Predictive modeling of fracture behavior in Ti6Al4V alloys manufactured by SLM process

التفاصيل البيبلوغرافية
العنوان:	Predictive modeling of fracture behavior in Ti6Al4V alloys manufactured by SLM process
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

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الوصف
تدمد:	19718993
DOI:	10.3221/IGF-ESIS.68.23