Acoustic emission (AE) measurements of avalanches in different systems, such as domain movements in ferroics or the collapse of voids in porous materials, cannot be compared with model predictions without a detailed analysis of the AE process. In particular, most AE experiments scale the avalanche energy E, maximum amplitude Amax and duration D as E ~ Amaxx and Amax ~ Dχ with x = 2 and a poorly defined power law distribution for the duration. In contrast, simple mean field theory (MFT) predicts that x = 3 and χ = 2. The disagreement is due to details of the AE measurements: the initial acoustic strain signal of an avalanche is modified by the propagation of the acoustic wave, which is then measured by the detector. We demonstrate, by simple model simulations, that typical avalanches follow the observed AE results with x = 2 and ‘half-moon’ shapes for the cross-correlation. Furthermore, the size S of an avalanche does not always scale as the square of the maximum AE avalanche amplitude Amax as predicted by MFT but scales linearly S ~ Amax. We propose that the AE rise time reflects the atomistic avalanche time profile better than the duration of the AE signal.

Intermittent avalanches in a multitude of materials are characterized by acoustic emission, AE, where local events lead to strain relaxations and generate shock waves (so-called “jerks”), which are measured at the sample surface. The bane of this approach is that several avalanche mechanisms may contribute to the same AE spectrum so that a detailed analysis of each individual contribution becomes virtually impossible. It is, hence, essential to develop tools to separate signals from different dynamical processes, such as ferroic domain switching, collapse of porous inclusions, dislocation movements, entanglements, and so on. Particularly, difficult cases are dynamical microstructures in fcc alloys where the AE signal strength is weak. Nevertheless, using profile analysis of AE signals, we can distinguish between two mechanisms, namely, dislocation movements and dynamic entanglements in fcc 316L stainless steel. In this approach, we are able to measure the statistical AE durations of both subsets separately. The fingerprint for superposed avalanches with different durations is seen by the scaling between the energy E and the maximum amplitude A of each avalanche E ∼ Ax with x = 2. While the same exponent x applies for both mechanisms, the scaling relation shows two branches with different absolute energy values. The two mechanisms are then confirmed by separating the energy distributions P(E) ∼ E−ε for the two mechanisms with ε = 1.55 for dislocation movements and ε = 1.36 for entanglements.

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Several physical processes can conspire to generate avalanches in materials. Such processes include avalanche mechanisms like dislocation movements, friction processes by pinning magnetic domain walls, moving dislocation tangles, hole collapse in porous materials, collisions of ferroelectric and ferroelastic domain boundaries, kinks in interfaces, and many more. Known methods to distinguish between these species which allow the physical identification of multiavalanche processes are reviewed. A new approach where the scaling relationship between the avalanche energies E and amplitudes A is considered is then described. Avalanches with single mechanisms scale experimentally as E = SiAi2. The energy E reflects the duration D of the avalanche and A(t), the temporal amplitude. The scaling prefactor S depends explicitly on the duration of the avalanche and on details of the avalanche profiles. It is reported that S is not a universal constant but assumes different values depending on the avalanche mechanism. If avalanches coincide, they can still show multivalued scaling between E and A with different S‐values for each branch. Examples for this multibranching effect in low‐Ni 316L stainless steel, 316L stainless steel, polycrystalline Ni, TC21 titanium alloy, and a Fe40Mn40Co10Cr10 high‐entropy alloy are shown.

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