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.
An Artificial Neural Network Model to Predict Material Characteristics From the Results of Miniature Disk Bending Tests
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.
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Ghosh, A. K., Verma, V., and Behera, G. (October 13, 2014). "An Artificial Neural Network Model to Predict Material Characteristics From the Results of Miniature Disk Bending Tests." ASME. J. Pressure Vessel Technol. February 2015; 137(1): 011404. https://doi.org/10.1115/1.4027320
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