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Design assistance method for metal material, prediction model generation method, metal material manufacturing method, and design assistance device

A metal material, prediction model technology, applied in the direction of calculation model, manufacturing calculation system, prediction, etc., can solve the problem that the neural network cannot predict the content and so on

Pending Publication Date: 2021-08-31
JFE STEEL CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

In addition, Patent Document 2 records that the training results of the neural network are obtained within the condition range of the training data set, and the neural network cannot predict the content of the range outside its condition

Method used

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  • Design assistance method for metal material, prediction model generation method, metal material manufacturing method, and design assistance device
  • Design assistance method for metal material, prediction model generation method, metal material manufacturing method, and design assistance device
  • Design assistance method for metal material, prediction model generation method, metal material manufacturing method, and design assistance device

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no. 1 Embodiment approach

[0040] Hereinafter, a first embodiment of the present invention will be described. The metal material designed in the first embodiment is, for example, steel, but the metal material is not limited to steel, and may be any metal.

[0041] figure 1 It is a functional block diagram showing the configuration of the design support device 1 according to the first embodiment of the present invention. Such as figure 1 As shown, the design support device 1 of the first embodiment is a computer including an acquisition unit 11 , a predictive model construction unit 12 , a search unit 13 , and a presentation unit 14 . The design support device 1 supports the design of a metal material having desired properties.

[0042] The acquisition unit 11 acquires, for example, past actual performance data of steel material production, which is necessary for constructing a prediction model described later. The acquisition unit 11 may include a communication interface for acquiring performance da...

no. 2 Embodiment approach

[0088] Hereinafter, a second embodiment of the present invention will be described. Configuration, basic functions, and use of the design support device 1 of the second embodiment Figure 1 to Figure 5 The above-mentioned contents related to the described first embodiment are the same. Therefore, the corresponding contents described in the first embodiment are similarly applied to the second embodiment. Components that are the same as those in the first embodiment are assigned the same reference numerals, and description thereof will be omitted. Hereinafter, points different from the first embodiment will be mainly described. In the second embodiment, unlike the first embodiment, in the evaluation function of Equation 1, variations in predicted values ​​of characteristics due to training data are considered. In this specification, the "variation of predicted values" includes, for example, deviations of a plurality of predicted values ​​based on a plurality of different trai...

no. 3 Embodiment approach

[0110] Hereinafter, a third embodiment of the present invention will be described. Configuration, basic functions, and use of the design support device 1 of the third embodiment Figure 1 to Figure 6 The above-mentioned content related to the first embodiment described is the same as the above-mentioned content related to the second embodiment. Therefore, the corresponding contents described in the first embodiment and the second embodiment are similarly applied to the third embodiment. The same configurations as those of the first embodiment and the second embodiment are denoted by the same reference numerals, and description thereof will be omitted. Hereinafter, points different from the first embodiment and the second embodiment will be mainly described. In the third embodiment, unlike the first and second embodiments, the difference between the design condition to be searched and the design condition of past performance data is taken into consideration in the evaluation ...

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Abstract

Provided is a design assistance method which enables a prediction value to be accurately obtained, while taking into consideration the manufacturing conditions of a metal material, and through which the load of the time taken to design the metal material can be reduced. A design assistance method according to the present invention, which assists designing a metal material having desired characteristics by means of a calculator, includes: a searching step for searching for design conditions, by which the desired characteristics can be obtained, by using a prediction model which is constructed on the basis of past result data obtained by associating design conditions, which include a component composition and a manufacturing condition of the metal material, with a characteristic value of the metal material, and which predict the characteristic value from the design conditions; and a presenting step for presenting at least the component composition and the manufacturing conditions among the design conditions that are searched in the search step and correspond to the desired characteristics, wherein, in the presentation step, the design conditions are searched so that the difference between a plurality of prediction values, which are based on a plurality of difference training data sets, is reduced.

Description

technical field [0001] The present invention relates to a design support method of a metal material having desired properties, a method of generating a predictive model, a method of manufacturing a metal material, and a design support device. Background technique [0002] When designing a new material, the user will repeatedly perform the steps of determining the design conditions empirically and reconsidering the design based on the trial production results of the material through trial and error. Therefore, it takes time until the result of the user's design is grasped, and as a result, the load of time taken until designing a material having sufficient characteristics increases. Also, since the design content depends on the experience of the user, past experience accumulated by the user may hinder new ideas. [0003] The accuracy of predictive models using machine learning has improved dramatically in recent years. In order to reduce the above-mentioned time load, the t...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/04
CPCB22D11/16G16C60/00C22C38/02C22C38/04C22C38/06C22C38/001C22C38/60C22C38/14C22C38/12C22C38/002C21D11/00C21D8/0226C21D8/0263C21D1/26G06N20/20G06N3/045G06Q10/04G06Q10/063G06Q50/04G06Q50/10G06N20/00Y02P90/30G06F30/27G06V10/443C22F1/00G06F18/214
Inventor 中辻一浩山口收高木宏征
Owner JFE STEEL CORP
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