Quantitative prediction method for loosening defects of large complex thin-wall high-temperature alloy casting

A high-temperature alloy and prediction method technology, which is applied in the direction of prediction, neural learning method, biological neural network model, etc.

Active Publication Date: 2021-02-26
SHANGHAI JIAO TONG UNIV +1
View PDF7 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] Aiming at the defects in the prior art, the object of the present invention is to provide a method for quant

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
  • Quantitative prediction method for loosening defects of large complex thin-wall high-temperature alloy casting
  • Quantitative prediction method for loosening defects of large complex thin-wall high-temperature alloy casting
  • Quantitative prediction method for loosening defects of large complex thin-wall high-temperature alloy casting

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0042] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention. These all belong to protection domain of the present invention.

[0043]The porosity formation mechanism of large complex thin-walled superalloy castings is complicated, and there is no model in the prior art to quantitatively predict porosity defects. Based on this, the embodiment of the present invention implements the quantitative prediction of loose defects in large complex thin-walled superalloy castings by constructing a BP neural network. Specifically, an embodiment of the present invention provides a method for quantitatively predicting loose defects in lar...

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 provides a quantitative prediction method for loosening defects of large complex thin-wall high-temperature alloy casting, which comprises the following steps: analyzing the structure ofa large complex thin-wall casting, and calculating the thermal modulus of each part of the casting; calculating the crystallization temperature interval, density, specific heat capacity and shrinkagecoefficient parameters of the high-temperature alloy, importing the calculated parameters into ProCAST to carry out molding and solidification simulation on the casting, extracting the temperature gradient and cooling speed values, calculating the secondary dendritic crystal interval through a formula, and calculating the secondary dendritic crystal density of the high-temperature alloy; selecting the obtained thermal modulus, temperature gradient and secondary dendrite spacing as input parameters of the BP neural network and the porosity index as output parameters, constructing a BP neural network prediction model, training the BP neural network prediction model by adopting casting body anatomical data, and constructing a casting porosity quantitative prediction network after training, thereby realizing casting porosity defect quantitative prediction. The BP neural network is constructed to realize casting loose defect quantitative prediction, and loose defect sensitive process parameters can be obtained.

Description

technical field [0001] The invention relates to the field of formation and control of superalloy precision casting defects, in particular to a quantitative prediction method for loose defects of large complex thin-walled superalloy castings, electronic equipment and media. Background technique [0002] The aero-engine is the heart of the aircraft, and the design and manufacturing level of the aero-engine plays a decisive role in improving the overall performance of the aircraft. The third-generation commercial engine has very high reliability requirements, with less than 3 air shutdowns per million flight hours, and the life of hot-end components needs to reach 15,000 hours, which puts forward higher requirements for the overall reliability of large complex thin-walled superalloy components requirements, prompting the overall investment casting technology to become one of the mainstream technical routes for the manufacture of advanced aero-engine hot-end components. However...

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): G06F30/15G06F30/23G06F30/27G06N3/08G06Q10/04
CPCG06F30/15G06F30/23G06F30/27G06N3/084G06Q10/04
Inventor 康茂东王俊高海燕王国祥吴贇孙宝德
Owner SHANGHAI JIAO TONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products