CN108127481A - A kind of Forecasting Methodology of the workpiece surface appearance based on Flank machining - Google Patents

Abstract

The invention discloses a method for predicting the surface topography of a workpiece based on side milling, which belongs to the technical field of mechanical manufacturing simulation; the measured surface topography data of the workpiece in the real-time processing process are compared with the surface topography data of the workpiece model Yes, and obtain the difference data between the two as the experimental random surface topography data; process the experimental random surface topography data according to the method of probability statistics, Pearson distribution clusters and random numbers, and obtain the random model of surface topography prediction; according to the workpiece model The stochastic model for surface topography prediction, by changing the processing parameters in the simulation model, can obtain the surface topography data of the workpiece in the actual side milling process corresponding to the processing parameters, that is, predict the surface topography of the workpiece; this method combines the experimental Compared with the existing prediction methods, it improves the efficiency and improves the prediction accuracy.

Description

A Prediction Method of Workpiece Surface Topography Based on Side Milling

technical field

The invention belongs to the technical field of mechanical manufacturing simulation, and in particular relates to a method for predicting the surface topography of a workpiece based on side milling.

Background technique

The surface topography of the workpiece is an important index to measure the surface quality of the processed workpiece, and it also has an important impact on the mechanical properties of the workpiece. The surface topography of the processed workpiece refers to the micro-geometric forms of various shapes and sizes remaining on the surface of the workpiece during the processing process. Tool wear, eccentricity, various deformation errors and material properties during the processing process will affect the parts. surface topography. Therefore, it is of great significance to analyze the surface topography of the processed workpiece, deeply analyze the mechanism of surface topography in the milling process, and establish a surface topography prediction model.

At present, there are mainly two types of prediction models for surface topography, empirical models and analytical models. The empirical model obtains data by means of experiments. This method is very accurate and convenient, but its disadvantages are also very obvious, that is, its scope of application is very limited by experiments, and it is not convenient for various processing conditions. The analysis model is based on the processing principle and uses mathematical methods to analyze the kinematic characteristics in the processing process. This method has a wide range of applications, but the calculation is very complicated and the efficiency is very low.

Contents of the invention

In view of this, the object of the present invention is to provide a method for predicting the workpiece surface topography based on side milling, which combines the advantages of experiments and statistical methods, and simplifies calculations while getting rid of the limitations of experiments. Compared with the existing forecasting methods, the efficiency is improved and the forecasting accuracy is improved.

The present invention is achieved through the following technical solutions:

A method for predicting the surface topography of a workpiece based on side milling, the specific steps are as follows:

The first step is to perform side milling on the workpiece through the tool, and measure the surface topography data of the workpiece during real-time processing;

The second step is to establish the simulation model of the side milling of the workpiece, including: workpiece model, tool model, control module, motion module and input and output module;

In the third step, after inputting the model data of the tool in the first step and the processing parameters of the tool in the first step through the input and output module, the tool model is controlled by the motion module and the control module to perform side milling on the workpiece model. The processing is the same as the side milling of the workpiece by the tool in the first step. After the side milling is completed, the surface topography data of the workpiece model is obtained;

The fourth step is to compare the surface topography data of the workpiece in the real-time processing process measured in the first step with the surface topography data of the workpiece model in the third step, and obtain the difference data between the two as the experimental random surface topography data;

The fifth step is to process the experimental random surface topography data in the fourth step according to the method of probability statistics, Pearson distribution clusters and random numbers to obtain a random model for surface topography prediction;

The sixth step is to predict the random model according to the surface topography of the workpiece model. By changing the processing parameters in the simulation model, the surface topography data of the workpiece in the actual side milling process corresponding to the processing parameters can be obtained, that is, the surface topography of the workpiece is predict.

Further, in the first step, the surface topography data of the workpiece during real-time processing is measured by a contact profiler.

Further, in the second step,

The workpiece model is obtained by simulating the workpiece in the first step; the workpiece model is located in the workpiece coordinate system, and the workpiece coordinate system is used to locate the position of the workpiece model, and the workpiece model is divided by dexel lines, and each dexel line has a start and end points;

The tool model is obtained by simulating the tool in the first step; the tool model is located in the tool coordinate system, and the tool coordinate system is used to locate the position of the tool model, and the positional relationship between the tool model and the workpiece model is the same as that in the first step The positional relationship between the tool and the workpiece is the same;

The control module includes: axis motion control and simulation parameter setting; the axis motion control is the motion control of the tool model, including the rotation and translation of the tool model; the simulation parameter setting includes: the processing parameters of the tool model, the tool model Motion parameters, control parameters of tool model, number of lines of dexel and discrete time; wherein, the processing parameters include cutting depth a _p , feed rate f _z and cutting width a _e ;

The motion module is used to drive the tool model to move and then move relative to the workpiece model. During the relative movement between the tool model and the workpiece model, the tool model contacts the dexel lines and cuts the dexel lines. The cut dexel lines form new start and end points of

The input and output module is used for data input and output; the input data includes the model data of the workpiece and the model data of the tool; the output data is the coordinates of the new starting point and the ending point of the cut dexel lines.

Further, the laser scanner is used to scan the tool in the first step to obtain point cloud data, and the point cloud data is converted from rectangular coordinates to polar coordinates through coordinate transformation, and then the model data of the tool is obtained.

Furthermore, the number of lines and discrete time of the dexel in the simulation parameter setting can be adjusted according to the prediction accuracy and work efficiency of the setting requirements.

Further, during the side milling process of the simulation model, the tool model and the workpiece model move relative to each other, the tool model contacts the dexel line, and cuts the dexel line, and the cut dexel line forms a new starting point and end point; through The input and output module outputs the cut dexel lines to form the coordinates of the new start point and end point, and then obtains the surface topography data of the workpiece model.

Further, the number of data points of the surface topography data of the workpiece model is the same as the number of data points of the surface topography data of the workpiece in the first step.

Further, in the fifth step, the steps of obtaining the random model for surface topography prediction are as follows:

Step 1. The experimental random surface topography data is counted according to the number of occurrences at different heights to obtain the distribution function CDF1, which is displayed in the form of a histogram. The abscissa is the height value of the random model for surface topography prediction, and the ordinate is the number of occurrences ;

Step 2, the distribution function of Gaussian distribution is used to characterize the distribution function CDF1, and its distribution parameters are respectively: the expectation of the distribution function CDF1 is The standard deviation of the distribution function CDF1 is The skewness of the distribution function CDF1 is And the kurtosis of the distribution function CDF1 is Among them, x _i is the height value of the i-th random model for surface topography prediction, and n is the total number of height values for the random model for surface topography prediction;

Step 3, after changing the processing parameters of the tool model in the second step more than twice, repeat step 1 and step 2 respectively, after obtaining more than two groups of distribution parameters μ ₁ -μ ₄ , use the quadratic fitting method to simulate Combining the relationship between the distribution parameters and the processing parameters of the tool model in the second step, i.e. μ _i = μ(a _p , f _z , a _e ), where i=1, 2, 3, 4, i.e. μ ₁ = μ ₁ (a _p , f _z , a _e ), μ ₂ = μ ₂ (a _p , f _z , a _e ), μ ₃ = μ ₃ (a _p , f _z , a _e ), μ ₄ = μ ₄ (a _p , f _z , a _e );

Step 4, according to μ _i = μ(a _p , f _z , a _e ) and the Pearson distribution cluster, the distribution function CDF2 is calculated, and the distribution function CDF2 is displayed in the form of a histogram, and the abscissa is the random model for surface topography prediction The height value, the vertical axis is the probability density;

The function f(x) of this probability density satisfies: Among them, x is the height value of the random model for surface topography prediction, b ₀ =0, Derivative A'=10β _{2-18-12β 1 of} A, μ ' ₁ is the derivative of μ ₁ , μ ' ₂ is the derivative of μ ₂ , μ ' ₃ is the derivative of μ ₃ , μ ' ₄ is the derivative of μ ₄ ;

Step 5, discretizing the height value of the surface topography prediction random model in the distribution function CDF2 in the horizontal direction to obtain the surface topography prediction random model;

The discrete is expressed in a discrete coordinate system, the abscissa is the number of height values of the surface topography prediction random model, and the ordinate is the random value of the height value of the surface topography prediction random model. Generate; the area formed by the abscissa and ordinate represents the number of random values in the discrete area of the height value interval;

The probability of occurrence of random values in the discrete area is determined by ΔA/A, ΔA is the area formed by the probability density corresponding to two height values in the discrete area in the distribution function CDF2, and A is the total probability density, which is A=1.

Beneficial effects: (1) The present invention combines experiments with statistical methods to predict the random appearance of workpieces in side milling; by using Pearson distribution clusters, the actual measurement results are used as analysis references for simulation, which greatly improves The precision and accuracy of the simulation are improved; by using the random number method to characterize the influence of random factors in processing on the surface morphology of the workpiece, and using mathematical methods to characterize the experimental results, the prediction model is freed from the constraints of the experimental conditions. The surface topography of side milling can be predicted through mathematical calculation, which improves the prediction range and prediction efficiency.

(2) By establishing a simulation model, the present invention visualizes the simulation process and realizes stop-and-go operation through the control module, which is closer to the actual cutting process, has higher reliability of the simulation results, and effectively guarantees the similarity between the simulation and the actual results.

(3) The present invention uses a high-precision contact profiler to collect data on the surface topography of the workpiece processed by side milling, which ensures the accuracy of the actual data and provides a data source for the prediction method.

(4) The number of lines and the discrete time of the dexel in the present invention can be adjusted according to the prediction accuracy and work efficiency, which improves the work efficiency under the condition of high precision.

Description of drawings

Fig. 1 is a flow chart of the prediction method of the present invention.

Fig. 2 is a flow chart of obtaining a stochastic model for surface topography prediction in the present invention.

Fig. 3 is a schematic diagram of the dexel line of the present invention.

Fig. 4 is a schematic flow chart of the present invention.

Fig. 5 is a schematic diagram of the surface topography of the workpiece during real-time processing.

Fig. 6 is a schematic diagram of the surface topography of the workpiece predicted by the present invention.

Detailed ways

The present invention will be described in detail below with reference to the accompanying drawings and examples.

This embodiment provides a method for predicting the surface topography of a workpiece based on side milling, see accompanying drawings 1 and 4, the specific steps are as follows:

The first step is to perform side milling on the workpiece through the tool, and measure the surface topography data of the workpiece during real-time processing through the contact profiler, see Figure 5;

The second step is to establish the simulation model of the side milling of the workpiece, including: workpiece model, tool model, control module, motion module and input and output module;

The workpiece model is obtained by simulating the workpiece in the first step; the workpiece model is located in the workpiece coordinate system, and the workpiece coordinate system is used to locate the position of the workpiece model, and the workpiece model is divided by dexel lines (i.e. according to the outline of the workpiece model , divide the dexel lines, the dexel lines are located in the three directions of xyz, and each dexel line has a starting point and an ending point), see Figure 3;

The tool model is obtained by simulating the tool in the first step, that is, the point cloud data is obtained by scanning the tool in the first step by a laser scanner, and the point cloud data is converted from rectangular coordinates to polar coordinates through coordinate transformation, and then obtained The model data of the tool; the tool model is located in the tool coordinate system, the tool coordinate system is used to locate the position of the tool model, and the positional relationship between the tool model and the workpiece model is the same as the positional relationship between the tool and the workpiece in the first step; In this embodiment, two coordinate systems are used for the positioning of the workpiece model and the tool model, which has the same effect as using the same coordinate system, both of which are positioning. The advantage of using two coordinate systems is that when writing the control program and defining the workpiece or tool separately It is more convenient when using the characteristics;

The control module includes: axis motion control and simulation parameter setting; the axis motion control is the motion control of the tool model, including the rotation and translation of the tool model; the simulation parameter setting includes: the processing parameters of the tool model (the processing Parameters include cutting depth a _p , feed rate f _z and cutting width a _e ), tool model motion parameters (speed and feed rate), tool model control parameters (program start, end and pause), number of lines in dexel And discrete time (discrete time represents the time interval of data calculation by the control module, which is used to represent the accuracy of control calculation); among them, the number of lines of dexel and discrete time can be adjusted according to the prediction accuracy and work efficiency of the set demand;

The motion module is used to drive the tool model to move and then move relative to the workpiece model (in this embodiment, the workpiece model does not move). During the relative movement between the tool model and the workpiece model, the tool model is in contact with the dexel line, and Cut the dexel lines, and the cut dexel lines form new starting and ending points;

The input and output module is used for data input and output; the input data includes the model data of the workpiece and the model data obtained by the laser scanner scanning the tool in the first step; the output data forms a new starting point for the cut dexel lines and the coordinates of the end point, the simulated surface topography is measured by the height value;

In the third step, after inputting the model data obtained by scanning the tool in the first step by the laser scanner and the processing parameters of the tool in the first step through the input and output module, the tool model is controlled by the motion module and the control module to perform side milling on the workpiece model Processing, the side milling process is the same as the side milling process of the tool on the workpiece in the first step; during the side milling process, the tool model and the workpiece model move relative to each other, the tool model contacts the dexel line, and cuts the dexel line, and is cut The final dexel lines form a new starting point and end point; the coordinates of the new starting point and end point are formed by outputting the cut dexel lines through the input and output module, and then the surface topography data of the workpiece model are obtained; wherein, the workpiece model’s The number of data points of the surface topography data is the same as the number of data points of the surface topography data of the workpiece in the first step, so as to obtain the difference;

The fourth step is to compare the surface topography data of the workpiece in the real-time processing process measured in the first step with the surface topography data of the workpiece model in the third step, and obtain the difference data between the two as the experimental random surface topography data;

The fifth step is to process the experimental random surface topography data in the fourth step according to the method of probability statistics, Pearson distribution clusters and random numbers to obtain a random model for surface topography prediction;

Described process is as follows, referring to accompanying drawing 2:

Step 1. The experimental random surface topography data is counted according to the number of occurrences of different heights, and the distribution function CDF1 (Cumulative Distribution Function) is obtained. The distribution function is displayed in the form of a histogram, and the abscissa is the height value of the random model for surface topography prediction. The vertical axis is the number of occurrences;

Step 2, the distribution function of Gaussian distribution is used to characterize the distribution function CDF1, and its distribution parameters are respectively: the expectation of the distribution function CDF1 is The standard deviation of the distribution function CDF1 is The skewness of the distribution function CDF1 is And the kurtosis of the distribution function CDF1 is Among them, x _i is the height value of the i-th random model for surface topography prediction, and n is the total number of height values for the random model for surface topography prediction;

Step 3, after changing the processing parameters of the tool model in the second step more than twice, repeat step 1 and step 2 respectively to obtain more than two groups of distribution parameters μ ₁ -μ ₄ (more than three groups of distribution parameters μ ₁ -μ ₄ to achieve quadratic fitting), the quadratic fitting method is used to fit the relationship between the distribution parameters and the machining parameters of the tool model, that is, μ _i = μ(a _p , f _z , a _e ), where , i=1, 2, 3, 4, namely μ ₁ = μ ₁ (a _p , f _z , a _e ), μ ₂ = μ ₂ (a _p , f _z , a _e ), μ ₃ = μ ₃ ( a _p , f _z , a _e ), μ ₄ =μ ₄ (a _p , f _z , a _e );

Step 4, according to μ _i = μ(a _p , f _z , a _e ) and the Pearson distribution cluster, the distribution function CDF2 is calculated, and the distribution function CDF2 is displayed in the form of a histogram, and the abscissa is the random model for surface topography prediction The height value, the vertical axis is the probability density;

The function f(x) of this probability density satisfies: Among them, x is the height value of the random model for surface topography prediction, b ₀ =0, Derivative A'=10β _{2-18-12β 1 of} A, μ ' ₁ is the derivative of μ ₁ , μ ' ₂ is the derivative of μ ₂ , μ ' ₃ is the derivative of μ ₃ , μ ' ₄ is the derivative of μ ₄ ;

Step 5, discretizing the height value of the surface topography prediction random model in the distribution function CDF2 in the horizontal direction to obtain the surface topography prediction random model;

The discrete is expressed in a discrete coordinate system, the abscissa is the number of height values of the surface topography prediction random model, and the ordinate is the random value of the height value of the surface topography prediction random model. Generate; the area formed by the abscissa and the ordinate represents the number of discrete points (i.e. random values) in the discrete area of the height value interval;

The probability of occurrence of discrete points in the discrete area is determined by ΔA/A, where ΔA is the area formed by the probability density corresponding to two height values in the discrete area in the distribution function CDF2, and A is the total probability density, that is, A=1 (for example, When there are a total of 10,000 discrete points in the discrete coordinate system, for a height value of 2-2.01 microns, the discrete point with ΔA/A of 0.01 is expressed in the discrete coordinate system as: 100 discrete points randomly fall within the range of 2-2.01 microns Inside);

The sixth step is to predict the random model according to the surface topography of the workpiece model. By changing the processing parameters in the simulation model, the surface topography data of the workpiece in the actual side milling process corresponding to the processing parameters can be obtained, that is, the surface topography of the workpiece is Forecast, see Figure 6.

To sum up, the above are only preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims (8)

1. A method for predicting the workpiece surface topography based on side milling, characterized in that the specific steps are as follows: The first step is to perform side milling on the workpiece through the tool, and measure the surface topography data of the workpiece during real-time processing; The second step is to establish the simulation model of the side milling of the workpiece, including: workpiece model, tool model, control module, motion module and input and output module; In the third step, after inputting the model data of the tool in the first step and the processing parameters of the tool in the first step through the input and output module, the tool model is controlled by the motion module and the control module to perform side milling on the workpiece model. The processing is the same as the side milling of the workpiece by the tool in the first step. After the side milling is completed, the surface topography data of the workpiece model is obtained; The fourth step is to compare the surface topography data of the workpiece in the real-time processing process measured in the first step with the surface topography data of the workpiece model in the third step, and obtain the difference data between the two as the experimental random surface topography data; The fifth step is to process the experimental random surface topography data in the fourth step according to the method of probability statistics, Pearson distribution clusters and random numbers to obtain a random model for surface topography prediction; The sixth step is to predict the random model according to the surface topography of the workpiece model. By changing the processing parameters in the simulation model, the surface topography data of the workpiece in the actual side milling process corresponding to the processing parameters can be obtained, that is, the surface topography of the workpiece is predict. 2. a kind of prediction method based on the surface topography of workpiece of side milling as claimed in claim 1, it is characterized in that, in the first step, measure the surface topography of workpiece in the real-time machining process by contact profiler data. 3. a kind of prediction method based on the workpiece surface topography of side milling as claimed in claim 1, it is characterized in that, in the second step, The workpiece model is obtained by simulating the workpiece in the first step; the workpiece model is located in the workpiece coordinate system, and the workpiece coordinate system is used to locate the position of the workpiece model, and the workpiece model is divided by dexel lines, and each dexel line has a start and end points; The tool model is obtained by simulating the tool in the first step; the tool model is located in the tool coordinate system, and the tool coordinate system is used to locate the position of the tool model, and the positional relationship between the tool model and the workpiece model is the same as that in the first step The positional relationship between the tool and the workpiece is the same; The control module includes: axis motion control and simulation parameter setting; the axis motion control is the motion control of the tool model, including the rotation and translation of the tool model; the simulation parameter setting includes: the processing parameters of the tool model, the tool model Motion parameters, control parameters of tool model, number of lines of dexel and discrete time; wherein, the processing parameters include cutting depth a _p , feed rate f _z and cutting width a _e ; The motion module is used to drive the tool model to move and then move relative to the workpiece model. During the relative movement between the tool model and the workpiece model, the tool model contacts the dexel lines and cuts the dexel lines. The cut dexel lines form new start and end points of The input and output module is used for data input and output; the input data includes the model data of the workpiece and the model data of the tool; the output data is the coordinates of the new starting point and the ending point of the cut dexel lines. 4. A kind of prediction method based on the surface topography of workpiece of side milling as claimed in claim 3, it is characterized in that, the tool in the first step is scanned by laser scanner and obtain point cloud data, point cloud data After the coordinate transformation, the Cartesian coordinates are transformed into polar coordinates, and then the model data of the tool is obtained. 5. A kind of prediction method based on the workpiece surface topography of side milling as claimed in claim 3, it is characterized in that, the number of lines and the discrete time of the dexel in the simulation parameter setting can be according to the prediction accuracy and the working time of setting demand Efficiency adjustments. 6. a kind of method for predicting the workpiece surface topography based on side milling as claimed in claim 3, is characterized in that, in the side milling process of emulation model, tool model and workpiece model take place relative movement, tool model and The dexel lines touch and cut the dexel lines, and the cut dexel lines form a new start point and end point; output the coordinates of the cut dexel lines to form a new start point and end point through the input and output module, and then obtain the workpiece model surface topography data. 7. A kind of prediction method based on the workpiece surface topography of side milling as claimed in claim 1, is characterized in that, the number of data points of the surface topography data of workpiece model and the surface topography of workpiece in the first step The number of data points of the appearance data is the same. 8. A kind of prediction method based on the workpiece surface topography of side milling as claimed in claim 3, it is characterized in that, in the 5th step, the step that obtains surface topography prediction stochastic model is as follows: Step 1. The experimental random surface topography data is counted according to the number of occurrences at different heights to obtain the distribution function CDF1, which is displayed in the form of a histogram. The abscissa is the height value of the random model for surface topography prediction, and the ordinate is the number of occurrences ; Step 2, the distribution function of Gaussian distribution is used to characterize the distribution function CDF1, and its distribution parameters are respectively: the expectation of the distribution function CDF1 is The standard deviation of the distribution function CDF1 is The skewness of the distribution function CDF1 is And the kurtosis of the distribution function CDF1 is Among them, x _i is the height value of the i-th random model for surface topography prediction, and n is the total number of height values for the random model for surface topography prediction; Step 3, after changing the processing parameters of the tool model in the second step more than twice, repeat step 1 and step 2 respectively, after obtaining more than two groups of distribution parameters μ ₁ -μ ₄ , use the quadratic fitting method to simulate Combining the relationship between the distribution parameters and the processing parameters of the tool model in the second step, i.e. μ _i = μ(a _p , f _z , a _e ), where i=1, 2, 3, 4, i.e. μ ₁ = μ ₁ (a _p , f _z , a _e ), μ ₂ = μ ₂ (a _p , f _z , a _e ), μ ₃ = μ ₃ (a _p , f _z , a _e ), μ ₄ = μ ₄ (a _p , f _z , a _e ); Step 4, according to μ _i = μ(a _p , f _z , a _e ) and the Pearson distribution cluster, the distribution function CDF2 is calculated, and the distribution function CDF2 is displayed in the form of a histogram, and the abscissa is the random model for surface topography prediction The height value, the vertical axis is the probability density; The function f(x) of this probability density satisfies: Among them, x is the height value of the random model for surface topography prediction, b ₀ =0, Derivative A'=10β _{2-18-12β 1 of} A, μ ' ₁ is the derivative of μ ₁ , μ ' ₂ is the derivative of μ ₂ , μ ' ₃ is the derivative of μ ₃ , μ ' ₄ is the derivative of μ ₄ ; Step 5, discretizing the height value of the surface topography prediction random model in the distribution function CDF2 in the horizontal direction to obtain the surface topography prediction random model; The discrete is expressed in a discrete coordinate system, the abscissa is the number of height values of the surface topography prediction random model, and the ordinate is the random value of the height value of the surface topography prediction random model. Generate; the area formed by the abscissa and ordinate represents the number of random values in the discrete area of the height value interval; The probability of occurrence of random values in the discrete area is determined by ΔA/A, ΔA is the area formed by the probability density corresponding to two height values in the discrete area in the distribution function CDF2, and A is the total probability density, which is A=1.