CN115826502A - Numerical control machine tool cutting power prediction method - Google Patents
Numerical control machine tool cutting power prediction method Download PDFInfo
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Abstract
The invention belongs to the technical field of numerical control machine tool control, and particularly relates to a numerical control machine tool cutting power prediction method. The method utilizes the badger search algorithm to carry out global optimization on the weight and the threshold of the BP nerve and search for the optimal weight threshold, overcomes the defects of local minimization, low convergence rate, high randomness and the like of a nerve network, and remarkably improves the prediction precision and efficiency of the BP nerve network, thereby improving the prediction precision and efficiency of the cutting power of a machine tool.
Description
Technical Field
The invention relates to the technical field of numerical control machine tool control, in particular to a method for predicting cutting power of a numerical control machine tool.
Background
The manufacturing industry consumes too much energy every year, which is a troublesome problem facing the world, and the problem of machine tool energy consumption as an industrial 'mother machine' is particularly serious. The machine tool has large quantity and wide range, huge energy consumption and extremely low energy efficiency, and generally does not exceed 30 percent.
In the traditional numerical control machining method, machining parameters are already determined in a programming stage before machining, and the machining parameters are often set by experience of operators and are not optimal or optimal cutting parameters, so that optimal machining quality and machining efficiency cannot be guaranteed; in the actual processing process, especially in the rough processing process, because the cutting conditions are constantly changed due to factors such as impact vibration, uneven machining allowance, cutter abrasion and the like, constant cutting parameters are not suitable for processing; because an operator does not know the quality of a machining condition in actual machining, the selection of cutting parameters is conservative when programming setting is often carried out, even unreasonable conditions can occur, the operator needs to monitor the machining process in real time and timely adjust the cutting parameters according to the change of the machining condition, smooth machining is guaranteed, damages to a cutter, a workpiece and a machine tool are avoided, and the production cost is greatly increased.
The Chinese invention patent with the application number of 201910732917.3 discloses a machine tool self-adaptive control method based on a GA-BP neural network algorithm, which is characterized by comprising the following steps of: firstly, carrying out an orthogonal milling test on a machine tool by changing cutting parameters to obtain a main shaft power signal and a vibration signal under the processing of different cutting parameters; then, carrying out data interpolation and characteristic value extraction on the main shaft power signal, and carrying out denoising and characteristic value extraction on the vibration signal; then, training the BP neural network by using the cutting parameters, the power characteristic values and the vibration characteristic values, and optimizing the initial weight and the threshold of the BP neural network by adopting a genetic algorithm; and finally, in the actual processing process, real-time monitoring is carried out on the power signal and the vibration signal of the machine tool spindle, the power signal and the vibration signal and the cutting parameters are input into a trained neural network, and the feeding speed and the spindle rotating speed are adjusted and controlled through the neural network.
Therefore, the cutting power prediction model established by analyzing and researching the cutting power of the numerical control machine tool has important significance for energy conservation and emission reduction in the manufacturing industry. The existing problems of BP neural network prediction, such as local minimization, low convergence rate, high randomness and the like, are not enough to be used for directly predicting the cutting power of a machine tool, so that the existing problems of the BP neural network are improved by a badger algorithm, and the prediction accuracy and efficiency of the BP neural network are improved.
Disclosure of Invention
The invention aims to provide a method for predicting cutting power of a numerical control machine tool, overcomes the defects of the prior art, utilizes a Tent chaos improved badger search algorithm to carry out a global optimization method on initial weights and thresholds of a neural network structure, overcomes the defects of local minimization, low convergence speed, high randomness and the like of the neural network, and remarkably improves the prediction precision and efficiency of the neural network, thereby improving the prediction precision and efficiency of the cutting power of the machine tool.
The invention provides a method for realizing the technical purpose, which adopts the following scheme to realize the following steps:
a method for predicting cutting power of a numerical control machine tool is characterized by comprising the following steps: the method comprises the following steps:
1) Preprocessing input and output data according to the input data and the output data;
2) Determining the number of input nodes, the number of output nodes and the number of hidden nodes of the neural network so as to determine the structure of the BP neural network;
3) Normalizing the input data and the output data through a normalization model, and respectively taking the normalized input data and the normalized output data as an input P and an output T of a BP neural network structure; setting the number of training data and the number of test data; determining an initial weight and a threshold in a BP neural network structure according to the number of nodes of an input layer, a hidden layer and an output layer;
4) Setting the weight and the threshold value as the initial position of the badger, wherein the position of the badger is a vector;
5) Setting the size and the iteration times of a badger population, generating an initial population of the badger by Tent chaotic mapping to form a position matrix X of the badger population, inputting the initial population of the badger into a BP neural network model, calculating an initial weight, a threshold value and a predicted value of the BP neural network, and calculating the fitness value of the position of the badger by taking an error function between the predicted value and an actual measured value of the BP neural network as a fitness function F;
6) Calculating the fitness value of the position of the badger according to the foraging rule and the fitness function of the badger in the badger searching method;
7) Judging the motion trail of the badger according to the control parameters of the badger mining and honey collecting stage, adopting a trail similar to a cardioid line to update the position in the mining stage, and directly updating the position along with the honey in the honey collecting stage;
8) The optimal position and the optimal fitness value of the badger are changed in each iteration, and when the maximum iteration times are met, the optimal position and the optimal fitness value of the badger appear, namely the optimal weight and the threshold of the BP neural network;
9) And retraining the BP neural network structure model by adopting the optimal weight and the threshold value searched by the badger algorithm, and predicting the cutting power of the machine tool under different working conditions according to the training result.
Compared with the prior art, the invention has the beneficial effects that:
1) The method improves the defects of local minimization, low convergence speed, larger randomness and the like in BP neural network prediction, and can quickly predict the cutting power of the numerical control machine under given conditions.
2) Data analysis shows that the evaluation error of BP prediction data is about 3.3%, the prediction error of the BP neural network optimized by the badger is about 0.03%, and comparison shows that the prediction accuracy of the BP neural network optimized by the badger is obviously improved compared with that of the traditional BP neural network.
Drawings
FIG. 1 is a graph comparing the prediction results of a test value using a BP neural network and a Tent-HBA-BP of the present invention;
FIG. 2 is a schematic diagram of the Tent-HBA-BP process of the present invention.
Detailed Description
The invention is better described in detail below with reference to the figures and the embodiments.
The invention relates to a numerical control machine tool cutting power prediction method, which is significant in optimizing a weight and a threshold of a BP neural network model through an improved badger algorithm. The optimal weight and the threshold of the BP neural network model are calculated by a method of minimizing the error between the predicted value and the actual value, so that the prediction precision and the calculation speed of the BP neural network are obviously improved, and the prediction precision of the cutting power of the machine tool is further improved.
In the melger search algorithm (HBA), the melger foraging rule is as follows:
generally speaking, a badger can continuously locate its prey by using sense of smell. Meliger prefers honey, but it is not good at locating honeycomb. Interestingly, however, honey wizards (a bird) can find a honeycomb but honey is not available. These phenomena form a cooperative relationship even though both: the wizard bird brings the badger to the bee nest, which opens the bee nest with the front paws, and then both enjoy the return of team cooperation.
The first case is referred to herein as the dig mode and the second case is the honey harvest mode. In the digging mode, the badgers position the honeycomb by utilizing the olfaction capability of the badgers, and when the badgers approach the honeycomb, the badgers can select a proper place to dig; in the honey mode, the badger directly utilizes the honeycomb for positioning.
3) In order to simplify the mathematical model, the following assumption is established that n badgers exist in the population, and each badger searches for honey according to the mining and honey collection mode. The optimal positions of the badgers are represented by a position vector, the optimal positions of the badgers can be shared, the optimal positions of the badgers can randomly select a mining mode and a honey collection mode in each optimizing process, and the optimal positions of the badgers are recorded for population sharing.
The principle of the BP neural network combined with the improved badger algorithm in the invention is as follows:
determining the number of input nodes, the number of output nodes and the number of hidden nodes of the neural network according to the input data and the output data so as to determine the structure of the BP neural network, normalizing the input data and the output data through a normalization model, respectively using the input data and the output data after normalization as the input P and the output T of the BP neural network structure, determining the number of initial weights and threshold values in the BP neural network according to a parameter number model, and mapping the initial weights and the threshold values into the positions of the badgers. Setting the population scale and the iteration times of the meliger, initializing the population through Tent mapping to form a meliger population position matrix X, then calculating a neural network predicted value under the initial weight and a threshold value, taking an error function between the network predicted value and an actual measured value as a fitness function F, and calculating the fitness value of the meliger position according to the foraging rule of the meliger in the meliger search method; and updating the positions of the badgers according to the randomly selected mining and honey collecting mode. When the maximum iteration times are met, the position corresponding to the minimum fitness value searched by the algorithm of the badger is the optimal weight and the threshold of the BP neural network.
Substituting the weight value and the threshold value of the optimized BP neural network into the BP neural network for training, and finally predicting the cutting power of the machine tool under different working conditions according to the training result.
The embodiment of the method for predicting the cutting power of the numerical control machine tool comprises the following specific steps:
1) Preprocessing input and output data according to the input data and the output data;
2) Determining the number of input nodes, the number of output nodes and the number of hidden nodes of the neural network so as to determine the topological structure of the BP neural network; determining the influencing parameters of the machine tool on the cutting power in the machining process and the corresponding cutting power, and storing the data in a matrix form. And determining the number of input layer neurons of the neural network as m according to the input dimension m of the matrix, and determining the number of output neurons n according to the dimension n of the output matrix. Determining the number of hidden layers and the corresponding node number according to the number of input and output neurons, wherein the number of hidden layer neuron nodes is as follows:
wherein k is the number of hidden layer neuron nodes, m the number of neurons in the input layer is, n is the efferent layer nerveThe number of elements, delta, is a constant, taking a value of [1,10 ]]；
3) Normalizing the input data and the output data through a normalization model, and respectively taking the normalized input data and the normalized output data as an input P and an output T of a BP neural network structure; setting the number of training data and the number of test data; determining an initial weight and a threshold in a BP neural network structure according to the number of nodes of an input layer, a hidden layer and an output layer;
the normalized model is:
wherein y in the formula is normalized data, and X min Is the minimum value of sample data, X max Is the maximum value of the sample data.
And the input data and the output data after sample normalization are respectively used as the input data P and the output data T of Tent-HBA-BP. Setting the number of training samples and the number of test samples, wherein the number of the training samples accounts for 85 percent of the total number of the samples, and the number of the test samples accounts for 15 percent of the total number of the sample data.
4) And determining the number of parameters needing optimization, namely the number of weights and thresholds in the BP neural network according to the parameter number model, and mapping the number of the weights and the thresholds into the positions of the badgers, wherein the positions of the badgers are vectors.
The number of parameters in the network model is:
j＝k(m+n+1)+n
wherein j is the number of the optimization parameters, k is the number of the hidden layer neurons, m is the number of the input layer neurons, and n is the number of the output layer neurons.
The upper limit of the parameter to be optimized is 1, the lower limit is 0, and the parameter is limited between [0,1 ].
5) Setting the population scale and the maximum iteration number of the badgers, generating an initial population of the badgers through Tent chaos to form a position matrix X of the population of the badgers, inputting training data into an initial BP neural network model, optimizing an initial weight and a threshold, and evaluating the optimal positions of the badgers by taking the minimum error value between a predicted value and an actual value as a fitness function F.
The meliger population adopts a Tent chaotic initial position transformation model as follows:
wherein, phi =0.4,X i Represents randomly generated [0,1]]Initial individuals of inner j-dimensional vectors.
The population initialization process is as follows;
firstly, randomly generating a j-dimension vector in [0,1] as an initial individual, then substituting the initial individual into the above formula to iterate each dimension and generate i-1 new badger individuals, and finally mapping all the badger individuals to a variable value range to generate a population position matrix of the badger. Compared with the randomly generated population, the initial population generated by Tent chaotic mapping has better diversity and can be uniformly distributed in a solution space, so that the defect that the algorithm is easy to converge prematurely is overcome, and the optimization efficiency of the algorithm is improved.
The meliger population positions are represented as the following matrix:
wherein i represents the number of badgers.
The fitness function for the badger positions may be expressed as:
where f represents the fitness (error) of each badger position.
6) Calculating the fitness value of the position of the badger according to the foraging rule and the fitness function of the badger in the badger searching method;
7) Judging the motion trail of the badger according to the control parameters of the digging and honey collecting stages of the badger, updating the position by adopting a trail similar to a cardioid line in the digging stage, and directly updating the position along with the bee in the honey collecting stage;
first, strength is related to the concentration of prey and to the distance between the badgers, I i Is the odor intensity of the prey; if the smell is high, the movement speed is fast, and vice versa
S＝(X i -X i+1 ) 2
d i ＝X prey -X i
Wherein S is the source intensity or concentration intensity; d i Indicating the distance between the prey and the current individual badger
The density factor alpha varies randomly under control to ensure a smooth transition from exploration to production, and the mathematical model is as follows:
wherein t is max C is a constant for the maximum number of iterations, and is 2 by default.
Two conditions of the change of the positions of the badgers:
(1) In case one, the badger will execute the trace like the heart line shape to update its position in the mining phase, and the position model of the heart line is:
X new ＝X prey +F×β×I×X prey +F×r 3 ×α×d i ×|cos(2πr 4 )×[1-cos(2πr 5 )]|
in the formula X prey Beta (default is 6) is the ability of badgers to obtain food, d, which is the best position of badgers so far i Distance between prey and current individual badger 3 、r 4 、r 5 Is [0,1]]F is a sign of changing the search direction, and the mathematical model is:
(2) The second condition, honey badger can directly follow the honeybee in the honey collection stage and reach the honeycomb and adopt honey, and its mathematical model is:
X new ＝X prey +F×r 7 ×α×d i
in the formula r 7 Is [0,1]]A random number within a range;
8) The optimal position and the optimal fitness value of the badger are changed in each iteration, and when the maximum iteration times are met, the optimal position and the optimal fitness value of the badger appear, namely the optimal weight and the threshold of the BP neural network;
9) And retraining the BP neural network structure model by adopting the optimal weight and the threshold value searched by the badger algorithm, and predicting the cutting power of the machine tool under different working conditions according to the training result.
Verification example
(1) The cutting power of the numerical control machine tool is tested, the rotating speed n (r/min) of a main shaft of the machine tool, the feeding speed f (m/min) and the back cutting clearance ap (mm) are used as input variables of a neural network, the cutting power corresponding to the input variables is used as an output variable to test the cutting energy consumption of the numerical control machine tool, and the cutting power is arranged as shown in table 1.
TABLE 1 Experimental data on the cutting power of machine tools
(2) The dimension of the input P is 3, so the number of the input layer units is determined to be 3; since the output T dimension is 1, the number of output layer cells is determined to be 1. And (3) calculating the number of hidden layer neurons to be 7, setting the population scale to be 20, setting the iteration number maxgen =30, and setting the proportion of the training set to be 85%.
(3) And (3) optimizing the BP neural network by using an improved badger algorithm according to the data in the steps (1) and (2), and comparing the trained result with the output data of the original BP neural network, wherein the result is shown in a table 2.
TABLE 2 BP and Tent-HBA-BP prediction error comparison
(4) The average error of BP prediction data is found to be about 3.3% through analysis of test data, BP prediction data and Tent-HBA-BP prediction data, the average error of BP neural network prediction optimized by the badger is found to be about 0.03%, and the obvious discovery can be made by comparison, the prediction precision of the BP neural network optimized by the badger is obviously improved compared with that of the traditional BP neural network.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications derived therefrom are intended to be within the scope of the invention.
Claims (10)
1. A method for predicting cutting power of a numerical control machine tool is characterized by comprising the following steps: the method comprises the following steps:
1) Preprocessing input and output data according to the input data and the output data;
2) Determining the number of input nodes, the number of output nodes and the number of hidden nodes of the neural network so as to determine the structure of the BP neural network;
3) Normalizing the input data and the output data through a normalization model, and respectively taking the normalized input data and the normalized output data as an input P and an output T of a BP neural network structure; setting the number of training data and the number of test data; determining an initial weight and a threshold in a BP neural network structure according to the number of nodes of an input layer, a hidden layer and an output layer;
4) Setting the weight and the threshold value as the initial position of the badger, wherein the position of the badger is a vector;
5) Setting the size and the iteration times of a badger population, generating an initial population of the badger by Tent chaotic mapping to form a position matrix X of the badger population, inputting the initial population of the badger into a BP neural network model, calculating an initial weight, a threshold value and a predicted value of the BP neural network, and calculating the fitness value of the position of the badger by taking an error function between the predicted value and an actual measured value of the BP neural network as a fitness function F;
6) Calculating the fitness value of the position of the badger according to the foraging rule and the fitness function of the badger in the badger search method;
7) Judging the motion trail of the badger according to the control parameters of the digging and honey collecting stages of the badger, updating the position by adopting a trail similar to a cardioid line in the digging stage, and directly updating the position along with the bee in the honey collecting stage;
8) The optimal position and the optimal fitness value of the badger are changed in each iteration, and when the maximum iteration times are met, the optimal position and the optimal fitness value of the badger appear, namely the optimal weight and the threshold of the BP neural network;
9) And retraining the BP neural network structure model by adopting the optimal weight and the threshold value searched by the badger algorithm, and predicting the cutting power of the machine tool under different working conditions according to the training result.
2. The method for predicting cutting power of a numerical control machine according to claim 1, wherein: the framework of the BP neural network in the step 2) is three layers, namely an input layer, a hidden layer and an output layer in sequence, wherein the number of neurons in the input layer is 3, the number of neurons in the hidden layer is 5, and the number of neurons in the output layer is 1.
3. The method for predicting cutting power of a numerical control machine according to claim 1, wherein: the normalized formula in the step 3) is as follows:
wherein y in the formula is normalized data, and X min Is the minimum value of sample data, X max Is the maximum value of the sample data.
4. The method for predicting cutting power of a numerical control machine according to claim 1, wherein: in the step 4), according to the input and output of the neural network and the number of hidden layer nodes, the established dimension model of the badger position vector is as follows:
j＝k(m+n+1)+n
wherein j is the number of the optimization parameters, k is the number of the hidden layer neurons, m for the input layer of the number of neurons, n is the output layer neuron number.
5. The method for predicting cutting power of a numerical control machine according to claim 1, wherein: the transformation model of the initial positions of the badger population adopting Tent chaotic mapping in the step 5) is as follows:
wherein, the first and the second end of the pipe are connected with each other, φ＝ 0 .4 ，X i represents randomly generated [0,1]]Initial individuals of inner j-dimensional vectors.
6. The method for predicting cutting power of a numerical control machine according to claim 1, wherein: generating an initial badger population through Tent mapping in the step 5), wherein a matrix X of the position of the initial badger population is as follows:
wherein i represents the number of badgers.
7. The method for predicting cutting power of a numerical control machine according to claim 1, wherein: the fitness function F in the step 6) is as follows:
wherein f represents the fitness of each position of the badger.
8. The method for predicting cutting power of a numerical control machine according to claim 1, wherein: determining the position change track of the badgers according to the foraging rule of the badgers in the step 7) and the control parameter R of the digging and honey collecting stages; r is a random number between 0 and 1, if R is less than 0.5, the badger enters the digging stage, and if R is more than 0.5, the badger enters the honey collecting stage;
the strength is related to the concentration of prey and to the distance between the badgers, I i Is the odor intensity of the prey; if the smell is high, the movement speed is high, and vice versa;
S＝(X i -X i+1 ) 2
d i ＝X prey -X i
wherein S is the source intensity or concentration intensity; d i Representing the distance between the prey and the current individual badger;
the density factor alpha is controlled to randomly change so as to ensure smooth transition from exploration to exploitation, and the mathematical model of the density factor alpha is as follows:
wherein t is max C is a constant for the maximum number of iterations, and is 2 by default.
9. The method for predicting cutting power of a numerical control machine according to claim 8, wherein: the badger will carry out the orbit like heart line shape in the excavation stage and update own position, and the position model of heart line is:
X new ＝X prey +F×β×I×X prey +F×r 3 ×α×d i ×|cos(2πr 4 )×[1-cos(2πr 5 )]|
in the formula X prey As the best position of the badgers so far, beta (default is 6) is the capacity of the badgers to take food,
d i distance between prey and current individual badger 3 、r 4 、r 5 Is [0,1]]The mathematical model of the random numbers is as follows:
10. The method of claim 8, wherein the melbadger directly reaches the honeycomb to collect honey in the honey collection stage by following the bee, and the mathematical model is as follows:
X new ＝X prey +F×r 7 ×α×d i
in the formula r 7 Is [0,1]]Random numbers within a range.
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