Process parameter optimization method and system for evaluating grinding surface quality based on force signal

Abstract

The application discloses a process parameter optimization method and system for evaluating grinding surface quality based on a force signal, and relates to the field of grinding intelligent machining. The method comprises the following steps: acquiring a grinding force signal and surface roughness; determining a friction coefficient and effective machining time according to the grinding force signal; determining a multivariate nonlinear numerical model of the surface roughness, the friction coefficient and the effective machining time with respect to grinding input parameters respectively; determining a multi-objective numerical function model according to the multivariate nonlinear numerical model; optimizing a weight vector of each sub-objective function in the multi-objective numerical function model to obtain an optimized multi-objective numerical function model; and optimizing the grinding input parameters in the optimized multi-objective numerical function model to obtain optimal grinding input parameters and determine the best value range of the friction coefficient. The application can determine reasonable grinding machining parameters, and improve the grinding machining efficiency on the premise of guaranteeing excellent machining quality.

Description

Technical Field

[0001] This invention relates to the field of intelligent grinding, and in particular to a method and system for optimizing process parameters based on force signals to evaluate the surface quality of grinding. Background Technology

[0002] Most existing methods use BP (BackPropagation) neural networks or multi-objective optimization algorithms to optimize grinding input parameters, predict surface roughness, or predict effective processing time.

[0003] Since existing methods or technologies are basically based on the mapping relationship of massive data to build models, they lack detailed measurement and evaluation of the microscopic distribution of data points on the surface profile curve. This leads to random errors, which can easily result in small surface roughness values ​​but large differences in the distribution of data points, resulting in poor overall surface quality performance. Therefore, their guiding significance in actual processing is limited.

[0004] Therefore, there is an urgent need to provide a method to solve the problems of unreasonable grinding parameters, poor surface finish, and low efficiency in the grinding of difficult-to-machine materials. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for optimizing process parameters based on force signals to evaluate the surface quality of grinding, so as to determine reasonable grinding parameters and improve grinding efficiency while ensuring excellent processing quality.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A method for optimizing process parameters to evaluate the surface quality of grinding based on force signals, the method comprising:

[0008] Acquire grinding force signals and surface roughness;

[0009] The friction coefficient and effective processing time are determined based on the grinding force signal.

[0010] Multivariate nonlinear regression analysis was used to determine the multivariate nonlinear numerical models of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters, including: grinding wheel speed, workpiece feed rate, and grinding depth.

[0011] Based on the multivariate nonlinear numerical model of the surface roughness, the friction coefficient, and the effective processing time, a multi-objective numerical function model is determined.

[0012] A decomposition-based multi-objective evolutionary algorithm is used to optimize the weight vectors of each sub-objective function in the multi-objective numerical function model to obtain an optimized multi-objective numerical function model. The optimized multi-objective numerical function model includes: a first optimized model optimized with the objectives of minimizing surface roughness, minimizing friction coefficient, and minimizing effective processing time, and a second optimized model optimized with the objectives of minimizing surface roughness, maximizing friction coefficient, and minimizing effective processing time.

[0013] A genetic algorithm is used to optimize the grinding input parameters in the optimized multi-objective numerical function model to obtain the optimal grinding input parameters and determine the optimal range of friction coefficient values. The optimal grinding input parameters include: the first set of grinding input parameters corresponding to the first optimization model and the second set of grinding input parameters corresponding to the second optimization model.

[0014] Optionally, the friction coefficient and effective processing time are determined based on the grinding force signal, specifically including:

[0015] The effective machining stage force signal is determined based on the grinding force signal; the effective machining stage force signal includes: effective tangential grinding force signal and effective normal grinding force signal;

[0016] The friction coefficient and effective processing time are determined based on the force signal of the effective processing stage.

[0017] Optionally, determining the effective machining stage force signal based on the grinding force signal specifically includes:

[0018] The grinding force signal is subjected to drift compensation, removal of incomplete contact force signals, and filtering and noise reduction to obtain a preprocessed grinding force signal;

[0019] Determine the minimum effective normal force value and the maximum effective tangential force value;

[0020] The effective normal grinding force signal in the preprocessed grinding force signal is determined based on the minimum effective normal force value;

[0021] The effective tangential grinding force signal in the preprocessed grinding force signal is determined based on the maximum effective tangential force value.

[0022] Optionally, the friction coefficient and effective processing time are determined based on the force signal of the effective processing stage, specifically including:

[0023] Calculate the mean value of the effective tangential grinding force signal;

[0024] Calculate the mean value of the effective normal grinding force signal;

[0025] The friction coefficient is determined based on the ratio of the mean of the effective tangential grinding force signal to the mean of the effective normal grinding force signal.

[0026] Determine the sampling frequency and the total number of data points for the force signal in the effective processing stage;

[0027] The effective processing time is determined based on the ratio of the total number of data points to the sampling frequency.

[0028] Optionally, multivariate nonlinear regression analysis is employed to determine the multivariate nonlinear numerical models of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters, specifically including:

[0029] Nonlinear function models of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters are established respectively.

[0030] Taking the logarithm of both ends of each of the nonlinear function models yields the corresponding linear function model; the linear function model and the nonlinear function model have the same parameters to be determined.

[0031] Each of the linear function models is fitted to obtain the values ​​of the parameters to be determined.

[0032] Substituting the values ​​of each parameter to be determined into the corresponding nonlinear function model, we obtain a multivariate nonlinear numerical model of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters.

[0033] Optionally, a decomposition-based multi-objective evolutionary algorithm is used to optimize the weight vectors of each sub-objective function in the multi-objective numerical function model to obtain an optimized multi-objective numerical function model, specifically including:

[0034] A decomposition-based multi-objective evolutionary algorithm is used to optimize the weight vectors of each sub-objective function in the multi-objective numerical function model with the objectives of minimizing surface roughness, minimizing friction coefficient, and minimizing effective processing time, thereby obtaining the first set of weight vector proportions and the corresponding first optimization model.

[0035] A decomposition-based multi-objective evolutionary algorithm is used to optimize the weight vectors of each sub-objective function in the multi-objective numerical function model with the objectives of minimizing surface roughness, maximizing friction coefficient, and minimizing effective processing time, thereby obtaining the proportion of the second set of weight vectors and the corresponding second optimization model.

[0036] Optionally, a genetic algorithm is used to optimize the grinding input parameters in the optimized multi-objective numerical function model to obtain the optimal grinding input parameters and determine the optimal range of the friction coefficient, specifically including:

[0037] A genetic algorithm is used to optimize the grinding input parameters in the optimized multi-objective numerical function model to obtain the optimal grinding input parameters.

[0038] A genetic algorithm is used to determine the optimal range of friction coefficient values ​​based on the proportion of the two weight vectors of the friction coefficient in the optimized multi-objective numerical function model.

[0039] Grinding tests were conducted to determine the surface roughness, friction coefficient, effective processing time, and surface profile curve under the optimal grinding input parameters.

[0040] The uniformity and periodicity of the data point distribution of the surface profile curve are evaluated using the coefficient of variation and autocorrelation function.

[0041] A process parameter optimization system for evaluating grinding surface quality based on force signals, the system comprising:

[0042] The data acquisition module is used to acquire grinding force signals and surface roughness.

[0043] A friction coefficient and effective processing time determination module is used to determine the friction coefficient and effective processing time based on the grinding force signal;

[0044] The multivariate nonlinear numerical model determination module is used to determine the multivariate nonlinear numerical models of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters using multivariate nonlinear regression analysis; the grinding input parameters include: grinding wheel speed, workpiece feed rate, and grinding depth;

[0045] The multi-objective numerical function model determination module is used to determine the multi-objective numerical function model based on the surface roughness, the friction coefficient, and the effective processing time using a multivariate nonlinear numerical model.

[0046] The multi-objective numerical function model optimization module is used to optimize the weight vectors of each sub-objective function in the multi-objective numerical function model using a decomposition-based multi-objective evolutionary algorithm to obtain an optimized multi-objective numerical function model. The optimized multi-objective numerical function model includes: a first optimized model optimized with the objectives of minimizing surface roughness, minimizing friction coefficient, and minimizing effective processing time, and a second optimized model optimized with the objectives of minimizing surface roughness, maximizing friction coefficient, and minimizing effective processing time.

[0047] The process parameter optimization module is used to optimize the grinding input parameters in the optimized multi-objective numerical function model using a genetic algorithm to obtain the optimal grinding input parameters and determine the optimal range of friction coefficients. The optimal grinding input parameters include: a first set of grinding input parameters corresponding to the first optimization model and a second set of grinding input parameters corresponding to the second optimization model.

[0048] Optionally, the multivariate nonlinear numerical model determination module specifically includes:

[0049] The nonlinear function model building unit is used to build nonlinear function models of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters, respectively.

[0050] A linear transformation unit is used to take the logarithm of both ends of each of the nonlinear function models to obtain the corresponding linear function model; the linear function model and the nonlinear function model have the same parameters to be determined;

[0051] The function fitting unit is used to fit each of the linear function models to obtain the values ​​of each parameter to be determined.

[0052] The multivariate nonlinear numerical model determination unit is used to substitute the values ​​of each parameter to be determined into the corresponding nonlinear function model to obtain the multivariate nonlinear numerical model of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters.

[0053] Optionally, the process parameter optimization module specifically includes:

[0054] The optimal grinding input parameter determination unit is used to optimize the grinding input parameters in the optimized multi-objective numerical function model using a genetic algorithm to obtain the optimal grinding input parameters.

[0055] The optimal range of friction coefficient is determined by using a genetic algorithm to determine the optimal range of friction coefficient based on the proportion of the two weight vectors of friction coefficient in the optimized multi-objective numerical function model.

[0056] The test analysis unit is used to conduct grinding tests to determine the surface roughness, friction coefficient, effective processing time, and surface profile curve under the optimal grinding input parameters.

[0057] An evaluation unit is used to evaluate the uniformity and periodicity of the data point distribution of the surface profile curve using the coefficient of variation and autocorrelation function.

[0058] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0059] This invention provides a method for optimizing process parameters based on force signals to evaluate the surface quality of grinding. The method determines the friction coefficient and effective processing time based on the grinding force signals from grinding experiments. It employs multivariate nonlinear regression analysis to determine multivariate nonlinear numerical models of surface roughness, friction coefficient, and effective processing time with respect to grinding input parameters. A multi-objective numerical function model is constructed, and a decomposition-based multi-objective evolutionary algorithm is used to optimize the weight vectors of each sub-objective function in the multi-objective numerical function model. Finally, a genetic algorithm is used to determine the optimal range of grinding input parameters and the friction coefficient. Because this invention uses surface roughness, friction coefficient, and effective processing time as optimization objectives to determine grinding parameters, and verifies this by examining the uniformity and periodicity of the data point distribution on the surface profile curve, it can improve grinding efficiency while ensuring excellent processing quality. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 A flowchart of the process parameter optimization method for evaluating grinding surface quality based on force signals provided by the present invention;

[0062] Figure 2 The overall structure diagram of the process parameter optimization method for evaluating grinding surface quality based on force signals provided by the present invention;

[0063] Figure 3 A block diagram of the process parameter optimization system for evaluating grinding surface quality based on force signals provided by this invention.

[0064] Symbol explanation:

[0065] Data acquisition module-1, friction coefficient and effective processing time determination module-2, multivariate nonlinear numerical model determination module-3, multi-objective numerical function model determination module-4, multi-objective numerical function model optimization module-5, process parameter optimization module-6. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] The purpose of this invention is to provide a method and system for optimizing process parameters based on force signals to evaluate the surface quality of grinding, so as to determine reasonable grinding parameters and improve grinding efficiency while ensuring excellent processing quality.

[0068] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0069] Example 1

[0070] This embodiment provides a method for optimizing process parameters to evaluate the surface quality of grinding based on force signals. For example... Figure 1 and Figure 2 As shown, the method includes:

[0071] Step S1: Acquire grinding force signal and surface roughness R a .

[0072] Specifically, during the grinding test, a force gauge is used to acquire the grinding force signal online, and a surface roughness measuring instrument is used to acquire the surface profile curve and roughness offline.

[0073] Step S2: Determine the friction coefficient and effective processing time based on the grinding force signal.

[0074] Step S2 specifically includes:

[0075] Step S2.1: Determine the effective machining stage force signal based on the grinding force signal; the effective machining stage force signal includes: effective tangential grinding force signal and effective normal grinding force signal.

[0076] Step S2.1 specifically includes:

[0077] Step S2.1.1: Perform drift compensation, removal of incomplete contact force signals, and filtering and noise reduction on the grinding force signal to obtain the preprocessed grinding force signal.

[0078] Drift compensation: The accumulation of charge in the cable causes the force signal to drift. Specifically, Dynoware software is used to compensate for drift at the beginning and end points of the force signal drift.

[0079] Removal of insufficient contact force signals: At the beginning and end of the grinding process, the contact between the grinding wheel and the workpiece is insufficient, and the force signals generated during this process are considered as insufficient contact. Therefore, these insufficient contact signals need to be removed.

[0080] Filtering and denoising: The force signal in the grinding process includes the effective force signal when the grinding wheel is in contact with the workpiece and the ineffective force signal when the grinding wheel is not in contact with the workpiece. By using Discrete Fourier Transform in the ineffective signal stage, the low-pass filter value of the force signal for the entire grinding process can be determined, thereby removing the influence of noise in the grinding process on the force signal. This is called filtering and denoising.

[0081] Step S2.1.2: Determine the minimum effective normal force value and the maximum effective tangential force value.

[0082] Step S2.1.3: Determine the effective normal grinding force signal in the preprocessed grinding force signal based on the minimum effective normal force value.

[0083] Step S2.1.4: Determine the effective tangential grinding force signal in the preprocessed grinding force signal based on the maximum effective tangential force value.

[0084] Step S2.2: Determine the friction coefficient and effective processing time based on the force signal of the effective processing stage.

[0085] Step S2.2 specifically includes:

[0086] Step S2.2.1: Calculate the mean value F of the effective tangential grinding force signal. t .

[0087] Step S2.2.2: Calculate the mean value F of the effective normal grinding force signal. n .

[0088] Step S2.2.3: Determine the friction coefficient μ based on the ratio of the average value of the effective tangential grinding force signal to the average value of the effective normal grinding force signal. The specific formula is: μ = F t / F n .

[0089] Step S2.2.4: Determine the sampling frequency f and the total number N of data points of the force signal in the effective processing stage.

[0090] Step S2.2.5: Determine the effective processing time T based on the ratio of the total number of data points to the sampling frequency, specifically using the formula: T = N / f. Generally, the sampling frequency is 2000Hz. The total number of data points for the force signal during the effective processing stage specifically refers to the total number of data points for the effective normal grinding force signal or the total number of data points for the effective tangential grinding force signal, determined using the "Array Size" function in LabVIEW.

[0091] Step S3: Using multivariate nonlinear regression analysis, determine the multivariate nonlinear numerical models of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters; the grinding input parameters include: grinding wheel speed v sWorkpiece feed speed v w and grinding depth α p .

[0092] Step S3 specifically includes:

[0093] Step S3.1: Establish nonlinear function models for the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters.

[0094] The nonlinear function model of surface roughness with respect to grinding input parameters is as follows:

[0095]

[0096] The nonlinear function model of the friction coefficient with respect to the grinding input parameters is as follows:

[0097]

[0098] The nonlinear function model of the effective machining time with respect to the grinding input parameters is as follows:

[0099]

[0100] Among them, δ, η, τ, ε1, ε2, ε3, α1, α2, α3, ω1, ω2, and ω3 are all parameters to be determined.

[0101] Step S3.2: Take the logarithm of both ends of each of the nonlinear function models to obtain the corresponding linear function models; the linear function models and the nonlinear function models have the same parameters to be determined.

[0102] Step S3.3: Fit each of the linear function models to obtain the values ​​of each parameter to be determined.

[0103] Step S3.4: Substitute the values ​​of each parameter to be determined into the corresponding nonlinear function model to obtain the multivariate nonlinear numerical model of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters.

[0104] Specifically, this invention uses 65 sets of experimental data for fitting and 10 sets of data for model validity verification, ultimately obtaining the following multivariate nonlinear numerical model of surface roughness, friction coefficient, and effective processing time with respect to grinding input parameters:

[0105]

[0106]

[0107]

[0108] Where i represents the sequence number of the test data.

[0109] Step S4: Based on the multivariate nonlinear numerical model of the surface roughness, the friction coefficient and the effective processing time, determine the multi-objective numerical function model.

[0110] Step S5: The weight vectors of each sub-objective function in the multi-objective numerical function model are optimized using the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA / D) algorithm to obtain the optimized multi-objective numerical function model. Here, each sub-objective function refers to the multivariate nonlinear numerical model of surface roughness, the multivariate nonlinear numerical model of friction coefficient, and the multivariate nonlinear numerical model of effective processing time, respectively. The optimized multi-objective numerical function model includes: a first optimized model optimized with the objectives of minimizing surface roughness, minimizing friction coefficient, and minimizing effective processing time, and a second optimized model optimized with the objectives of minimizing surface roughness, maximizing friction coefficient, and minimizing effective processing time.

[0111] Step S5 specifically includes:

[0112] Step S5.1: Using a decomposition-based multi-objective evolutionary algorithm, the weight vectors of each sub-objective function in the multi-objective numerical function model are optimized with the objectives of minimizing surface roughness, minimizing friction coefficient, and minimizing effective processing time, to obtain the first set of weight vector proportions and the corresponding first optimization model.

[0113] Step S5.2: Using a decomposition-based multi-objective evolutionary algorithm, the weight vectors of each sub-objective function in the multi-objective numerical function model are optimized with the objectives of minimizing surface roughness, maximizing friction coefficient, and minimizing effective processing time, to obtain the proportion of the second set of weight vectors and the corresponding second optimization model.

[0114] Specifically, the MOEA / D algorithm is used to synthesize the three sub-objective functions established by the multivariate nonlinear numerical model into a single multi-objective function. The establishment of this multi-objective function lays the foundation for the rationality of subsequent process optimization.

[0115] The constructed multi-objective numerical function model is as follows:

[0116]

[0117] Where x refers to the grinding input parameters, including: grinding wheel speed v s Workpiece feed speed v w and grinding depth α p .

[0118] Because the relationship between the friction coefficient and surface roughness is unclear (i.e., within a suitable range for the friction coefficient, both excessively high and low values ​​tend to decrease surface roughness), two objective functions are established: minimizing surface roughness, minimizing the friction coefficient, and minimizing effective processing time; and minimizing surface roughness, maximizing the friction coefficient, and minimizing effective processing time. The MOEA / D algorithm is used to optimize the weight vectors of the three established multivariate nonlinear numerical function models, resulting in two sets of optimal weight proportion vectors. The specific process of optimizing the weight vector proportions using the MOEA / D algorithm is as follows:

[0119] Step 1: Define the encoding format of the grinding input parameters as decimal encoding. Randomly generate N... p The initial population P0 is constrained by size. N is generated. p An initial weight vector w ij Calculate the distance between two vectors, generate a distance matrix d, and select a neighbor set B from d according to the Euclidean distance minimization rule. i This refers to the evolved parent entity. Let the ideal point z* and an empty external document S* be defined. Where N... p This represents the number of weight vectors; the ideal point can be understood as the optimal weight vector allocation. External documents are used to store the optimized weight proportions.

[0120] Step 2: Calculate the fit function value R of the initial surface roughness, friction coefficient, and effective processing time of the parent individual. a The fitness functions are μ(x) and T(x). All fitness functions are chosen as the reciprocal of the sub-objective function. The higher the fitness value of the parent individual, the greater the probability of it being selected.

[0121] Step 3: Assign a weight vector to each sub-objective function.

[0122] Step 4: For individual X i Genetic recombination: from B i Two randomly selected individuals undergo crossover mutation to generate new offspring. The objective function value F of the offspring is compared with the ideal point z*. r (X i ,new) to update the ideal point.

[0123] Step 5: Use the strategy from Step 2 to calculate the fitness function values ​​for the surface roughness, friction coefficient, and effective processing time of the offspring individuals.

[0124] Step 6: Update the neighbor scheme (parent individual) using the Tchebycheff aggregation function.

[0125] Step 7: During the evolutionary process, all discovered non-dominated solutions are added to the external document S* (in a population, if an individual is not dominated by other individuals, it is called a non-dominated solution, or non-superior solution; specifically, it is sorted by fitness value, and individuals with high fitness values ​​are stored in the external document). All individuals in S* that are dominated by new individuals are deleted, leaving all solutions in S* as non-dominated solutions.

[0126] Step 8: Determine if the termination condition is met. If the termination condition is not met, proceed to Step 3. The termination condition is reaching the maximum fitness value or the maximum number of iterations.

[0127] Step 9: If the termination condition is met, the output S* is taken as the Pareto optimal solution set for the optimization problem. This completes the multi-objective optimization strategy for the grinding process.

[0128] In this process, due to the special nature of the friction coefficient sub-objective function (here, minimizing and maximizing the friction coefficient are the respective objective functions), two sets of optimal weight vector proportions are ultimately obtained. The existence of these two sets of optimal weight vector proportions leads to two definite multi-objective numerical function models.

[0129] In this embodiment, MOEA / D is used for multi-objective weight optimization based on the antagonistic relationship between the optimal values ​​of the sub-objective functions. This enables the optimal values ​​of the process parameters to be obtained, resulting in better surface processing quality and efficiency.

[0130] Step S6: A Genetic Algorithm (GA) is used to optimize the grinding input parameters in the optimized multi-objective numerical function model to obtain the optimal grinding input parameters and determine the optimal range of the friction coefficient. The optimal grinding input parameters and the optimal range of the friction coefficient are used to guide the control of grinding parameters, thereby improving processing quality and efficiency. The optimal grinding input parameters include: a first set of grinding input parameters corresponding to the first optimization model and a second set of grinding input parameters corresponding to the second optimization model.

[0131] Specifically, the input parameters are optimized using GA based on the optimized weight vector, and verified by grinding experiments. Analysis of the surface profile curves obtained under the machining parameters using the coefficient of variation and autocorrelation function reveals that: when the friction coefficient is minimized within a reasonable range, the distribution of microscopic data points on the workpiece surface profile curve deviates less from the roughness value, and the uniformity of data point distribution on the profile curve is better; when the friction coefficient is maximized within a reasonable range, the more periodic signals are present in the data point distribution on the workpiece surface profile curve, and the better the surface machining quality.

[0132] Step S6 specifically includes:

[0133] Step S6.1: Use a genetic algorithm to optimize the grinding input parameters in the optimized multi-objective numerical function model to obtain the optimal grinding input parameters.

[0134] Specifically, a genetic algorithm is used to initialize the population, encode chromosomes in decimal, calculate the population fitness value and select the best individuals from the population, determine whether the termination condition is met, perform selection, crossover, and mutation, and output the best individuals, ultimately obtaining two sets of optimal grinding input parameters.

[0135] Step S6.2: Using a genetic algorithm, determine the optimal range of friction coefficient values ​​based on the proportion of the two weight vectors of the friction coefficient in the optimized multi-objective numerical function model.

[0136] Step S6.3: Conduct a grinding test to determine the surface roughness, friction coefficient, effective processing time, and surface profile curve under the optimal grinding input parameters.

[0137] Specifically, the process parameters optimized by GA are used as the input parameters of the grinding machine. Relevant test parameters (including surface roughness, surface profile curve and grinding force signal) are collected for grinding test verification in order to determine the surface roughness, friction coefficient, effective processing time and surface profile curve under the optimal grinding input parameters.

[0138] Step S6.4: Use the coefficient of variation and autocorrelation function to evaluate the uniformity and periodicity of the data point distribution of the surface profile curve.

[0139] Specifically, the coefficient of variation reflects the degree of deviation of data points on the surface profile curve. A smaller coefficient of variation indicates a smaller deviation of the data points from the surface roughness, signifying better surface profile uniformity and higher surface quality. The autocorrelation function reflects the periodicity of the data points. The more oscillating and bending fluctuations the surface profile curve exhibits, the more periodic components are present, indicating better processing quality.

[0140] Analysis of experimental data reveals that as the grinding depth increases (with constant grinding wheel speed and workpiece feed rate), the friction coefficient first increases and then decreases with increasing surface roughness, exhibiting optimal values ​​at both ends. Therefore, based on the two weighted coefficients of the friction coefficient sub-objective function optimized using MOEA / D within the multi-objective function, the minimum and maximum values ​​of the friction coefficient sub-objective function are calculated using GA to determine the optimal range for the friction coefficient. The reasonable range for the friction coefficient is (0.197, 0.216).

[0141] Specifically, based on the friction coefficient weighting range optimized by the MOEA / D algorithm, GA is used to determine the optimal range of friction coefficient values. From the objective function value R... aAs can be seen from the measurements of μ and T, this process parameter improves grinding efficiency while ensuring the surface finish of the workpiece.

[0142] This invention uses surface roughness, friction coefficient, and effective processing time as optimization objectives to determine grinding parameters. The MOEA / D algorithm is used to optimize the weighting of surface roughness, friction coefficient, and effective processing time. GA is used to determine the optimal range of grinding input parameters and friction coefficient values, thus minimizing random errors. Furthermore, this invention evaluates the optimized process parameters by considering the uniformity and periodicity of the distribution of microscopic data points on the surface profile curve. This allows for detailed evaluation from a microscopic distribution perspective. Experimental verification shows that this invention can improve grinding efficiency while ensuring surface finish.

[0143] Example 2

[0144] To implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a process parameter optimization system for evaluating grinding surface quality based on force signals is provided below. For example... Figure 3 As shown, the system includes:

[0145] Data acquisition module 1 is used to acquire grinding force signals and surface roughness.

[0146] Friction coefficient and effective processing time determination module 2 is used to determine the friction coefficient and effective processing time based on the grinding force signal.

[0147] The multivariate nonlinear numerical model determination module 3 is used to determine the multivariate nonlinear numerical models of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters using multivariate nonlinear regression analysis; the grinding input parameters include: grinding wheel speed, workpiece feed rate, and grinding depth.

[0148] The multi-objective numerical function model determination module 4 is used to determine the multi-objective numerical function model based on the multivariate nonlinear numerical model of the surface roughness, the friction coefficient and the effective processing time.

[0149] The multi-objective numerical function model optimization module 5 is used to optimize the weight vectors of each sub-objective function in the multi-objective numerical function model using a decomposition-based multi-objective evolutionary algorithm, thereby obtaining an optimized multi-objective numerical function model. The optimized multi-objective numerical function model includes: a first optimized model optimized with the objectives of minimizing surface roughness, minimizing friction coefficient, and minimizing effective processing time; and a second optimized model optimized with the objectives of minimizing surface roughness, maximizing friction coefficient, and minimizing effective processing time.

[0150] The process parameter optimization module 6 is used to optimize the grinding input parameters in the optimized multi-objective numerical function model using a genetic algorithm to obtain the optimal grinding input parameters and determine the optimal range of friction coefficient values. The optimal grinding input parameters include: a first set of grinding input parameters corresponding to the first optimization model and a second set of grinding input parameters corresponding to the second optimization model.

[0151] Furthermore, the friction coefficient and effective processing time determination module 2 includes: an effective processing stage force signal determination submodule and a friction coefficient and effective processing time determination submodule.

[0152] The submodule for determining the force signal during the effective processing stage specifically includes:

[0153] The preprocessing unit is used to perform drift compensation, removal of insufficient contact force signals, and filtering and noise reduction on the grinding force signal to obtain the preprocessed grinding force signal.

[0154] The numerical determination unit is used to determine the minimum effective normal force value and the maximum effective tangential force value.

[0155] An effective normal grinding force signal determination unit is used to determine the effective normal grinding force signal in the preprocessed grinding force signal based on the minimum effective normal force value.

[0156] An effective tangential grinding force signal determination unit is used to determine the effective tangential grinding force signal in the preprocessed grinding force signal based on the maximum effective tangential force value.

[0157] The submodule for determining the friction coefficient and effective processing time specifically includes:

[0158] The first mean value calculation unit is used to calculate the mean value of the effective tangential grinding force signal.

[0159] The second mean calculation unit is used to calculate the mean of the effective normal grinding force signal.

[0160] The friction coefficient determination unit is used to determine the friction coefficient based on the ratio of the mean of the effective tangential grinding force signal to the mean of the effective normal grinding force signal.

[0161] The sampling frequency and total number of data points determination unit is used to determine the sampling frequency and the total number of data points of the force signal in the effective processing stage.

[0162] An effective processing time determination unit is used to determine the effective processing time based on the ratio of the total number of data points to the sampling frequency.

[0163] Furthermore, the multivariate nonlinear numerical model determination module 3 specifically includes:

[0164] The nonlinear function model building unit is used to build nonlinear function models of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters.

[0165] A linear transformation unit is used to take the logarithm of both ends of each of the nonlinear function models to obtain the corresponding linear function model; the linear function model and the nonlinear function model have the same parameters to be determined.

[0166] The function fitting unit is used to fit each of the linear function models to obtain the values ​​of each parameter to be determined.

[0167] The multivariate nonlinear numerical model determination unit is used to substitute the values ​​of each parameter to be determined into the corresponding nonlinear function model to obtain the multivariate nonlinear numerical model of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters.

[0168] Furthermore, the process parameter optimization module 6 specifically includes:

[0169] The optimal grinding input parameter determination unit is used to optimize the grinding input parameters in the optimized multi-objective numerical function model using a genetic algorithm to obtain the optimal grinding input parameters.

[0170] The optimal range of friction coefficient is determined by using a genetic algorithm to determine the optimal range of friction coefficient based on the proportion of the two weight vectors of the friction coefficient in the optimized multi-objective numerical function model.

[0171] The test analysis unit is used to conduct grinding tests to determine the surface roughness, friction coefficient, effective processing time, and surface profile curve under the optimal grinding input parameters.

[0172] An evaluation unit is used to evaluate the uniformity and periodicity of the data point distribution of the surface profile curve using the coefficient of variation and autocorrelation function.

[0173] In summary, based on grinding force signal processing, this invention discovers that as the grinding depth of cut increases (with the same grinding wheel speed and workpiece feed rate), the friction coefficient (the ratio of the average tangential force to the average normal force) first increases and then decreases with increasing surface roughness. That is, the friction coefficient is optimally sized at both excessively high and low values. Using MOEA / D optimization based on a multi-objective function model, the reasonable range of friction coefficient values ​​is determined through GA. Analysis of the coefficient of variation and autocorrelation of the workpiece surface profile curve reveals that: when the friction coefficient is at its smallest value within the reasonable range, the distribution of microscopic data points on the workpiece surface profile curve deviates less from the roughness value, and the uniformity of data point distribution on the profile curve is better; when the friction coefficient is at its largest value within the reasonable range, the more periodic signals in the data point distribution on the workpiece surface profile curve, the better the surface finish.

[0174] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0175] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for optimizing process parameters to evaluate grinding surface quality based on force signals, characterized in that, The method includes: Acquire grinding force signals and surface roughness; The friction coefficient and effective processing time are determined based on the grinding force signal. Multivariate nonlinear regression analysis was used to determine the multivariate nonlinear numerical models of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters, including: grinding wheel speed, workpiece feed rate, and grinding depth. Based on the multivariate nonlinear numerical model of the surface roughness, the friction coefficient, and the effective processing time, a multi-objective numerical function model is determined. A decomposition-based multi-objective evolutionary algorithm is used to optimize the weight vectors of each sub-objective function in the multi-objective numerical function model to obtain an optimized multi-objective numerical function model. The optimized multi-objective numerical function model includes: a first optimized model optimized with the objectives of minimizing surface roughness, minimizing friction coefficient, and minimizing effective processing time, and a second optimized model optimized with the objectives of minimizing surface roughness, maximizing friction coefficient, and minimizing effective processing time. A genetic algorithm is used to optimize the grinding input parameters in the optimized multi-objective numerical function model to obtain the optimal grinding input parameters and determine the optimal range of friction coefficient values. The optimal grinding input parameters include: the first set of grinding input parameters corresponding to the first optimization model and the second set of grinding input parameters corresponding to the second optimization model.

2. The method for optimizing process parameters based on force signals to evaluate the surface quality of grinding, as described in claim 1, is characterized in that... The friction coefficient and effective processing time are determined based on the grinding force signal, specifically including: The effective machining stage force signal is determined based on the grinding force signal; the effective machining stage force signal includes: effective tangential grinding force signal and effective normal grinding force signal; The friction coefficient and effective processing time are determined based on the force signal of the effective processing stage.

3. The method for optimizing process parameters based on force signals to evaluate the surface quality of grinding, as described in claim 2, is characterized in that... The effective machining stage force signal is determined based on the grinding force signal, specifically including: The grinding force signal is subjected to drift compensation, removal of incomplete contact force signals, and filtering and noise reduction to obtain a preprocessed grinding force signal; Determine the minimum effective normal force value and the maximum effective tangential force value; The effective normal grinding force signal in the preprocessed grinding force signal is determined based on the minimum effective normal force value; The effective tangential grinding force signal in the preprocessed grinding force signal is determined based on the maximum effective tangential force value.

4. The method for optimizing process parameters based on force signals to evaluate the surface quality of grinding, as described in claim 2, is characterized in that... The friction coefficient and effective processing time are determined based on the force signal during the effective processing stage, specifically including: Calculate the mean value of the effective tangential grinding force signal; Calculate the mean value of the effective normal grinding force signal; The friction coefficient is determined based on the ratio of the mean of the effective tangential grinding force signal to the mean of the effective normal grinding force signal. Determine the sampling frequency and the total number of data points for the force signal in the effective processing stage; The effective processing time is determined based on the ratio of the total number of data points to the sampling frequency.

5. The method for optimizing process parameters based on force signals to evaluate the surface quality of grinding, as described in claim 1, is characterized in that... Multivariate nonlinear regression analysis was used to determine the multivariate nonlinear numerical models of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters, specifically including: Nonlinear function models of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters are established respectively. Taking the logarithm of both ends of each of the nonlinear function models yields the corresponding linear function model; the linear function model and the nonlinear function model have the same parameters to be determined. Each of the linear function models is fitted to obtain the values ​​of the parameters to be determined. Substituting the values ​​of each parameter to be determined into the corresponding nonlinear function model, we obtain a multivariate nonlinear numerical model of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters.

6. The method for optimizing process parameters based on force signals to evaluate the surface quality of grinding, as described in claim 1, is characterized in that, A decomposition-based multi-objective evolutionary algorithm is used to optimize the weight vectors of each sub-objective function in the multi-objective numerical function model, resulting in an optimized multi-objective numerical function model, specifically including: A decomposition-based multi-objective evolutionary algorithm is used to optimize the weight vectors of each sub-objective function in the multi-objective numerical function model with the objectives of minimizing surface roughness, minimizing friction coefficient, and minimizing effective processing time, thereby obtaining the first set of weight vector proportions and the corresponding first optimization model. A decomposition-based multi-objective evolutionary algorithm is used to optimize the weight vectors of each sub-objective function in the multi-objective numerical function model with the objectives of minimizing surface roughness, maximizing friction coefficient, and minimizing effective processing time, thereby obtaining the proportion of the second set of weight vectors and the corresponding second optimization model.

7. The method for optimizing process parameters based on force signals to evaluate the surface quality of grinding, as described in claim 1, is characterized in that... A genetic algorithm is used to optimize the grinding input parameters in the optimized multi-objective numerical function model to obtain the optimal grinding input parameters and determine the optimal range of friction coefficient values, specifically including: A genetic algorithm is used to optimize the grinding input parameters in the optimized multi-objective numerical function model to obtain the optimal grinding input parameters. A genetic algorithm is used to determine the optimal range of friction coefficient values ​​based on the proportion of the two weight vectors of the friction coefficient in the optimized multi-objective numerical function model. Grinding tests were conducted to determine the surface roughness, friction coefficient, effective processing time, and surface profile curve under the optimal grinding input parameters. The uniformity and periodicity of the data point distribution of the surface profile curve are evaluated using the coefficient of variation and autocorrelation function.

8. A process parameter optimization system for evaluating grinding surface quality based on force signals, characterized in that, The system includes: The data acquisition module is used to acquire grinding force signals and surface roughness. A friction coefficient and effective processing time determination module is used to determine the friction coefficient and effective processing time based on the grinding force signal; The multivariate nonlinear numerical model determination module is used to determine the multivariate nonlinear numerical models of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters using multivariate nonlinear regression analysis; the grinding input parameters include: grinding wheel speed, workpiece feed rate, and grinding depth; The multi-objective numerical function model determination module is used to determine the multi-objective numerical function model based on the surface roughness, the friction coefficient, and the effective processing time using a multivariate nonlinear numerical model. The multi-objective numerical function model optimization module is used to optimize the weight vectors of each sub-objective function in the multi-objective numerical function model using a decomposition-based multi-objective evolutionary algorithm to obtain an optimized multi-objective numerical function model. The optimized multi-objective numerical function model includes: a first optimized model optimized with the objectives of minimizing surface roughness, minimizing friction coefficient, and minimizing effective processing time, and a second optimized model optimized with the objectives of minimizing surface roughness, maximizing friction coefficient, and minimizing effective processing time. The process parameter optimization module is used to optimize the grinding input parameters in the optimized multi-objective numerical function model using a genetic algorithm to obtain the optimal grinding input parameters and determine the optimal range of friction coefficients. The optimal grinding input parameters include: a first set of grinding input parameters corresponding to the first optimization model and a second set of grinding input parameters corresponding to the second optimization model.

9. The process parameter optimization system for evaluating grinding surface quality based on force signals according to claim 8, characterized in that, The multivariate nonlinear numerical model determination module specifically includes: The nonlinear function model building unit is used to build nonlinear function models of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters, respectively. A linear transformation unit is used to take the logarithm of both ends of each of the nonlinear function models to obtain the corresponding linear function model; the linear function model and the nonlinear function model have the same parameters to be determined; The function fitting unit is used to fit each of the linear function models to obtain the values ​​of each parameter to be determined. The multivariate nonlinear numerical model determination unit is used to substitute the values ​​of each parameter to be determined into the corresponding nonlinear function model to obtain the multivariate nonlinear numerical model of the surface roughness, the friction coefficient, and the effective processing time with respect to the grinding input parameters.

10. The process parameter optimization system for evaluating grinding surface quality based on force signals according to claim 8, characterized in that, The process parameter optimization module specifically includes: The optimal grinding input parameter determination unit is used to optimize the grinding input parameters in the optimized multi-objective numerical function model using a genetic algorithm to obtain the optimal grinding input parameters. The optimal range of friction coefficient is determined by using a genetic algorithm to determine the optimal range of friction coefficient based on the proportion of the two weight vectors of friction coefficient in the optimized multi-objective numerical function model. The test analysis unit is used to conduct grinding tests to determine the surface roughness, friction coefficient, effective processing time, and surface profile curve under the optimal grinding input parameters. An evaluation unit is used to evaluate the uniformity and periodicity of the data point distribution of the surface profile curve using the coefficient of variation and autocorrelation function.

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