دورية أكاديمية
Predictive modeling of fracture behavior in Ti6Al4V alloys manufactured by SLM process
العنوان: | Predictive modeling of fracture behavior in Ti6Al4V alloys manufactured by SLM process |
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المؤلفون: | Mohsen Sarparast, Majid Shafaie, Mohammad Davoodi, Ahmad Memaran Babakan, Hongyan Zhang |
المصدر: | Frattura ed Integrità Strutturale, Vol 18, Iss 68, Pp 340-356 (2024) |
بيانات النشر: | Gruppo Italiano Frattura, 2024. |
سنة النشر: | 2024 |
المجموعة: | LCC:Mechanical engineering and machinery LCC:Structural engineering (General) |
مصطلحات موضوعية: | fracture, gtn model, am, ann, hidden layers, Mechanical engineering and machinery, TJ1-1570, Structural engineering (General), TA630-695 |
الوصف: | 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 |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | English |
تدمد: | 1971-8993 |
Relation: | https://www.fracturae.com/index.php/fis/article/view/4783/4009; https://doaj.org/toc/1971-8993 |
DOI: | 10.3221/IGF-ESIS.68.23 |
URL الوصول: | https://doaj.org/article/b629703db67f4dc4815677c732ee7d17 |
رقم الأكسشن: | edsdoj.b629703db67f4dc4815677c732ee7d17 |
قاعدة البيانات: | Directory of Open Access Journals |
تدمد: | 19718993 |
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DOI: | 10.3221/IGF-ESIS.68.23 |