با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Delamination analysis using cohesive zone model: A discussion on traction-separation law and mixed-mode criteria
تجزیه و تحلیل لایه لایه شدن با استفاده از مدل منطقه منسجم: بحث در مورد قانون جداسازی کشش و معیارهای حالت مختلط-2020
A discussion on cohesive zone model formulation for prediction of interlaminar damage in composite laminates is presented in this paper. The degradation of interlaminar mechanical properties is analysed from a physical point of view. Firstly, the damage evolution is evaluated according to the traction-separation law and it is demonstrated that if a linear elastic unloading/ reloading curve is assumed, the softening function must also be linear. Secondly, issues regarding damage onset and fracture criteria in mixed-mode loading are critically addressed and commented. A new set of criteria is proposed, and the limitations of existing criteria are discussed.
Keywords: Cohesive zone modelling | Fracture mechanics | Finite element analysis (FEA) | Delamination | Interface fracture
Prediction of displacement in the equine third metacarpal bone using a neural network prediction algorithm
پیش بینی جابجایی در استخوان metacarpal سوم اسب با استفاده از الگوریتم پیش بینی شبکه عصبی-2019
Bone is a nonlinear, inhomogeneous and anisotropic material. To predict the behavior of bones expert systems are employed to reduce the computational cost and to enhance the accuracy of simulations. In this study, an artificial neural network (ANN) was used for the prediction of displacement in long bones followed by ex-vivo experiments. Three hydrated third metacarpal bones (MC3) from 3 thoroughbred horses were used in the experiments. A set of strain gauges were distributed around the midshaft of the bones. These bones were then loaded in compression in an MTS machine. The recordings of strains, load, Load exposure time, and displacement were used as ANN input parameters. The ANN which was trained using 3,250 experimental data points from two bones predicted the displacement of the third bone (R2 ≥ 0.98). It was suggested that the ANN should be trained using noisy data points. The proposed modification in the training algorithm makes the ANN very robust against noisy inputs measurements. The performance of the ANN was evaluated in response to changes in the number of input data points and then by assuming a lack of strain data. A finite element analysis (FEA) was conducted to replicate one cycle of force-displacement experimental data (to gain the same accuracy produced by the ANN). The comparison of FEA and ANN displacement predictions indicates that the ANN produced a satisfactory outcome within a couple of seconds, while FEA required more than 160 times as long to solve the same model (CPU time: 5 h and 30 min).
Keywords: Artificial neural network (ANN) | Displacement prediction | Finite element analysis (FEA) | Expert system | Long bones | Equine third metacarpal bone (MC3)