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Data collapse enables comparison of measurement data measured in different laboratories on different samples. In the case of energy losses in Soft Magnetic Materials (SMM) the data collapse is possible to achieved only if the measurement data can be described by the two components formula. For more complicated cases we propose to perform data collapses sequence in the two-dimensional subspaces $L_{i,i+1}$ spanned by the appropriate powers of frequency ${f^{i},f^{i+1}}$. Such approach enables the data comparison in the different two-dimensional subspaces. This idea has been tested with measurement data of the four SMM-s: amorphous alloy textrm{Fe}_{78}textrm{Si}_{13}textrm{B}_{9}$, amorphous alloy $textrm{Co}_{71.5} textrm{Fe}_{2.5} textrm{Mn}_{2} textrm{Mo}_{1} textrm{Si}_{9} textrm{B}_{14}$, crystalline material -- oriented electrotechnical steel sheets 3% Si--Fe, iron--nickel alloy $79% textrm{Ni}-textrm{Fe}$. Intermediate calculations revealed interesting property of the energy losses in the cristalline and amorphous SMM-s which lead to the following hypothesis. Let $P_{tot,1,2}=f_{1,2}(1+f_{1,2})$ be scaled two-components formula for the energy loss in SMM, where $f_{1,2}$ is the corresponding scaled frequency. Then the scaled energy losses values in amorphous SMM are below the second order universal curve $P_{tot,1,2}=f_{1,2}(1+f_{1,2})$, whereas the scaled energy losses values in crystalline SMM are above that universal curve.
A new mathematical model of hysteresis loop has been derived. Model consists in an extansion of tanh($cdot$) by extanding the base of exp function into an arbitrary positive number. The presented model is self-similar and invariant with respect to sc
Assuming that core loss data of Soft Magnetic Materials obey scaling relations, models describing the power losses in materials exposed to non-sinusoidal flux waveforms and DC Bias conditions have been derived. In order to test these models, the meas
Machine learning was utilized to efficiently boost the development of soft magnetic materials. The design process includes building a database composed of published experimental results, applying machine learning methods on the database, identifying
A mechanistic understanding of adhesion in soft materials is critical in the fields of transportation (tires, gaskets, seals), biomaterials, micro-contact printing, and soft robotics. Measurements have long demonstrated that the apparent work of adhe
Conventional optical components are limited to size-scales much larger than the wavelength of light, as changes in the amplitude, phase and polarization of the electromagnetic fields are accrued gradually along an optical path. However, advances in n