scholarly journals A comparative study of failure criteria applied to composite materials

2008 ◽  
Vol 2 (2) ◽  
pp. 141-147
Author(s):  
E. H. Irhirane ◽  
J. Echaabi ◽  
M. Hattabi ◽  
M. Aboussaleh ◽  
A. Saouab
Keyword(s):  
Composite Materials ◽  
Comparative Study ◽  
Failure Criteria

scholarly journals Introduction to Macroscopic Optimal Design in the Mechanics of Composite Materials and Structures

2021 ◽  
Vol 5 (2) ◽  
pp. 36
Author(s):  
Aleksander Muc
Keyword(s):  
Composite Materials ◽  
Composite Structures ◽  
Constitutive Relations ◽  
A Priori ◽  
Chemical Properties ◽  
Composite Plates ◽  
Failure Criteria ◽  
Lagrange Equations ◽  
Homogenization Methods ◽  
Mechanics Of Composite Materials

The main goal of building composite materials and structures is to provide appropriate a priori controlled physico-chemical properties. For this purpose, a strengthening is introduced that can bear loads higher than those borne by isotropic materials, improve creep resistance, etc. Composite materials can be designed in a different fashion to meet specific properties requirements.Nevertheless, it is necessary to be careful about the orientation, placement and sizes of different types of reinforcement. These issues should be solved by optimization, which, however, requires the construction of appropriate models. In the present paper we intend to discuss formulations of kinematic and constitutive relations and the possible application of homogenization methods. Then, 2D relations for multilayered composite plates and cylindrical shells are derived with the use of the Euler–Lagrange equations, through the application of the symbolic package Mathematica. The introduced form of the First-Ply-Failure criteria demonstrates the non-uniqueness in solutions and complications in searching for the global macroscopic optimal solutions. The information presented to readers is enriched by adding selected review papers, surveys and monographs in the area of composite structures.

Comparative Study of three composite Materials in bonding Attachments for clear aligners

2021 ◽  
Author(s):  
Wener Chen ◽  
Liwen Qian ◽  
Yufen Qian ◽  
Zhechen Zhang ◽  
Xin Wen
Keyword(s):  
Composite Materials ◽  
Comparative Study

Comparative Study on Determining the Drilling Force Regression at Drilling Composites Materials

2013 ◽  
Vol 436 ◽  
pp. 213-218
Author(s):  
Constantin Ocnărescu ◽  
Doru Bardac ◽  
Maria Ocnărescu
Keyword(s):  
Composite Materials ◽  
Comparative Study ◽  
Polymeric Matrix ◽  
Fiber Glass ◽  
Drilling Force ◽  
Composites Materials ◽  
Current Article

Composite materials are used extensively because of their higher strength to weight ratios and, when compared to metals, offer new opportunities for design. However, being non-homogenous, anisotropic and reinforced with very abrasive fibers, these materials are difficult to machine. In this current article is present a comparative study of results obtained in determining the drilling force regression at drilling a tree types composites material with polymeric matrix and fiber glass.

scholarly journals General invariant forms of the anisotropic failure criteria

1993 ◽  
Vol 15 (3) ◽  
pp. 41-48
Author(s):  
Tran Ich Thinh
Keyword(s):  
Composite Materials ◽  
Rotational Symmetry ◽  
Three Dimensional ◽  
Failure Criteria ◽  
Orthotropic Composite ◽  
Hill Criterion ◽  
Special Cases ◽  
Stress States ◽  
The Hill

The general invariant forms of failure criteria for anisotropic solids were studied and applied to orthotropic composite materials. when subjected to three-dimensional stress states with rotational symmetry. The Hill criterion and the Tsai and Wu criterion are special cases of these general forms.

FAILURE PREDICTION OF COMPOSITE MATERIALS USING DEEP NEURAL NETWORKS

2021 ◽  
Author(s):  
ALLYSON FONTES ◽  
FARJAD SHADMEHRI
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Composite Materials ◽  
Composite Laminates ◽  
Deep Neural Networks ◽  
Mean Squared Error ◽  
Failure Criteria ◽  
Failure Strength ◽  
Failure Data ◽  
Frp Composite

Fiber-reinforced polymer (FRP) composite materials are increasingly used in engineering applications. However, an investigation into the precision of conventional failure criteria, known as the World-Wide Failure Exercise (WWFEI), revealed that current theories remain unable to predict failure within an acceptable degree of accuracy. Deep Neural Networks (DNN) are emerging as an alternate and time-efficient technique for predicting the failure strength of FRP composite materials. The present study examined the applicability of DNNs as a tool for creating a data-driven failure model for composite materials. The experimental failure data presented in the WWFE-I were used to develop the datadriven model. A fully connected DNN with 23 input units and 1 output unit trained with a constant learning rate (α=0.0001). The network’s inputs described the laminates and the loading conditions applied to the test specimen, whereas the output was the length of the failure vector (L=(σx+σy+τxy)0.5). The DNN’s performance was evaluated using the mean squared error on a subset of the experimental data unseen during training. Network configurations with a varying number of hidden layers and units per layer were evaluated. The DNN with 3 hidden layers and 20 units per hidden layer performed the best. In fact, the network’s predictions show good agreement with the experimental results. The failure boundaries generated by the DNN were compared to three conventional theories: the Tsai-Wu, Cuntze, and Puck theory. The DNN’s failure envelopes were found to fit the experimental data more closely than the above-mentioned theories. In sum, the DNN’s ability to fit higher-order polynomials to data separates it from conventional failure criteria. This characteristic makes DNNs an effective method for predicting the failure strength of composite laminates.

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