Energy consumption modeling and process parameter optimization method for internal gearing powerful gear honing machining

Abstract

The invention discloses an internal gearing powerful gear honing machining energy consumption modeling and process parameter optimizing method which comprises the following steps: firstly, analyzing energy consumption composition characteristics and time period characteristics of an internal gearing powerful gear honing process, and revealing an energy dissipation mechanism of the internal gearing powerful gear honing process; secondly, the multi-axis linkage and feeding process of the internal meshing powerful gear honing technology is analyzed, and an energy consumption model of the internal meshing powerful gear honing machining technology is established; and finally, the mapping relation between gear honing machining time and the technological parameters is analyzed, a machining energy consumption and time-oriented multi-target optimization model of the internal meshing powerful gear honing technological parameters is constructed, an improved multi-target atomic orbit search algorithm is used for optimization solution, and an optimal combination scheme of the internal meshing powerful gear honing technological parameters is obtained. And the processing energy consumption of the internal meshing powerful gear honing process is reduced.

Description

Technical Field

[0001] The invention relates to the field of mechanical cutting processing, and in particular to an energy consumption modeling and parameter optimization method for an internal meshing power honing process. Background Art

[0002] CNC machine tools are widely used in various fields of manufacturing. Their total energy consumption is large and their energy efficiency is low. Improving the energy utilization rate of machine tools is an effective way to promote carbon peak and carbon neutrality. The internal meshing high-power honing process can effectively reduce gear meshing noise and increase service life, becoming a key processing technology for high-speed and low-noise gears in the electric drive system of new energy vehicles. In the internal meshing high-power honing process, multiple axes are involved in the movement, the honing speed is high, the rated total power reaches 60 to 80kW, and the energy-saving potential is huge. Therefore, the research on energy consumption modeling and parameter optimization methods of the internal meshing high-power honing process is of great significance to the green transformation and upgrading of the manufacturing industry.

[0003] At present, most of the research on energy consumption modeling of CNC machine tools is aimed at turning and milling processes, and a few studies are about gear hobbing processes, while there are few studies on energy consumption modeling of internal meshing power honing processes. The internal meshing power honing process has complex multi-axis linkage trajectories, high spindle speed, and a rated total power of the honing process as high as 60 to 80 kW, with great energy-saving potential. Therefore, the present invention starts from the energy consumption modeling and parameter optimization method of the internal meshing power honing process, analyzes the energy consumption characteristics of the internal meshing power honing process, reveals the energy dissipation mechanism of the honing process, analyzes the multi-axis linkage and feeding process of the honing process, establishes an energy consumption model of the internal meshing power honing process, constructs a multi-objective optimization model of honing process parameters for processing energy consumption and time, and uses an improved multi-objective atomic orbital search algorithm for optimization and solution, and proposes an internal meshing power honing process parameter optimization decision method. Summary of the invention

[0004] The present invention provides an internal meshing high-power gear honing process energy consumption modeling and process parameter optimization method, so as to optimize the process parameters and thus reduce the processing energy consumption of the internal meshing high-power gear honing process.

[0005] The technical solution adopted to achieve the purpose of the present invention is as follows, that is, a method for energy consumption modeling and process parameter optimization of internal meshing power honing, comprising the following steps:

[0006] Step 1: Analyze the energy consumption composition and time characteristics of the internal meshing power honing process to reveal the energy dissipation mechanism of the internal meshing power honing process;

[0007] Step 2: Analyze the multi-axis linkage and feeding process of the internal meshing power honing process, and establish the energy consumption model of the internal meshing power honing process;

[0008] Step 3: Establish a multi-objective optimization model for the process parameters of internal meshing power honing for processing energy consumption and time, and perform optimization solution based on the improved multi-objective atomic orbital search algorithm.

[0009] Preferably, the energy consumption composition and time period characteristic curve of the internal meshing power honing process in step 1 are as follows: Figure 1 As shown, the specific analysis process is:

[0010] (1) The energy consumption characteristics of the internal meshing power honing process are analyzed. The total energy consumption of the internal meshing power honing process is composed of auxiliary system energy consumption, no-load energy consumption, material removal energy consumption and additional load loss, which can be expressed as:

[0011] E total =E au +E u +E mr +E add

[0012] In the formula, E total is the total energy consumption of gear honing process, E au represents the energy consumption of auxiliary system, E u Indicates no-load energy consumption, E mr Represents the energy consumption for material removal, E add Indicates additional load loss.

[0013] The energy consumption of auxiliary systems accompanies the entire gear honing process, including the energy consumption of startup-related auxiliary systems and the energy consumption of processing-related auxiliary systems. The energy consumption of startup-related auxiliary systems refers to the energy consumption generated by auxiliary components that are started when the machine tool is turned on, including CNC systems, lighting, etc.; the energy consumption of processing-related auxiliary systems refers to the energy consumption generated by auxiliary components related to the material removal process, including hydraulic systems, motor inverters, servo drives, etc. Therefore, the energy consumption of the auxiliary system of the internal meshing power honing process can be expressed as:

[0014] E au =E aus +E auc

[0015] In the formula, E aus Indicates the energy consumption of starting the associated auxiliary system, E auc Represents the energy consumption of processing-related auxiliary systems.

[0016] (2) The energy consumption period characteristics of the internal meshing power honing process are analyzed. According to the period characteristics, the total energy consumption of the internal meshing power honing process includes standby energy consumption, air cutting energy consumption and cutting energy consumption, which can be expressed as:

[0017]

[0018] In the formula, E st、E air 、E cut They represent the standby energy consumption, air cutting energy consumption and cutting energy consumption of the gear honing process, respectively. st Indicates standby power, P air Indicates the air-cut power, which is mainly composed of auxiliary system power and no-load power. cut It represents cutting power, which is mainly composed of auxiliary system power, no-load power, material removal power and additional load power. st ,t air ,t cut They represent the waiting time, idle cutting time and cutting time respectively.

[0019] Preferably, the energy consumption modeling process of the internal meshing power honing process in step 2 is:

[0020] (1) Analyze the multi-axis linkage and feeding process of the internal meshing power honing process. The structure of the internal meshing power honing machine proposed in the present invention is as follows: Figure 2 As shown in the figure, the internal meshing high-power honing process involves a total of 6 axes (C1, C2, A, B, X, and Z). The C1 and C2 axes are the honing wheel (tool) and workpiece rotation axes, respectively. The A axis is the axis angle adjustment axis. The B axis is the honing wheel swing axis to achieve tooth modification. The X axis and Z axis are the radial and axial feed axes of the honing wheel, respectively.

[0021] The multi-axis linkage and feeding process of the internal meshing high-power honing process is as follows: First, the A-axis rotates an axis angle to ensure that the honing wheel and the workpiece can mesh, and the honing wheel spindle (C1 axis) and the workpiece spindle (C2 axis) are immediately started and maintain a meshing motion relationship; secondly, the honing wheel moves axially along the Z-axis to the vicinity of the workpiece tooth top; then, the honing wheel moves radially along the X-axis quickly for a distance to approach the workpiece, and the honing wheel moves radially along the X-axis slowly for a safe distance to the meshing point; then, the Z-axis starts to oscillate left and right, the B-axis starts to swing left and right, and the honing wheel moves radially along the X-axis by a machining allowance to remove material from the tooth surface; finally, the honing wheel continues to oscillate left and right along the Z-axis to achieve spark-free cutting. After the spark-free cutting is completed, the X-axis and Z-axis quickly retract to the initial position, and the spindle also stops accordingly. After analysis, it is determined that the motion axes involved in the air cutting period are A, C1, C2, Z, and X, and the motion axes involved in the cutting period are A, C1, C2, Z, X, and B.

[0022] (2) Model the standby energy consumption, air cutting energy consumption, and cutting energy consumption respectively, and construct a mapping model of the total energy consumption of the internal meshing power honing process with respect to process parameters such as the honing wheel speed, axial oscillation distance, axial feed rate, and radial feed rate. The total energy consumption model of the internal meshing power honing process can be expressed as follows:

[0023]

[0024] In the formula, Respectively represent the no-load power of C1 axis, C2 axis, A axis, B axis, X axis and Z axis in the internal meshing power honing process, P au is the auxiliary system power, P mr is the material removal power, P add is the additional load power, ∑ hw is the axial angle between the honing wheel and the workpiece, w A is the angular velocity of A axis, L Zair is the Z-axis air cutting length, f z is the axial feed rate, n h is the speed of the honing wheel, L1 is the fast feed air cutting length of the X-axis, F Xair is the X-axis air cutting feed speed, L safe is the safety distance, L Z is the axial oscillation distance, L X is the radial feed amount, L cut is the honing machining allowance, t spc It is the spark-free cutting time.

[0025] The no-load power of each axis is mainly composed of motor power, inverter / driver power, and mechanical transmission power. For example, the no-load power of the honing wheel spindle (C1 axis) is:

[0026]

[0027] In the formula, is the C1 axis motor power, is the inverter power of the C1 axis motor, a1 and a2 are the mechanical transmission power coefficients. The no-load power of other axes is similar, and the movement of the B axis is only a few microns, so the no-load power can be ignored.

[0028] The material removal power of the internal gear honing process is:

[0029]

[0030] In the formula, F c is the honing force, v is the honing speed, k1, k2, k3 are the honing force coefficients, b w is the workpiece tooth width, d hw is the equivalent honing wheel diameter.

[0031] Preferably, in step 3, a multi-objective optimization model of the process parameters of the internal meshing power honing gear for processing energy consumption and time is established and solved, and the modeling and optimization solution process is as follows: Figure 3 As shown, the specific process is as follows:

[0032] (1) A mapping model of the internal meshing power honing process processing time with respect to the honing wheel speed, axial oscillation distance, axial feed rate, and radial feed amount is established. The constructed honing process processing time model can be expressed as:

[0033]

[0034] The air cutting time is composed of the air cutting time of the A axis, Z axis and X axis, which can be expressed as:

[0035]

[0036] In the formula, are the air cutting time of A axis, Z axis and X axis respectively, Z is the axial feed speed of the honing wheel, F Z =f z ·n h , F X is the radial feed speed of the honing wheel,

[0037] Cutting time t cut Cutting time t for material removal mr and spark-free cutting time t spc The material removal cutting time can be expressed as:

[0038]

[0039] (2) Establish a multi-objective optimization model for the process parameters of internal meshing power honing for processing energy consumption and time:

[0040] minF(n h ,f z ,L Z ,L X )=(min E total ,min T total )

[0041]

[0042] Where n hmin and n hmax are the minimum and maximum speeds of the honing wheel, respectively, and f zmin and f zmax are the minimum and maximum axial feed rates, L Zmin and L Zmax are the minimum and maximum axial oscillation distances, L Xmin and L Xmax are the minimum and maximum radial feed, n wmin and n wmax are the minimum and maximum rotation speeds of the workpiece, respectively, Xmaxis the maximum permissible radial feed speed, F cmax is the maximum allowable honing force, P hmax is the rated power of the honing wheel spindle motor, and η is the honing wheel spindle motor efficiency.

[0043] (3) Positrons are added to the candidate solutions of the atomic orbital search (AOS) algorithm, and an improved multi-objective atomic orbital search (IMOAOS) algorithm is proposed, which is used to optimize the solution.

[0044] Each electron represents a candidate solution. The m candidate solutions of the multi-objective optimization model of gear honing process can be expressed as:

[0045] X=[X1,X2,…,X i ,…,X m ] T ,X i =[n h ,f z ,L Z ,L X ]

[0046] The opposite of each candidate solution (electron) in the search space is defined as a positron, and the candidate solution corresponding to the positron can be expressed as:

[0047] X′ i =X max +X min -X i

[0048] Where, X max and X min are the minimum and maximum values ​​of the candidate solutions (electrons), respectively.

[0049] The objective function value of the candidate solution (electron) is:

[0050]

[0051] The objective function value corresponding to the positron is E′ i =F(X′ i ).

[0052] The n virtual layers are used to simulate the transition behavior of electrons around the nucleus. The candidate solution and objective function value of the kth virtual layer can be expressed as:

[0053]

[0054] In the formula, represents the i-th candidate solution of the k-th virtual layer, p represents the total number of candidate solutions of the k-th virtual layer, represents the i-th objective function value of the k-th virtual layer. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Energy consumption composition characteristics and time period characteristic curve of internal meshing power honing process

[0056] Figure 2 Internal meshing powerful gear honing machine structure

[0057] Figure 3 Energy consumption modeling and optimization process of internal gear honing process

[0058] Figure 4 Fitting effect of no-load power of each axis in internal meshing power honing process

[0059] Figure 5 Effect of process parameters on energy consumption in internal gear honing

[0060] Figure 6 Pareto frontiers of different optimization algorithms DETAILED DESCRIPTION

[0061] The present invention is further described below in conjunction with the accompanying drawings and embodiments, but it should not be understood that the above subject matter of the present invention is limited to the following embodiments. Without departing from the above technical ideas of the present invention, various substitutions and changes are made according to common technical knowledge and customary means in the field, which should all be included in the protection scope of the present invention.

[0062] In this embodiment, a gear honing experiment is carried out using a company's internal meshing high-power honing machine as a platform, and a power sensor is used to collect the real-time power of the machine tool. The experimental operating parameters are shown in Table 1.

[0063] Table 1 Experimental operating parameters

[0064]

[0065]

[0066] According to the results of energy consumption characteristics analysis, the idling power of each shaft in the internal meshing power honing process consists of no-load power and standby power. The standby power is usually a fixed value. The standby power P st = 1880W. The standby time and spark-free cutting time are usually fixed. The standby time t st =12s, spark-free cutting time t spc =3s. In order to obtain the no-load power of each axis, 9 different groups of honing spindle speeds and feed rates were set, and then the least squares method was used to fit these experimental data to obtain the no-load power model of each axis.

[0067] Taking the honing wheel spindle (C1 axis), workpiece spindle (C2 axis), X axis and Z axis as examples, the no-load power fitting effect of each axis is as follows: Figure 4To evaluate the fitting accuracy of these no-load power models, the root mean square error (RMSE) and the coefficient of determination (R 2 ) as the evaluation index, the smaller the RMSE and the larger the R 2 This indicates that the fitting accuracy is higher. Figure 4 It can be seen that the measured values ​​of the no-load power of each axis almost coincide with the fitting curve, and are basically all located on the fitting curve, which shows that the established no-load power model has excellent fitting performance and interpretability. In addition, the RMSE of the no-load power fitting model of the C1 axis, C2 axis, X axis, and Z axis are 53.043, 2.410, 0.709, and 1.083, respectively. Compared with the no-load power measurement value, the RMSE of each power model is very small, and the R 2 They are also higher than 0.99, which shows that the established no-load power model has high fitting accuracy and credibility.

[0068] In order to evaluate the modeling accuracy of the total energy consumption of the internal meshing power honing process, seven experiments were carried out with different process parameters, and the energy consumption model calculation values ​​and energy consumption measurement values ​​were collected. The results are shown in Table 2. Residuals and accuracy (accuracy = model calculation value / measurement value) are introduced to judge the degree of deviation. It is noted that the energy consumption measurement value is greater than the energy consumption model calculation value. This is because the actual measurement value includes the energy consumption during startup and spindle acceleration / deceleration, and the established energy consumption model ignores this part of the energy consumption. It can be seen from Table 2 that the residuals of each experiment are very small, and the accuracy is above 98%, which shows that the established internal meshing power honing process energy consumption model has high modeling accuracy and fitting performance, and is reliable for energy consumption modeling of honing process.

[0069] Table 2 Results and performance of energy consumption modeling for internal meshing power honing process

[0070]

[0071] The influence of internal meshing power honing process parameters on energy consumption is as follows Figure 5 As shown in Figure 2, it can be seen that the energy consumption of gear honing increases with the honing wheel speed n. h and axial oscillation distance L Z increases with the increase of the axial feed rate f z and radial feed L X In addition, the steepness of the surface reflects the influence of the honing process parameters on energy consumption. Figure 5 It can be seen that n h The impact on energy consumption is greater than f z , L Z The impact on energy consumption is greater than L X , L Z The impact on energy consumption is greater than n h, f z The impact on energy consumption is greater than L X Therefore, the axial oscillation distance is the most important factor affecting the energy consumption of gear honing, followed by the honing wheel speed, axial feed rate and radial feed.

[0072] In order to verify the necessity of multi-objective optimization, it is compared with a single energy consumption or time optimization scheme. In addition, to verify the practicality, the empirical scheme is also added to the comparison. The optimization results of different optimization schemes are shown in Table 3. As can be seen from the table, the minimum processing energy consumption and time of the multi-objective optimization scheme are 1.43×10 5 J and 30.34s. Compared with optimizing only energy consumption, the energy consumption of the multi-objective optimization scheme increased by 9.16%, but the processing time was shortened by 15.13%. Compared with optimizing only time, the energy consumption of the multi-objective optimization scheme decreased by 60.93%, while the processing time increased by only 7.13%. In addition, compared with the empirical scheme, the energy consumption of the proposed multi-objective optimization scheme was reduced by 1.19×10 5 J, the processing time was reduced by 2.81s, and the energy consumption and processing time were reduced by 45.42% and 8.48%, respectively. Therefore, the proposed multi-objective optimization scheme effectively reduced the energy consumption and time of gear honing and provided a solution for the selection of gear honing process parameters.

[0073] Table 3 Optimization results of different optimization schemes

[0074]

[0075] In order to evaluate the performance of the proposed improved multi-objective atomic orbital search (IMOAOS) algorithm for solving the multi-objective optimization problem of gear honing process parameters, it is compared with the multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II) and constrained two-archive evolutionary algorithm (CTAEA). Figure 6 As shown in the figure, it can be seen that the Pareto solution set of IMOAOS almost completely dominates all the solutions of MOPSO, NSGA-II and CTAEA, and the distribution of Pareto solutions is more uniform, which proves that the proposed IMOAOS algorithm has superior solution performance and can better solve the multi-objective optimization problem of process parameters of internal meshing power honing.

[0076] In order to verify the reliability of the optimization results, the optimal process parameters obtained by single-objective optimization and multi-objective optimization were used to carry out gear honing experiments, and the processing energy consumption under three conditions was measured. The processing experimental results of different optimization objectives are shown in Table 4. It can be found that the error rates of the three schemes are all within 5%, indicating that the optimal process parameter scheme of multi-objective optimization is suitable and feasible. Therefore, the proposed energy consumption modeling and process parameter optimization method for internal meshing power honing has high reliability and practicality.

[0077] Table 4 Processing experimental results of different optimization schemes

[0078]

[0079] The present invention proposes a method for energy consumption modeling and process parameter optimization of internal meshing power honing, analyzes the energy consumption composition characteristics and time characteristics of the internal meshing power honing process, establishes an energy consumption model of the internal meshing power honing process, constructs a multi-objective optimization model of honing process parameters for processing energy consumption and time, and uses an improved multi-objective atomic orbital search algorithm for optimization and solution to obtain the optimal combination of internal meshing power honing process parameters. The proposed method effectively reduces the processing energy consumption of the internal meshing power honing process and provides a decision-making idea for the selection of honing process parameters.

Claims

1. A method for energy consumption modeling and process parameter optimization of internal meshing power honing, characterized in that: The following steps are involved: Step 1: Analyze the energy consumption composition and time characteristics of the internal meshing power honing process to reveal the energy dissipation mechanism of the internal meshing power honing process; Step 2: Analyze the multi-axis linkage and feeding process of the internal meshing power honing process, and establish the energy consumption model of the internal meshing power honing process; Step 3: Establish a multi-objective optimization model for the process parameters of internal meshing power honing for processing energy consumption and time, and perform optimization solution based on the improved multi-objective atomic orbital search algorithm.

2. The method for energy consumption modeling and process parameter optimization of internal meshing power honing according to claim 1, characterized in that: The process of analyzing the energy consumption composition and time characteristics of the internal meshing power honing process in step 1 is as follows: (1) The energy consumption characteristics of the internal meshing power honing process are analyzed. The total energy consumption of the internal meshing power honing process is composed of auxiliary system energy consumption, no-load energy consumption, material removal energy consumption and additional load loss, which can be expressed as: AND total =And au +E u +E mr +E add In the formula, E total is the total energy consumption of gear honing process, E au represents the energy consumption of auxiliary system, E u Indicates no-load energy consumption, E mr Represents the energy consumption for material removal, E add Indicates additional load loss; (2) The energy consumption period characteristics of the internal meshing power honing process are analyzed. According to the period characteristics, the total energy consumption of the internal meshing power honing process includes standby energy consumption, air cutting energy consumption and cutting energy consumption, which can be expressed as: In the formula, E st 、E air 、E cut They represent the standby energy consumption, air cutting energy consumption and cutting energy consumption of the gear honing process, respectively. st Indicates standby power, P air Indicates the air-cut power, which is mainly composed of auxiliary system power and no-load power. cut It represents cutting power, which is mainly composed of auxiliary system power, no-load power, material removal power and additional load power. st ,t air ,t cut They represent the waiting time, idle cutting time and cutting time respectively.

3. The method for energy consumption modeling and process parameter optimization of internal meshing power honing according to claim 1, characterized in that: The energy consumption modeling process of the internal meshing power honing process in step 2 is: (1) The internal meshing high-power honing process proposed in the present invention involves a total of 6 axes (C1, C2, A, B, X, and Z). The C1 axis and C2 axis are the rotation axes of the honing wheel (tool) and the workpiece, respectively. The A axis is the axis angle adjustment axis. The B axis is the honing wheel swing axis to achieve tooth profile modification. The X axis and Z axis are the radial and axial feed axes of the honing wheel, respectively. The multi-axis linkage and feed process of the high-power honing process are analyzed to determine the motion axes involved in the standby period, air cutting period, and cutting period. (2) Model the standby energy consumption, air cutting energy consumption, and cutting energy consumption respectively, and construct a mapping model of the total energy consumption of the internal meshing power honing process with respect to process parameters such as the honing wheel speed, axial oscillation distance, axial feed rate, and radial feed rate. The total energy consumption model of the internal meshing power honing process can be expressed as follows: In the formula, Respectively represent the no-load power of C1 axis, C2 axis, A axis, B axis, X axis and Z axis in the internal meshing power honing process, P au is the auxiliary system power, P mr is the material removal power, P add is the additional load power, ∑ hw is the axial angle between the honing wheel and the workpiece, w A is the angular velocity of A axis, L Zair is the Z-axis air cutting length, f z is the axial feed rate, n h is the speed of the honing wheel, L1 is the fast feed air cutting length of the X-axis, F Xair is the X-axis air cutting feed speed, L safe is the safety distance, L Z is the axial oscillation distance, L X is the radial feed amount, L cut is the machining allowance, t spc It is the spark-free cutting time.

4. The method for energy consumption modeling and process parameter optimization of internal meshing power honing according to claim 1, characterized in that: In step 3, a multi-objective gear honing process parameter optimization model for processing energy consumption and time is established and solved. The specific process is as follows: (1) A mapping model of the internal meshing power honing process processing time with respect to the honing wheel speed, axial oscillation distance, axial feed rate, and radial feed amount is established. The constructed honing process processing time model can be expressed as: (2) Establish a multi-objective optimization model for the process parameters of internal meshing power honing for processing energy consumption and time: Where n hmin and n hmax are the minimum and maximum speeds of the honing wheel, respectively, and f zmin and f zmax are the minimum and maximum axial feed rates, L Zmin and L Zmax are the minimum and maximum axial oscillation distances, L Xmin and L Xmax are the minimum and maximum radial feed, n wmin and n wmax are the minimum and maximum rotation speeds of the workpiece, respectively, Xmax is the maximum permissible radial feed speed, F cmax is the maximum allowable honing force, P hmax is the rated power of the honing wheel spindle motor, η is the honing wheel spindle motor efficiency; (3) Add positrons to the candidate solutions of the atomic orbital search algorithm, propose an improved multi-objective atomic orbital search algorithm, and use this improved algorithm to optimize the solution; Each electron represents a candidate solution. The m candidate solutions of the multi-objective optimization model of gear honing process can be expressed as: X=[X1,X2,…,X i ,…,X m ] T ,X i [n h ,f z ,L Z ,L X ] The opposite of each candidate solution (electron) in the search space is defined as a positron, and the candidate solution corresponding to the positron can be expressed as: X i ′=X max +X min -X i Where, X max and X min are the minimum and maximum values ​​of the candidate solutions (electrons), respectively; The objective function value of the candidate solution (electron) is: The objective function value corresponding to the positron is E i ′=F(X i ′).

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