Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

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
View PDF5 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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 conditions in specific machining processes. The calculation efficiency is low, and real-time prediction of each working condition through cutting simulation is often used. is impractical

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • 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

Examples

Experimental program
Comparison scheme
Effect test

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...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com