Prediction method for piston cutting deformation based on BP neural network

A BP neural network and cutting processing technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve the problems of unrealistic real-time prediction, low computing efficiency, and large computing resources consumption, so as to improve the establishment efficiency, The effect of high reliability and shortened forecast time

Pending Publication Date: 2019-07-05
JIANGSU UNIV OF SCI & TECH
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Problems solved by technology

However, cutting physics simulation calculations consume a lot of computing resources, and qualitative analysis is often used for deformation prediction analysis of different working co

Method used

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  • Prediction method for piston cutting deformation based on BP neural network
  • Prediction method for piston cutting deformation based on BP neural network
  • Prediction method for piston cutting deformation based on BP neural network

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Embodiment Construction

[0018] The mechanism of the cutting deformation of the piston is that the blank of the part containing the initial residual stress, under the combined influence of factors such as clamping force load, cutting force load, cutting heat load and the change of constraint conditions caused by a large amount of material removal, its internal stress The field gradually evolves, and after the processing is completed and the clamping is removed, the internal stress field further evolves and causes the deformation of the part, and finally reaches the state of internal stress self-balance. In order to predict and analyze the deformation of the piston cutting process, it is necessary to track the evolution process of the internal stress field under the action of the outside world, and to process the data through efficient computing means to obtain the final machining deformation.

[0019] Such as figure 1 As shown, this example predicts the processing deformation for the rough boring cutt...

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Abstract

The invention discloses a prediction method for piston cutting deformation based on a BP neural network. The method comprises: piston cutting machining simulation is carried out through finite elementanalysis; taking the predicted deformation obtained by the simulation experiment as a training sample, establishing a BP neural network topology model according to the training sample, learning the BP neural network topology model according to preset training parameters, and finally predicting the piston cutting deformation according to the BP neural network topology model after the weights of the neurons are adjusted. Compared with a simulation process prediction method, the method has the advantages that the prediction time is greatly shortened, the establishment efficiency and prediction precision of the prediction model are improved, and a processing prediction guide with relatively high reliability can be quickly provided for production and processing.

Description

technical field [0001] The invention relates to a method for predicting the amount of deformation in piston cutting, in particular to a method for predicting the amount of deformation in piston cutting based on BP neural network. Background technique [0002] As the core component of the diesel engine, the quality of the final product in the process of processing the piston will be closely related to various process factors in the process. If the design process parameters are unreasonable, the problem of the size of the piston skirt will be out of tolerance, which will affect the quality of the piston process. The quality of the product seriously affects the performance of the diesel engine. [0003] Usually, the prediction of piston processing deformation is based on simulated processing and theoretical analysis, and the deformation prediction model is established with the help of finite element analysis technology, so that the deformation prediction analysis is carried out...

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Application Information

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IPC IPC(8): G06F17/50G06N3/08
CPCG06N3/084G06F30/17G06F30/23
Inventor 周宏根郝赛何强田桂中李国超刘金锋冯丰谢占成景旭文
Owner JIANGSU UNIV OF SCI & TECH
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