Research Papers: Materials and Fabrication

An Artificial Neural Network Model to Predict Material Characteristics From the Results of Miniature Disk Bending Tests

[+] Author and Article Information
A. K. Ghosh

Trombay, Maharastra,
Mumbai 85, India
e-mail: ashok.ghosh@gmail.com

Vishnu Verma

Scientific Officer
Maharastra, Mumbai 85, India
e-mail: vishnuv@barc.gov.in

G. Behera

Scientific Assistant
Maharastra, Mumbai 85, India
e-mail: gourahar@barc.gov.in

1Corresponding author.

Contributed by the Pressure Vessel and Piping Division of ASME for publication in the JOURNAL OF PRESSURE VESSEL TECHNOLOGY. Manuscript received November 20, 2013; final manuscript received March 27, 2014; published online October 13, 2014. Assoc. Editor: Osamu Watanabe.

J. Pressure Vessel Technol 137(1), 011404 (Oct 13, 2014) (7 pages) Paper No: PVT-13-1195; doi: 10.1115/1.4027320 History: Received November 20, 2013; Revised March 27, 2014

The inverse problem of evaluating mechanical properties of material from the observed values of load and deflection of a miniature disk bending specimen is discussed in this paper. It involves analysis of large amplitude, elasto-plastic deformation considering contact and friction. The approach in this work is to first generate—by a finite element (FE) solution—a large database of load-displacement (P-w) records for varying material properties. An artificial neural network (ANN) is trained with some of these data. The errors in the various values of the parameters during testing with additional known data were found to be reasonably small.

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Fig. 1

Schematic drawing of setup for miniature disk bending test; specimen thickness (h) = 0.25 mm; d1 = 1 mm; d2 = 1.5 mm; R = 0.2 mm

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Fig. 2

FE model of MDBT simulation (axisymmetric)

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Fig. 4

Values of total resultant strain in the specimen at a load of 360 N

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Fig. 3

Finite element simulations with different boundary condition and comparison with analytical and experimental results of MDBT

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Fig. 8

Load versus deflection—base case: (a) direct FEM with base values of parameters and (b) FEM with ANN predicted parameters

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Fig. 9

Error in ANN-predicted values of Sy for all 81 cases

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Fig. 6

ANN scheme for evaluating material properties from load–displacement records

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Fig. 7

Details of the ANN in Fig. 6. (Note: The outward connection from all the neurons have not been shown.)

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Fig. 5

Load versus deflection for varying: 5(a)-E; 5(b)-σy; 5(c)-m; 5(d)-A0 (other parameters at the base values)



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