Predictive Modeling of Fracture Behavior in Ti6Al4V Alloys Manufactured by SLM Process
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https://orcid.org/0000-0002-9159-8460
Majid Shafaie
Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran.
https://orcid.org/0000-0002-3140-5495
Mohammad Davoodi
Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Ahmad Memaran Babakan
Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Hongyan Zhang
Department of Mechanical, Industrial & Manufacturing Engineering, The University of Toledo, Toledo, OH, USA
Abstract
This study focuses on ductile fracture behavior prediction for Ti6Al4V alloys fabricated via Selective Laser Melting (SLM). A modified Gurson-Tvergaard-Needleman (GTN) model characterizes void growth and shear mechanisms under uniaxial stress. The research explores the impact of Artificial Neural Network (ANN) architecture, specifically hidden layers and neurons, on predicting fracture parameters. Results reveal that increasing hidden layers substantially enhances accuracy, particularly for fracture displacement. Notably, predicting maximum force requires fewer layers than fracture displacement. Using selected layers and neurons, the system consistently achieved R2-values exceeding 0.99 for both maximum force and fracture displacement. The study identifies the initial void volume fraction (f0) parameter as having the most significant influence on both properties.
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