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Neural Network Analysis

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Presentation on theme: "Neural Network Analysis"— Presentation transcript:

1 Neural Network Analysis
CSIRO

2 critical stress intensity
Charpy fatigue tensile critical stress intensity corrosion

3 Axioms All properties can be measured.
Measurements can be used in safe design. Measurements can be used in control. Self evident truth, statement which commands general belief, an unverifiable although widely accepted fact

4 Dogma Given a comprehensive description of material, process and structure, it is not yet possible to predict most properties. Dogma - statement of belief

5 hardness

6 Variables C, Mn, Si, Ni, Cr, Mo, V, Co, B, N, O…..
Thermomechanical processing of steel Welding consumable Welding parameters Subsequent heat treatment

7 Conclusions There are useful ways of expressing hardness
Limited models relating hardness to microstructure No method for predicting hardness in general Dogma - statement of belief

8 large numbers of variables uncertainties exploit large knowledge base
Solution non-linear functions large numbers of variables uncertainties exploit large knowledge base Dogma - statement of belief

9 Empirical Equations y = a + b (%C) +c (%Mn) + d (%Ni) .... 1223

10 y = a + b (%C) +c (%Mn) + d(%C x %Mn)

11 y = a + b (%C) +c (%Mn) + d(%C x %Mn) y = sin (%C) + tanh (%Mn)

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13 Hyperbolic Tangents

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16 non-linear functions

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21 y A B x

22 Brun, Robson, Narayan, MacKay & Bhadeshia, 1998

23 Components of Creep Strength, 2.25Cr1Mo
iron + microstructure 550 °C solid solution 600 °C precipitates Murugananth & Bhadeshia, 2001

24 Cole & Bhadeshia, 1999

25 GTA weld at 823 K (data from Nippon Steel)
600 500 400 300 200 100 20000 30000 40000 Life / hours Cole & Bhadeshia, 1999

26 Cole & Bhadeshia, 1999

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28 Cool, 1996

29 Cool, 1996

30 600 °C As-welded 700 °C 650 °C Cool, 1996

31 Siemens Mitsui Babcock Nippon Steel ABB

32 Nickel base alloy FT750dc wt%

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34 Tancret & Bhadeshia, 2002 1000 800 600 Yield stress / MPa 400 200 200 400 600 800 1000 1200 Temperature / °C

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