary systems, motor frequency converters, and servo drives. The standby power consumption E of the honing process... st It can be represented as:

[0018]

[0019] In the formula, E aus E represents the energy consumption of power-related auxiliary system components. inverter E represents the energy consumption of the motor frequency converter. driver This indicates the power consumption of the servo drive.

[0020] The energy consumption of honing in the air cutting process mainly includes the energy consumption generated during the shaft angle adjustment process, the axial air feed and radial air feed of the honing wheel, and is mainly composed of auxiliary system energy consumption and no-load energy consumption. The air cutting energy consumption E of the honing process air It can be represented as:

[0021]

[0022] The cutting energy consumption of the honing process refers to the energy consumption generated by the honing machine during the material removal time of the workpiece tooth surface. It mainly includes auxiliary system energy consumption, no-load energy consumption, material removal energy consumption, and additional load energy consumption. The cutting energy consumption E of the honing process... cut It can be represented as:

[0023]

[0024] Preferably, the modeling process of the honing process tooth profile error and energy consumption coupling model driven by mechanism data in step 2 is as follows:

[0025] (1) A data-driven mapping model of the honing process tooth profile deviation and process parameters such as the honing wheel spindle speed, honing wheel axial feed rate, axial oscillation distance, and radial honing feed rate is established using the gradient-enhanced kriging method. The established tooth profile deviation model can be expressed as follows:

[0026]

[0027] In the formula, f fα For tooth profile deviation, f GEK For gradient-enhanced Kriging models, n h f is the spindle speed of the honing wheel. z L represents the axial feed rate of the honing wheel. Z L is the axial oscillation distance. X This is the radial honing feed rate.

[0028] (2) The processing energy consumption of internal meshing high-strength honing process includes standby energy consumption, air cutting energy consumption and cutting energy consumption.

[0029] Standby power consumption E st It can be represented as:

[0030]

[0031] In the formula, P st For standby power, t st This refers to standby time.

[0032] The no-load power of the internal meshing high-strength honing process includes auxiliary system power and no-load power, with no-load power P. air It can be represented as

[0033]

[0034] In the formula, P au P is the power of the auxiliary system for the honing process. u This refers to the no-load power during the idle cutting phase of the honing process.

[0035] The power of auxiliary systems is usually fixed and can be measured by sensors. The no-load power of the honing process mainly consists of the no-load power of each axis of the honing machine. The no-load power of each axis mainly consists of the motor power, the inverter / servo drive power, and the mechanical transmission loss power. For example, the no-load power of the honing wheel spindle is:

[0036]

[0037] In the formula, , , These represent the motor power, frequency converter power, and mechanical transmission loss power of the honing wheel spindle, respectively. h Let a be the rotational speed of the honing wheel spindle, and a1 and a2 be the mechanical transmission power loss coefficients of the honing wheel spindle. Similarly, the no-load power of the other shafts is calculated using this formula.

[0038] The cutting power in the internal meshing high-strength honing process includes auxiliary system power, no-load power, material removal power, and additional load power, with the cutting power P being the total cutting power. cut It can be represented as

[0039]

[0040] In the formula, P mr P represents the material removal power of the honing process. add This refers to the additional load power applied during the honing process.

[0041] Material removal power P of internal meshing high-strength honing process mr for

[0042]

[0043] In the formula, F c Let v be the honing force, v be the honing speed, k1, k2, and k3 be the honing force coefficients, and b be the honing force. w d is the tooth width of the workpiece. h d is the pitch circle diameter of the honing wheel. w The diameter of the workpiece's pitch circle. The angle between the honing wheel and the workpiece axis.

[0044] After obtaining the standby energy consumption, air cutting energy consumption, and cutting energy consumption, a mechanism mapping model for the energy consumption of internal meshing high-strength honing is constructed regarding process parameters such as the honing wheel spindle speed, honing wheel axial feed rate, axial oscillation distance, and radial honing feed. The established energy consumption model for internal meshing high-strength honing can be expressed as follows:

[0045]

[0046] In the formula, , , , , These represent the no-load power (w) of the internal meshing high-strength honing process shafts: the angle adjustment shaft, the honing wheel spindle, the workpiece spindle, the axial feed shaft of the honing wheel, and the radial feed shaft of the honing wheel. A Adjust the angular velocity of the shaft rotation to adjust the shaft intersection angle, L Zair L is the axial feed cut length of the honing wheel. Xair F is the radial feed cut length of the honing wheel. Xair L is the radial air feed speed of the honing wheel. safe For a safe distance, L cut For machining allowance, t spc For sparkless honing time.

[0047] (3) The established mechanism-data hybrid driven honing process tooth profile error and energy consumption coupling model can be expressed as follows:

[0048]

[0049] In the formula, f mech This represents the energy consumption mechanism mapping model.

[0050] Preferably, in step 3, the collaborative optimization model of internal meshing high-strength honing process parameters oriented towards tooth profile error and energy consumption is constructed and solved. The modeling and optimization solution process is as follows: Figure 3 As shown, the specific process is as follows:

[0051] (1) Construct a collaborative optimization model for internal meshing high-strength honing process parameters oriented towards tooth profile deviation and energy consumption:

[0052]

[0053] In the formula, n hmin and n hmax These represent the minimum and maximum rotational speeds of the honing wheel spindle, respectively. zmin and f zmax These represent the minimum and maximum axial feed rates, respectively, L Zmin and L Zmax L represents the minimum and maximum axial oscillation distances, respectively. Xmin and L Xmax These represent the minimum and maximum feed rates for radial honing, respectively, n wmin and n wmax These represent the minimum and maximum speeds of the workpiece spindle, β. w F is the helix angle of the workpiece. Xmax The maximum permissible radial feed rate, k1, k2, and k3 are honing force coefficients, b w d is the tooth width of the workpiece. h d is the pitch circle diameter of the honing wheel. w F is the pitch circle diameter of the workpiece. cmax For the maximum allowable honing force, P hmax This refers to the rated power of the honing wheel spindle motor. The efficiency of the honing wheel spindle motor.

[0054] (2) Introducing Circle chaotic mapping and Levy flight strategy, we propose an improved multi-objective raccoon optimization algorithm and use this improved algorithm to solve the optimization problem.

[0055] Based on the Circle wonton mapping, candidate solutions for initial honing process parameters, including the honing wheel spindle speed, axial feed rate, axial oscillation distance, and radial honing feed, are constructed as follows:

[0056]

[0057] In the formula, z iLet represent the candidate solution for the i-th honing process parameter combination, and mod is the remainder function.

[0058] The tooth profile deviation and energy consumption objective function value corresponding to the candidate solution can be expressed as:

[0059]

[0060] Introducing the Levy flight strategy, the candidate solution positions for honing process parameters are updated. The formula for calculating the candidate solution positions is as follows:

[0061]

[0062] In the formula, Let r represent the honing process parameter value of the i-th candidate solution in the l-th iteration. coa A random number between [0, 1] and Let L represent the local upper and lower boundaries of the j-th decision variable during the iteration process, respectively, and L be the flight step size. Attached Figure Description

[0063] Figure 1 Internal meshing high-strength honing machine structure

[0064] Figure 2 Power curve of internal meshing high-strength honing process

[0065] Figure 3 Modeling and Co-optimization Process of Tooth Profile Error and Energy Consumption in Internal Meshing High-Strength Honing Process

[0066] Figure 4 Prediction results of tooth profile shape deviation using different modeling methods

[0067] Figure 5 Influence of honing process parameters on tooth profile deviation and energy consumption

[0068] Figure 6 Pareto Front of Different Optimization Algorithms Detailed Implementation

[0069] The present invention will be further described below with reference to the accompanying drawings and embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practice in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0070] In this embodiment, an internal meshing high-strength honing experiment was carried out in a gear processing workshop for electric drive transmission assembly of a new energy vehicle. Energy consumption data and tooth profile deviation data of the workpiece gear were collected during the honing process in each experiment. The experimental operating parameters are shown in Table 1.

[0071]

[0072] To verify the superiority of the proposed honing process tooth profile error modeling method, the tooth profile shape deviation model constructed by the proposed gradient-enhanced kriging (GEK) is compared with the response surface methodology (RSM), support vector regression (SVR), and kriging methods. The tooth profile shape deviation prediction results of different modeling methods are shown below. Figure 4 As shown in the figure, compared with RSM, SVR, and Kriging, the tooth profile shape deviation model established using GEK predicts values ​​closer to the true values, with a smaller deviation between predicted and actual values, indicating that the proposed GEK modeling method has higher modeling accuracy. Furthermore, RMSE, MAE, and MAPE are introduced as evaluation indicators; the smaller the RMSE, MAE, and MAPE, the higher the prediction accuracy. Table 2 shows the evaluation indicators for different modeling methods. As can be seen from the table, the RMSE, MAE, and MAPE of the tooth profile shape deviation model established by GEK are 0.047, 0.042, and 0.052, respectively, which are also the smallest among the four modeling methods. The results show that the GEK modeling method effectively learns the key error features in the honing process, has high modeling accuracy, and is reliable for modeling tooth profile shape deviations in the honing process.

[0073]

[0074] To evaluate the modeling accuracy of the energy consumption model for internal meshing high-strength honing, different honing wheel spindle speeds n were used. h Axial feed rate f z axial oscillation distance L Z Radial feed rate L X Seven honing experiments were conducted under uniform honing process parameters, and the calculated values ​​of the machining energy consumption model and the actual measured values ​​of energy consumption were statistically analyzed. The modeling performance of the machining energy consumption for the internal meshing high-strength honing process is shown in Table 3. Residual and accuracy were introduced to determine the degree of deviation between the calculated values ​​of the energy consumption model and the actual measured values ​​of energy consumption. The residual is the difference between the actual measured value and the calculated value of the energy consumption model, and the accuracy is the ratio of the calculated value of the energy consumption model to the actual measured value of energy consumption. As can be seen from Table 3, the residuals between the actual measured values ​​and the calculated values ​​of the energy consumption model in the seven experiments were very small, and the accuracy was all above 98%. The results indicate that the machining energy consumption model for the internal meshing high-strength honing process established in this invention has high modeling accuracy, and the modeling results are reliable.

[0075]

[0076] The influence of internal meshing high-strength honing process parameters on tooth profile deviation and energy consumption is as follows: Figure 5 As shown in the figure. It can be seen that the tooth profile shape deviation ffα As the honing wheel spindle speed n h and axial oscillation distance L Z It decreases as the axial feed rate f increases. z and radial feed rate L X The energy consumption of gear honing increases with the increase in spindle speed. This is because a higher spindle speed increases the number of cutting actions of the abrasive grains on the tooth surface material per unit time, while a larger axial oscillation distance reduces the wear frequency of the abrasive grains on the honing wheel surface. Larger axial feed rates and radial feed amounts increase the axial and radial feed speeds; excessively fast feed speeds reduce the stability of the tooth surface material removal process. The energy consumption of gear honing is E. honing As the honing wheel rotates at speed n h and axial oscillation distance L Z The value increases with the increase of the axial feed rate f. z and radial feed rate L X The steepness of the surface decreases as the honing process parameters increase. Furthermore, the steepness of the surface reflects the degree to which honing process parameters affect tooth profile deviation and energy consumption. Figure 5 It can be seen that the spindle speed of the honing wheel, the axial feed rate, and the radial feed amount have a significant impact on the tooth profile deviation. The process parameters that have a significant impact on the energy consumption of honing are the radial feed amount, the axial feed rate, and the axial oscillation distance.

[0077] To verify the necessity of collaborative optimization, the results were compared with those of single tooth profile shape deviation, energy consumption optimization schemes, and empirical schemes. Table 4 shows the optimization results of different schemes. As can be seen from the table, the tooth profile shape deviation f obtained by the collaborative optimization scheme... fα The thickness is 0.8 μm, and the processing energy consumption is E. honing 2.01×10 5 Compared to optimizing tooth profile shape deviation individually, the collaborative optimization scheme sacrificed 37.50% of the tooth profile shape deviation but reduced energy consumption by 68.35%. Compared to optimizing energy consumption individually, the collaborative optimization scheme reduced tooth profile shape deviation by 70.37% but only sacrificed 52.27% of energy consumption. Furthermore, the tooth profile shape deviation obtained by individual energy optimization was 2.7 μm, exceeding the maximum allowable tooth profile shape deviation value (≤2.5 μm), which does not meet the processing requirements. Compared to empirical schemes, the collaborative optimization scheme reduced tooth profile shape deviation by 38.46% and energy consumption by 9.05%. The results show that the proposed collaborative optimization scheme effectively balances tooth profile error and energy consumption in the honing process, achieving optimal coordination between the two objectives while ensuring processing accuracy. This also proves the necessity and superiority of collaborative optimization.

[0078]

[0079] To verify the effectiveness of the proposed improved multi-objective raccoon optimization algorithm (IMOCOA) in solving the problem of co-optimization of tooth profile error and energy consumption in honing processes, it is compared with multi-objective particle swarm optimization (MOPSO), second-generation non-dominated sorting genetic algorithm (NSGA-II), and multi-task constrained multi-objective optimization (MTCMO) algorithms. The Pareto fronts of the four algorithms are as follows: Figure 6 As shown, the Pareto solution set of the proposed IMOCAA algorithm exhibits significant advantages in convergence, distribution, and diversity, almost completely dominating all solutions of the MOPSO, NSGA-II, and MTCMO algorithms. Compared with the other three algorithms, the proposed IMOCAA algorithm has a faster convergence speed, and its Pareto solution set shows better distribution diversity and uniformity in the target space. Optimization results demonstrate that the proposed IMOCAA algorithm has good adaptability to the co-optimization problem of honing process parameters considering profile error and energy consumption, and exhibits superior optimization performance.

[0080] To verify the engineering reliability of the proposed collaborative optimization method, honing experiments were conducted using the process parameters of both the collaborative optimization scheme and the empirical scheme, and corresponding tooth profile shape deviation and energy consumption data were collected. The experimental results for the two schemes are shown in Table 5. The tooth profile shape deviation f of the proposed collaborative optimization scheme... fα The thickness is 0.9 μm, and the processing energy consumption is E. honing 2.09×10 5 Compared to empirical methods, the proposed collaborative optimization scheme reduced the tooth profile deviation by 0.4 μm and decreased machining energy consumption by 0.16 × 10⁻⁶. 5 J. The results show that the proposed method for synergistic optimization of tooth profile error and energy consumption in the internal meshing high-strength honing process can achieve energy-saving honing while ensuring machining accuracy. It has high reliability and practical engineering significance, and can provide decision guidance for the selection of honing process parameters.

[0081]

[0082] This invention proposes a collaborative optimization method for internal meshing high-strength honing process parameters, addressing both profile error and energy consumption. It analyzes the multi-axis linkage machining mechanism and energy consumption characteristics of internal meshing high-strength honing, establishes a coupled model of profile error and energy consumption driven by a hybrid mechanism-data approach, reveals the coupling mechanism between profile error and energy consumption, constructs a collaborative optimization model for honing process parameters oriented towards profile error and energy consumption, and utilizes an improved multi-objective raccoon optimization algorithm to obtain the optimal combination of process parameters for internal meshing high-strength honing. The proposed method effectively reduces profile shape deviation and machining energy consumption in internal meshing high-strength honing, and provides a process parameter decision-making approach for high-precision, high-efficiency, and energy-saving machining of internal meshing high-strength honing.

Claims

1. A method for collaborative optimization of internal meshing high-strength honing process parameters addressing tooth profile error and energy consumption, characterized in that, Includes the following steps: Step 1: Analyze the material removal mechanism of multi-axis linkage machining in the internal meshing high-strength honing process, and clarify the multi-axis energy consumption composition and time characteristics of the honing process. Step 2: Establish a coupling model of tooth profile error and energy consumption in honing process driven by hybrid mechanism data, and reveal the coupling mechanism of tooth profile error and energy consumption in honing process; Step 3: Construct a collaborative optimization model for internal meshing high-strength honing process parameters oriented towards tooth profile error and energy consumption, and perform optimization and solution based on the improved multi-objective raccoon optimization algorithm.

2. The method for collaborative optimization of internal meshing high-strength honing process parameters oriented towards tooth profile error and energy consumption as described in claim 1, characterized in that: The process of analyzing the multi-axis linkage material removal mechanism and energy consumption characteristics of the internal meshing high-strength honing process in step 1 is as follows: (1) Analyze the multi-axis linkage machining process and material removal mechanism of the internal meshing high-strength honing process. The honing machining motion axis includes the honing wheel spindle, the workpiece spindle, the honing wheel axial feed axis, the honing wheel radial feed axis, the axis angle adjustment axis, and the honing wheel swing axis. With the axial reciprocating feed motion of the honing wheel and the continuous rotation of the honing wheel and the workpiece, the abrasive grains on the surface of the honing wheel continuously slide (elastic deformation stage), plow (plastic deformation stage), and cut (material removal stage) on the gear tooth surface of the workpiece, thereby removing the material on the gear tooth surface of the workpiece. (2) Analyze the multi-axis energy consumption composition and time characteristics of the internal meshing high-strength honing process. According to the energy consumption composition characteristics, the processing energy consumption of the internal meshing high-strength honing process consists of auxiliary system energy consumption, no-load energy consumption, material removal energy consumption and additional load energy consumption. Among them, the auxiliary system energy consumption includes the energy consumption of components such as honing machine lighting, hydraulic system, and cooling motor. The no-load energy consumption consists of the no-load energy consumption of each motion axis such as honing wheel spindle, workpiece spindle, honing wheel axial feed axis, honing wheel radial feed axis, axis angle adjustment axis, and honing wheel swing axis. The material removal energy consumption represents the energy consumption generated by the removal of tooth surface material during the meshing process between the honing wheel and the workpiece gear. The additional load energy consumption is the energy consumption of the honing machine tool caused by the fluctuation of honing force and torque. Based on the characteristics of energy consumption during different time periods, the energy consumption of internal meshing high-strength honing process includes standby energy consumption, air-cutting energy consumption, and cutting energy consumption. Standby energy consumption refers to the energy consumption generated by operations such as clamping, pre-honing inspection, and disassembly of the workpiece gear. Air-cutting energy consumption mainly includes the energy consumption generated during the shaft angle adjustment process, axial air feed and radial air feed of the honing wheel, and cutting energy consumption refers to the energy consumption generated by the honing machine during the time period of removing material from the workpiece tooth surface.

3. The method for collaborative optimization of internal meshing high-strength honing process parameters oriented towards tooth profile error and energy consumption as described in claim 1, characterized in that: The modeling process of the coupled honing process tooth profile error and energy consumption driven by hybrid mechanism data in step 2 is as follows: (1) A data-driven mapping model of the honing process tooth profile deviation and process parameters such as the honing wheel spindle speed, honing wheel axial feed rate, axial oscillation distance, and radial honing feed rate is established using the gradient-enhanced kriging method. The established tooth profile deviation model can be expressed as follows: In the formula, f fα For tooth profile deviation, f GEK For gradient-enhanced Kriging models, n h f is the spindle speed of the honing wheel. z L represents the axial feed rate of the honing wheel. Z L is the axial oscillation distance. X This is the radial honing feed rate; (2) Construct a mechanism mapping model for the energy consumption of internal meshing high-strength honing process with respect to process parameters such as the spindle speed of the honing wheel, the axial feed rate of the honing wheel, the axial oscillation distance, and the radial honing feed. The established energy consumption model for internal meshing high-strength honing process can be expressed as follows: In the formula, E honing Energy consumption for honing process, P st For standby power, t st For standby time, P au To assist system power, , , , These represent the no-load power of the internal meshing high-strength honing process shaft, the workpiece spindle, the honing wheel axial feed shaft, and the honing wheel radial feed shaft, respectively. , These represent the motor power and frequency converter power of the honing wheel spindle, respectively, n h Let a be the rotational speed of the honing wheel spindle, and a1 and a2 be the mechanical transmission power loss coefficients of the honing wheel spindle. Similarly, the no-load power of the other shafts is calculated using this formula. mr P is the material removal power. add For additional load power, The angle between the honing wheel and the workpiece shaft, w A Adjust the angular velocity of the shaft rotation to adjust the shaft intersection angle, L Zair L is the axial feed cut length of the honing wheel. Xair F is the radial feed cut length of the honing wheel. Xair L is the radial air feed speed of the honing wheel. safe For a safe distance, L cut For machining allowance, t spc For sparkless honing time; (3) The established mechanism-data hybrid driven honing process tooth profile error and energy consumption coupling model can be expressed as follows: In the formula, f mech This represents the energy consumption mechanism mapping model.

4. The method for collaborative optimization of internal meshing high-strength honing process parameters oriented towards tooth profile error and energy consumption as described in claim 1, characterized in that: Step 3 involves constructing and solving a collaborative optimization model for internal meshing high-strength honing process parameters, considering both tooth profile error and energy consumption. The specific process is as follows: (1) Construct a collaborative optimization model for internal meshing high-strength honing process parameters oriented towards tooth profile deviation and energy consumption: In the formula, n hmin and n hmax These represent the minimum and maximum rotational speeds of the honing wheel spindle, respectively. zmin and f zmax These represent the minimum and maximum axial feed rates, respectively, L Zmin and L Zmax L represents the minimum and maximum axial oscillation distances, respectively. Xmin and L Xmax These represent the minimum and maximum feed rates for radial honing, respectively, n wmin and n wmax These represent the minimum and maximum speeds of the workpiece spindle, β. w F is the helix angle of the workpiece. Xmax The maximum permissible radial feed rate, k1, k2, and k3 are honing force coefficients, b w d is the tooth width of the workpiece. h d is the pitch circle diameter of the honing wheel. w F is the pitch circle diameter of the workpiece. cmax For the maximum allowable honing force, P hmax This refers to the rated power of the honing wheel spindle motor. For the honing wheel spindle motor efficiency; (2) Introducing Circle chaotic mapping and Levy flight strategy, an improved multi-objective raccoon optimization algorithm is proposed, and this improved algorithm is used to optimize the solution; Based on the Circle wonton mapping, candidate solutions for initial honing process parameters, including the honing wheel spindle speed, axial feed rate, axial oscillation distance, and radial honing feed, are constructed as follows: In the formula, z i Let represent the candidate solution for the i-th honing process parameter combination, where mod is the remainder function; Introducing the Levy flight strategy, the candidate solution positions for honing process parameters are updated. The formula for calculating the candidate solution positions is as follows: In the formula, Let r represent the honing process parameter value of the i-th candidate solution in the l-th iteration. coa A random number between [0, 1] and Let L represent the local upper and lower boundaries of the j-th decision variable during the iteration process, respectively, and L be the flight step size.

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