Cast steel liquidity predicting method

A prediction method and fluidity technology, applied in the field of foundry, can solve the problems of increasing cost, prolonging product development and manufacturing cycle, lack of scientificity and reliability, etc., to achieve the effect of promoting digitalization

Inactive Publication Date: 2016-08-17
GUIZHOU UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

External conditions such as pouring temperature, pouring pressure, mold structure complexity, cavity wall thickness and other factors affect the fluidity of cast steel relatively simple and close to linear correlation; The impact of its fluidity has complex nonlinear correlation characteristics, and there is no theoretical formula or empiric

Method used

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Examples

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Embodiment 1

[0022] Embodiment 1: a method for predicting cast steel fluidity, comprising the following steps:

[0023] Step (1): Conduct spiral sample experiments with different grades of cast steel, and collect neural network training sample data. In this example, 26 grades of cast steel materials were selected. Under the condition of controlling the degree of superheat and consistent pouring speed (the degree of superheat is the liquidus temperature of the cast steel + 10°C, and the pouring speed is 0.01m / s), Spiral specimens (such as figure 1 Shown) pour molten steel, take the solid phase mass fraction of the sample when the mold is filled as the fluidity evaluation index w, the sample data includes the percentages of alloy components C, Fe, Mn, P, S and The liquidity evaluation index w, the obtained sample data are shown in Table 1 below:

[0024] Table 1 Neural network training sample data

[0025]

[0026] Step (2): Establish a neural network model for cast steel fluidity pred...

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Abstract

The invention discloses a cast steel liquidity predicting method. The method includes following steps: (1), using cast steel different in trademark for spiral sample experiments, and collecting neutral network training sample data; (2), building a neutral network model for cast steel liquidity predicting, and determining network topology structure of the neutral network model, wherein the type of a neutral network is a multilayer feedforward BP network; (3), extracting part of the sample data from the step (1), and training the neutral network model; (4), using remaining samples in the step (1) to perform simulation testing on the network model after being trained; (5), predicting liquidity of the cast steel of other trademarks through the neutral network model after being trained. Through the neutral network model, the predicting method is provided for cast steel liquidity during cast steel part casting process designing and is conducive to promoting digital, intelligent and energy-saving development of the casting industry.

Description

technical field [0001] The invention relates to a method for predicting the fluidity of cast steel, which belongs to the technical field of casting. Background technique [0002] The fluidity of liquid metal alloy is an important process performance in casting production, and the quality of fluidity directly affects the filling performance of the metal. The fluidity of the molten metal is good, the filling performance is strong, and it is easy to obtain castings with accurate dimensions, complete shapes and clear outlines. Otherwise, it is easy to cause cold shut, insufficient pouring, pores and inclusion defects of castings. [0003] Cast steel alloy is a commonly used material in casting production. Its fluidity is worse than that of gray cast iron, silicon brass alloy, and silicon aluminum alloy. Therefore, for steel castings, the fluidity of its liquid alloy has a great influence on the casting process design. Big. The fluidity of cast steel alloys is affected by many...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N3/04G06N3/08G06Q50/04
CPCG06F30/20G06F2117/08G06F2119/18G06N3/04G06N3/08G06Q50/04Y02P90/30
Inventor 崔晓斌黄放程桐梅益孙津原
Owner GUIZHOU UNIV
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