Shot peening strengthening surface integrity prediction method based on BP neural network
A BP neural network and surface integrity technology, applied in neural learning methods, biological neural network models, predictions, etc., can solve the complex model establishment process, cannot predict residual stress, and does not consider the correlation of input parameters of neural network prediction models, etc. problem, achieve the effect of reducing physical tests and improving efficiency
A BP neural network and surface integrity technology, applied in neural learning methods, biological neural network models, predictions, etc., can solve the complex model establishment process, cannot predict residual stress, and does not consider the correlation of input parameters of neural network prediction models, etc. problem, achieve the effect of reducing physical tests and improving efficiency
CN111401623APending Publication Date: 2020-07-10CHONGQING UNIV
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[0020] Below in conjunction with accompanying drawing and embodiment the present invention will be further described:
[0021] This embodiment is the surface integrity prediction of gear shot peening, including the following steps:
[0022] S1. Collect and sort out the shot peening test data. By analyzing the process parameters and surface integrity evaluation indicators that affect the shot peening performance during the shot peening process, the shot peening process parameters and material parameters are used as input parameters. After shot peening, the surface of the material is complete. The residual stress and surface roughness in the property parameters are the output parameters; and the input data is preprocessed, the preprocessing includes: deleting outliers, supplementing default values, performing feature dimensionality reduction on the data set, and normalizing the data Divide the test data into training set and test set according to appropriate proportion.
[0023...
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The invention discloses a shot peening strengthening surface integrity prediction method based on a BP neural network. The method comprises the following steps: 1, collecting and arranging part shot peening strengthening test data, and determining input and output parameters of a surface integrity prediction model by analyzing process parameters and surface integrity evaluation indexes influencingshot peening performance in a shot peening strengthening process; preprocessing the data; 2, determining a hidden layer activation function type and a hidden layer node number, and constructing a BPneural network structure; 3, optimizing the initial weight and bias of the BP neural network through a genetic algorithm, and establishing a shot peening strengthening residual stress and surface roughness prediction model; 4, determining the weight and bias in the BP neural network structure according to the precision evaluation parameter R2; 5, predicting the shot peening strengthening residualstress and surface roughness by using the trained model meeting the precision requirement. The test cost of the shot peening strengthening process can be reduced, and the efficiency of the shot peening process is improved.
Description
technical field [0001] The invention belongs to the technical field of intelligent manufacturing, and in particular relates to a method for predicting surface integrity of shot peening strengthening. Background technique [0002] Shot peening is a widely used surface strengthening process at present, and it is often used for surface strengthening of key components in high-speed trains, aerospace, ships and other fields. Shot peening can introduce residual compressive stress on the surface of the part, improve the surface structure of the part, and delay the fatigue failure of the part. Shot peening treatment of parts can improve fatigue resistance, which is of great significance to improve their service performance. [0003] At present, in the shot peening process of parts, the research on the law of process parameters such as projectile diameter, spray angle, spray velocity, coverage rate, spray pressure and shot flow, and surface integrity evaluation indicators such as re...
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Patent Timeline
10 Jul 2020
Publication
CN111401623A
- IPC
- G06Q10/04; G06N3/04; G06N3/08; G06N3/12; C21D7/06
- CPC
- G06Q10/04; G06N3/084; G06N3/126; C21D7/06; G06N3/045; Y02P10/20
- Inventors
- 刘怀举; 吴少杰