Measures of Entropy to Characterize Fatigue Damage in Metallic Materials

Abstract

This paper presents the entropic damage indicators for metallic material fatigue processes obtained from three associated energy dissipation sources. Since its inception, reliability engineering has employed statistical and probabilistic models to assess the reliability and integrity of components and systems. To supplement the traditional techniques, an empirically-based approach, called physics of failure (PoF), has recently become popular. The prerequisite for a PoF analysis is an understanding of the mechanics of the failure process. Entropy, the measure of disorder and uncertainty, introduced from the second law of thermodynamics, has emerged as a fundamental and promising metric to characterize all mechanistic degradation phenomena and their interactions. Entropy has already been used as a fundamental and scale-independent metric to predict damage and failure. In this paper, three entropic-based metrics are examined and demonstrated for application to fatigue damage. We collected experimental data on energy dissipations associated with fatigue damage, in the forms of mechanical, thermal, and acoustic emission (AE) energies, and estimated and correlated the corresponding entropy generations with the observed fatigue damages in metallic materials. Three entropic theorems—thermodynamics, information, and statistical mechanics—support approaches used to estimate the entropic-based fatigue damage. Classical thermodynamic entropy provided a reasonably constant level of entropic endurance to fatigue failure. Jeffreys divergence in statistical mechanics and AE information entropy also correlated well with fatigue damage. Finally, an extension of the relationship between thermodynamic entropy and Jeffreys divergence from molecular-scale to macro-scale applications in fatigue failure resulted in an empirically-based pseudo-Boltzmann constant equivalent to the Boltzmann constant.

Estimating damage size and remaining useful life in degraded structures using deep learning-based multi-source data fusion

“…The waveform threshold, corresponding timing parameters, resulting AE features, their definition, and corresponding considerations are reported elsewhere. 42,44 Regarding the loading conditions in Yun's 42 experiments, the maximum load for Experiments 1-5 is reported to be 13-17 kN, respectively, with stress ratio of 0.1 and frequency of 5 Hz. Loading was ceased every 1000 cycles for 2 min to capture images with a frequency of 0.2 Hz.…”

Low Cycle Fatigue Life Prediction Using Unified Mechanics Theory in Ti-6Al-4V Alloys

“…In unified mechanics theory [ 29 ], in addition to nodal displacements, the entropy generation rate is also necessary to relate microstructural changes in the material with spatial and temporal coordinates. This concept [ 29 ] has been successfully implemented for a wide range of materials and has been experimentally and mathematically validated and reported in literature [ 18 , 19 , 20 , 25 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 ]. The entropy generation rate of any material under any external disturbances like mechanical, thermal, electrical, chemical, radiation, and corrosion can be calculated from principles of physics, using the fundamental equation, with no need for curve fitting phenomenological models or polynomials fit to experimental test data.…”

Residual fatigue life prediction based on a novel damage accumulation model considering loading history

“…The irreversible thermodynamics theory, combined with the continuum damage mechanics, provided an acceptable life prediction with clear physical meaning. Subsequently, numerous scholars [48][49][50][51][52][53][54][55] developed the entropy-based models for fatigue life prediction. Wang and Yao 49 proposed a novel entropy increment model based on the framework of continuum damage mechanics, which was validated by eight groups of experiments.…”