Failure prediction

Strength predictions of composites are ia general quite complex and somewhat limited. This is particularly tme of compressive and shear strengths, which are needed, together with the tensile strengths, ia composite failure prediction. 

The system must produce diagnostic and failure prediction information in a timely manner before serious problems occur on the machines monitored. 

The Tsai-Hill failure criterion appears to be much more applicable to failure prediction for this E-glass-epoxy composite material than either the maximum stress criterion or the maximum strain failure criterion. Other less obvious advantages of the Tsai-Hill failure criterion are ... 

Figure 6-6, are insufficient for failure prediction of orthotropic (and anisotropic) plates. Moreover, if the plates are laminated, the comparison of stress states with failure stress states must be done on a layer-by-layer basis. 

For most practical purposes, the onset of plastic deformation constitutes failure. In an axially loaded part, the yield point is known from testing (see Tables 2-15 through 2-18), and failure prediction is no problem. However, it is often necessary to use uniaxial tensile data to predict yielding due to a multidimensional state of stress. Many failure theories have been developed for this purpose. For elastoplastic materials (steel, aluminum, brass, etc.), the maximum distortion energy theory or von Mises theory is in general application. With this theory the components of stress are combined into a single effective stress, denoted as uniaxial yielding. Tlie ratio of the measure yield stress to the effective stress is known as the factor of safety. 

Bowles, D.E. and Griffin, O.H. (1991b). Micromechanics analysis of space simulated thermal stresses in composites, part II Multidirectional laminates and failure predictions. J. Reinforced Plast. Composites 10, 522-539. 

Garg A.C. and Mai Y.W. (1988b). Failure prediction in toughened epoxy resins. Composites Sci. Technol. 31, 225-242. 

Bowden et al. [1994b] observed that a history of lithium nonresponse predicted lithium nonresponse, but not valproate nonresponse. Taken together, these studies suggest that a history of lithium nonresponse does not predict valproate nonresponse. Notably, no studies have examined whether a history of valproate treatment failure predicts future valproate or lithium response. 

The above results are applicable to tensile loading. When a bending moment is applied, the behavior is modified. In this case, the stress is redistributed by both matrix cracking and fiber failure. Predictions of the UTS in pure flexure (Fig. 1.17) indicate the salient phenomena.64... 

A first aim in the present study was to establish whether the approach proposed by Femlund Spelt, which will be described in more detail below, could be applied to bonded composite joints. A second objective was to evaluate the benefits to be gained from a fracture mechanics characterisation when industrial structural assemblies are considered, both qualitatively in material selection, and quantitatively when failure predictions are required. 

The recorded operating parameters captured by state-of-health data have great utility when a problem exists, the operator can review the most recently collected state-of-health data to diagnose the problem and determine corrective action. In addition, state-of-health data can be scanned automatically to attempt failure prediction. Reanalysis of state-of-health data from the systems described in this chapter has identified subtle hardware failures not detected by system operators until months later. 

Testing of real components can provide information regarding the susceptibility of the component to crevice corrosion. The propagation of crevice corrosion is often similar to that of other forms of localized corrosion, so failure prediction... 

Fracture mechanics applies the physics of stress and strain, and the theories of elasticity and plasticity to microscopic defects found in real materials in order to predict their macroscopic mechanical failure. Fractography is widely used along with fracture mechanics to understand the causes of failures and also to verify the theoretical failure predictions with real life failures. 

Las V, Zemcfk R, Kroupa T, Kottner R. Failure prediction of composite materials. Bull Appl Mech 2008 4 81-7. 

Soutis C, Fleck NA, Smith PA. Failure prediction technique for compression loaded carbon fibre-epoxy laminate with open holes. J Compos Mater 1991 25 1476-98. 

Failure prediction using bearing-bypass diagrams... 

ANTHONY G. EVANS is the Gordon McKay Professor of Materials Engineering at Harvard University. His research interests include the mechanical properties of brittle materials particularly the fracture of ceramics under conditions of impact thermal and mechanical stress and failure prediction based on nondestructive evaluation. He is a recipient of the American Ceramic Society s Ross ColEn Purdy Award and has authored and co-authored several publications. Dr. Evans is a member of the National Materials Advisory Board and has served on several National Research Council committees. Dr. Evans was elected to the National Academy of Engineering in 1997 for his contributions to the development and understanding of structural materials. 

Dao, M. and Asaro, R. (1999) A study on the failure prediction and design criteria for fiber composites under fire degradation. Composites Part A, 30(2), 123-131. 

The noncooled specimen NC failed after 49min of ISO fire exposure. Owing to the fact that the nondimensional slenderness rapidly increased to a high value of 5.6, the ultimate load converged to the Euler buckling load. The time-to-failure prediction could therefore be based on the Euler buckling load prediction. 

Figure 7.14 Time-to-failure prediction and comparison to experimental results, noncooled and water-cooled specimens [17]. (With permission from ASCE.)...

Donaghy, F.A. and Dabbs, T.P. (1988) Subthreshold flaws and their failure prediction in long distance optical fiber cables. J. Lightwave Technol., 6 226-232. 

Battery management systems (BMSs) are real-time systems controlling many functions vital to the correct and safe operation of the electrical energy storage system in EVs and PHEVs. This includes monitoring of temperatures, voltages and currents, maintenance scheduling, battery performance optimization, failure prediction and/or prevention as well as battery data collection/analysis. 

Hj verdict of rock mass failure predicted by applying DRRC as right ... 

Pirondi A, Moroni F (2009), An investigation of fatigue failure prediction of adhesively bonded metal/metal joints. International Journal of Adhesion Adhesives, 29, 796-805. 

DoT (USA) (2005), Methods of analysis and failure predictions for adhesively bonded joints of uniform and variable bondline thickness , Washington DC Federal Aviation Administration, Report no. lX)T/FAA/AR-05/12. 

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