CN107609647A - One kind is based on BP neural network roll alloy mechanical property Forecasting Methodology - Google Patents
One kind is based on BP neural network roll alloy mechanical property Forecasting Methodology Download PDFInfo
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- CN107609647A CN107609647A CN201710959823.0A CN201710959823A CN107609647A CN 107609647 A CN107609647 A CN 107609647A CN 201710959823 A CN201710959823 A CN 201710959823A CN 107609647 A CN107609647 A CN 107609647A
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Abstract
The invention discloses a kind of based on the roll of improved BP alloy cast steel mechanical property prediction method, belong to alloy cast steel roll technical field.A series of mechanical property test is carried out with the alloy cast steel under different-alloy composition and heat treatment process parameter first against roll, collects and screens the training sample data required for artificial nerve network model；Structure includes the BP artificial nerve network models of input layer, hidden layer and output layer, so as to establish the mapping relations between the alloying component of alloy cast steel roll, heat treatment process parameter and mechanical property；The mechanical property of alloy cast steel roll is predicted using the artificial neural network trained.The forecast precision for the BP neural network model that the present invention establishes is higher, and stability is preferable, and Generalization Ability is strong, and the alloy cast steel roll material further to research and develop new provides new approaches and methods, so as to reduce production cost, shortens the development time.
Description
Technical field
The present invention relates to alloy cast steel roll technical field, and in particular to one kind is based on BP neural network roll alloy mechanics
Performance prediction method.
Background technology
Roll is the primary deformable instrument of milling train, in use they can produce peeling, crackle, fracture the defects of and
Failure, so as to shorten the service life of roll, the service life of roll is the side such as intensity and hardness mainly by internal performance
What face determined, by adjusting its chemical composition and heat treatment process parameter, roller performance requirement can be met to a certain extent.
Therefore, the relation between the composition of research material, technique, performance, so as to search out to meet that the performance of rolling industry demand is excellent
Different roll material, have become roll industry and be faced with new challenges.
By carrying out design of material, so that it is determined that going out the pass between material chemical composition, Technology for Heating Processing, performance
System, a kind of effective method is provided for the exploitation of new material.At present between research roll material composition, technique, performance
During relation, generally use experiment analytical method and formula theoretical method, it is repeated multiple times do experiment must consume the substantial amounts of time and
Property, formula method must establish mathematical regression model in advance, but because material property is influenceed by many factors, and
Nonlinear Mapping relation between these factors be present so that empirical regression formula is complicated and various.Therefore, using area of computer aided
Design of material, convenient and effective method is provided for the research and development of new material.
The content of the invention
It is an object of the invention to overcome the above-mentioned problems in the prior art, there is provided one kind is rolled based on BP neural network
Roller alloy mechanical property Forecasting Methodology, reduce the blindness of experiment and shorten the new material R＆D cycle.
To realize above-mentioned technical purpose and the technique effect, the present invention is to be achieved through the following technical solutions：
One kind is based on BP neural network roll alloy mechanical property Forecasting Methodology, comprises the following steps：
Step S1：By carrying out a system to the alloy cast steel roll material under different-alloy composition and heat treatment process parameter
The mechanical property test of row, is collected and screening test result data, normalization obtain artificial nerve network model institute after pre-processing
The training sample data needed；
Step S2：It is determined that optimal Artificial Neural Network Structures, including the input/output argument of neutral net, hidden layer number
And hidden neuron number；
Step S3：Choose appropriate learning parameter, including the momentum term factor, learning rate, training pace, initial weights and
Threshold value, activation primitive, learning algorithm, are learnt and are trained to neural network model；
Step S4：Model is tested using test sample data, and the neural network prediction model to being established
Accuracy is assessed；
Step S5：The pre- of alloy cast steel roll mechanical property is carried out using artificial nerve network model caused by above-mentioned steps
Survey.
Further, in the step S1, the training sample data collected by experiment are 133 groups.
Further, in the step 2, optimal neural network structure includes：Input layer number is 10, hidden layer
Neuron number is 17, and output layer neuron number is 6.
Further, in the step S3, appropriate learning parameter：The momentum term factor is 0.75, learning rate 0.15, just
The activation primitive of random number of the weights and threshold value of beginning between (- 1,1), hidden layer and output layer is respectively tangent
Sigmoid and log-sigmoid, learning algorithm are improved back-propagation.
Further, in the step S4, the deviation base of neural network prediction alloy cast steel roll mechanical property is ± 5%
Within probability be more than 95%.
Further, in the step S5, using the artificial nerve network model trained, predict and study different conjunctions
Golden composition and heat treatment process parameter obtain optimal synthesis mechanical property to the affecting laws of alloy cast steel roll mechanical property
Energy.
The present invention income effect be：
The mechanical property of alloy cast steel roll can conveniently and be accurately predicted using the present invention, further according to the knot of prediction
Fruit, its composition and Technology for Heating Processing are designed, it is possible to reduce the blindness during alloy cast steel roll design of material, section
Save substantial amounts of time and cost.
Brief description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, used required for being described below to embodiment
Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the present invention, for ability
For the those of ordinary skill of domain, on the premise of not paying creative work, it can also be obtained according to these accompanying drawings other attached
Figure.
Fig. 1 is the mechanical properties prediction prototype network structure of alloy cast steel roll；
Fig. 2 is the change of network performance in neural network training process；
Fig. 3 is the comparison of Hardness Prediction value and measured value；
Fig. 4 is the comparison of tensile strength predicted value and measured value；
Fig. 5 is the comparison of prediction of yield strength value and measured value；
Fig. 6 is the comparison of elongation percentage predicted value and measured value；
Fig. 7 is the comparison of impact flexibility predicted value and measured value；
Fig. 8 is the comparison of contraction percentage of area predicted value and measured value.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained all other under the premise of creative work is not made
Embodiment, belong to the scope of protection of the invention.
As shown in figures 1-8, the present invention is based on BP neural network roll alloy mechanical property Forecasting Methodology for one kind, including such as
Lower step：
Step S1：By carrying out a system to the alloy cast steel roll material under different-alloy composition and heat treatment process parameter
The mechanical property test of row, is collected and screening test result data, normalization obtain artificial nerve network model institute after pre-processing
The training sample data needed；
Step S2：It is determined that optimal Artificial Neural Network Structures, including the input/output argument of neutral net, hidden layer number
And hidden neuron number；
Step S3：Choose appropriate learning parameter, including the momentum term factor, learning rate, training pace, initial weights and
Threshold value, activation primitive, learning algorithm, are learnt and are trained to neural network model；
Step S4：Model is tested using test sample data, and the neural network prediction model to being established
Accuracy is assessed；
Step S5：The pre- of alloy cast steel roll mechanical property is carried out using artificial nerve network model caused by above-mentioned steps
Survey.
Wherein, in step S1, the training sample data collected by experiment are 133 groups.
Wherein, in step 2, optimal neural network structure includes：Input layer number is 10, hidden neuron
Number is 17, and output layer neuron number is 6.
Wherein, in step S3, appropriate learning parameter：The momentum term factor is 0.75, learning rate 0.15, initial weights
With random number of the threshold value between (- 1,1), the activation primitive of hidden layer and output layer is respectively tangent sigmoid and log-
Sigmoid, learning algorithm are improved back-propagation.
Wherein, in step S4, the deviation base of the neural network prediction alloy cast steel roll mechanical property probability within ± 5%
More than 95%.
Wherein, in step S5, using the artificial nerve network model trained, predict and study different alloying components and
Heat treatment process parameter obtains optimal comprehensive mechanical property to the affecting laws of alloy cast steel roll mechanical property.
One of the present embodiment has particular application as：
Collect and screen to obtain training sample data of 113 groups of test datas as neutral net, carrying out neutral net
, it is necessary to which pretreatment is normalized to training sample data before training.Determine alloying component (C, Si, Mn, Cr, Mo, V, W,
Ni) and input parameter of the heat treatment process parameter (hardening heat, temperature) as neutral net, the power of alloy cast steel roll
The output that performance (hardness, tensile strength, yield strength, elongation percentage, impact flexibility, the contraction percentage of area) is learned as neutral net is joined
Number.Optimal neural network structure includes：Input layer number is 10, and hidden neuron number is 17, output layer nerve
First number is 6, as shown in Figure 1.By the way that to the multiple training of neutral net, it is suitable to determine under different learning parameters
Neural Network Training Parameter：The momentum term factor is 0.75, learning rate 0.15, initial weights and threshold value are between (- 1,1)
Random number, the activation primitive of hidden layer and output layer is respectively tangent sigmoid and Log-sigmoid, and using improving
BP algorithm network is learnt and trained.
In the training stage, the change of network error performance is as shown in Figure 2, it can be seen that neural metwork training is by about
After 50000 circulations, reach the anticipation error 0.005 of network, illustrate that the convergence rate of network is very fast, do not overtrain
Situation, training effect is preferable.The test sample data outside training sample set are chosen, utilize the artificial neural network mould trained
Type carries out the prediction of alloy cast steel roll mechanical property, and is compared with test value, it is found that neutral net is rolled to alloy cast steel
The prediction deviation of roller mechanical property is substantially all within ± 5%, in addition, by the method for statistical regression to predicted value and measured value
Analyzed, it is known that have very high Relativity of Coefficients between them.
As shown in figures 3-8, show that the forecast precision of BP neural network is higher, stability is preferable, and Generalization Ability is strong, Ke Yiman
Sufficient actual requirement.
In the description of this specification, the description of reference term " one embodiment ", " example ", " specific example " etc. means
At least one implementation of the present invention is contained in reference to specific features, structure, material or the feature that the embodiment or example describe
In example or example.In this manual, identical embodiment or example are not necessarily referring to the schematic representation of above-mentioned term.
Moreover, specific features, structure, material or the feature of description can close in any one or more embodiments or example
Suitable mode combines.
Present invention disclosed above preferred embodiment is only intended to help and illustrates the present invention.Preferred embodiment is not detailed
All details are described, it is only described embodiment also not limit the invention.Obviously, according to the content of this specification,
It can make many modifications and variations.This specification is chosen and specifically describes these embodiments, is to preferably explain the present invention
Principle and practical application so that skilled artisan can be best understood by and utilize the present invention.The present invention is only
Limited by claims and its four corner and equivalent.
Claims (6)
1. one kind is based on BP neural network roll alloy mechanical property Forecasting Methodology, it is characterised in that comprises the following steps：
Step S1：It is a series of by being carried out to the alloy cast steel roll material under different-alloy composition and heat treatment process parameter
Mechanical property test, is collected and screening test result data, normalization are obtained after pre-processing required for artificial nerve network model
Training sample data；
Step S2：It is determined that optimal Artificial Neural Network Structures, including the input/output argument of neutral net, hidden layer number and
Hidden neuron number；
Step S3：Choose appropriate learning parameter, including the momentum term factor, learning rate, training pace, initial weights and threshold
Value, activation primitive, learning algorithm, are learnt and are trained to neural network model；
Step S4：Model is tested using test sample data, and the neural network prediction model to being established is accurate
Property is assessed；
Step S5：The prediction of alloy cast steel roll mechanical property is carried out using artificial nerve network model caused by above-mentioned steps.
2. one kind according to claim 1 is based on BP neural network roll alloy mechanical property Forecasting Methodology, its feature exists
In：In the step S1, the training sample data collected by experiment are 133 groups.
3. one kind according to claim 1 is based on BP neural network roll alloy mechanical property Forecasting Methodology, its feature exists
In：In the step 2, optimal neural network structure includes：Input layer number is 10, and hidden neuron number is
17, output layer neuron number is 6.
4. one kind according to claim 1 is based on BP neural network roll alloy mechanical property Forecasting Methodology, its feature exists
In：In the step S3, appropriate learning parameter：The momentum term factor is 0.75, learning rate 0.15, initial weights and threshold value
For the random number between (- 1,1), the activation primitive of hidden layer and output layer is respectively tangent sigmoid and log-
Sigmoid, learning algorithm are improved back-propagation.
5. one kind according to claim 1 is based on BP neural network roll alloy mechanical property Forecasting Methodology, its feature exists
In：In the step S4, the deviation base of the neural network prediction alloy cast steel roll mechanical property probability within ± 5% is more than
95%.
6. one kind according to claim 1 is based on BP neural network roll alloy mechanical property Forecasting Methodology, its feature exists
In：In the step S5, using the artificial nerve network model trained, different alloying components and heat treatment are predicted and studied
Technological parameter obtains optimal comprehensive mechanical property to the affecting laws of alloy cast steel roll mechanical property.
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| CN108897925A (en) * | 2018-06-11 | 2018-11-27 | 华中科技大学 | A kind of casting technological parameter optimization method based on casting defect prediction model |
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