Experimental and artificial neural network ANN investigation
of bending fatigue behavior of glass fiber/polyester composite shafts
الباحث الأول:
د. عبد الكريم فليح حسن
الباحثين الآخرين:
د. لؤي صادق محمد علي
د. حسام جواد عبد الصمد
المجلة:
Journal of the Brazilian Society of Mechanical Sciences and Engineering
تاريخ النشر:
None
مختصر البحث:
This study includes an experimental investigation for rotating bending fatigue in five types of composite material shaft with
comparison to a standard type made of pure polyester material. In addition, an artificial neural network ANN prediction is…
This study includes an experimental investigation for rotating bending fatigue in five types of composite material shaft with
comparison to a standard type made of pure polyester material. In addition, an artificial neural network ANN prediction is
compared with experimental part. The five types vary according to the type of glass fiber fabric (thin and thick fiber fabric)
and fiber volume fraction (10.2 and 14.6% for thin fibers, and 14.6, 29.3 and 43.9% for thick fibers). Tensile tests are
performed to identify the range of applied fatigue stress for every type. Specimens weight is measured in order to calculate
the ratio of strength to weight. The rotating bending fatigue tests are performed to record the fatigue behavior for each type.
Results show that the type with (29.3%) volume fraction of thick fiber has the maximum tensile strength to weight ratio
with (290%) improvement compared with the standard type. The best fatigue behavior is for type with (14.6%) volume
fraction of thin glass fibers. This type has the maximum value of fatigue stress at life limit of (106 cycles) with (362%)
improvement compared with the standard type. Then, the types with (29.3%) and (43.9%) volume fractions of thick glass
fibers make (356%) and (331%) improvement of fatigue stress, respectively. The ANN gives satisfactory results and it is a
good prediction tool for fatigue life of composite materials.
