ANN based prediction model for fatigue crack growth in DP steel Academic Article

abstract

• An artificial neural network (ANN)-based model was developed to analyse high-cycle fatigue crack growth rates (da/dN) as a function of stress intensity ranges (K) for dual phase (DP) steel. The training data consisted of da/dN at K ranges between 5 and 16 MPam for DP steel with martensite contents in the range 32 to 76%. The ANN back-propagation model with Gaussian activation function exhibited excellent agreement with the experimental results. The fatigue crack growth rate predictions were made to demonstrate its practical significance in a given real-life situation. Because of the wide range of data points used during training of the model, it will provide a useful predictor for fatigue crack growth in DP steels.

authors

• Haque, Mohammed

published proceedings

• FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES

author list (cited authors)

• Haque, M. E., & Sudhakar, K. V.

citation count

• 21

complete list of authors

• Haque, ME||Sudhakar, KV

publication date

• January 2001

publisher

• Wiley  Publisher

published in

• Fatigue & Fracture of Engineering Materials & Structures (FFEMS)  Journal

keywords

• Artificial Neural Network (ann)
• Dual Phase (dp) Steel
• Fatigue Crack Growth
• Martensite Content
• Stress Intensity Range

Digital Object Identifier (DOI)

• 10.1046/j.1460-2695.2001.00361.x

start page

• 63

end page

• 68

volume

• 24

issue

• 1

URL

• http%3A%2F%2Fdx.doi.org%2F10.1046%2Fj.1460-2695.2001.00361.x