Analysis of p-SPT specimens using Gurson parameters ascertained by Artificial Neural Network. (December 2020)

• Record Type: Journal Article
• Authors: Shikalgar, Taslim D.; Dutta, B.K.; Chattopadhyay, J.
• Is Part Of: Engineering fracture mechanics. Volume 240(2020)
• Publication Date: 2020-12
• Subjects: Structural steels -- p-SPT specimen -- GTN parameters -- Artificial Neural Network -- J-R and crack growth data; Fracture mechanics -- Periodicals; Rupture, Mécanique de la -- Périodiques; Fracture mechanics; Periodicals; 620.112605
• DOI: 10.1016/j.engfracmech.2020.107324
• Languages: English
• ISSNs: 0013-7944

Abstract

Highlights: Analysis of p-SPT specimens using GTN model and comparison with experimental data. Identification of GTN material parameters by employing Artificial Neural Network. Comparison of computed and experimental load-displacement verified GTN parameters. Computed J-R curve using CTOD matched well with experimental data for two materials. p-SPT specimens are useful to assess J-init and J-R data for the materials in service. Abstract: Finite Element Analysis with Gurson-Tvergaard-Needleman (GTN) damage model is used to analyze p-SPT specimens of two structural steels. Load-displacement curves are calculated by parametric variation of GTN parameters. An ANN is trained using load-displacement data as input and GTN parameters as output. Trained ANN is used against p-SPT experimental data to ascertain GTN parameters. Three sets of p-SPT specimens are then analyzed for different a/W ratios. Computed load-displacement curves are in good agreement with experimental results. FE model output is used to calculate (a) J-R material data (b) crack growth at peak load. These are in good agreement with literature values.