Whether you are designing a traction motor for an electric vehicle, wind generator, transformer, or pump, your product is only as good as the magnetic materials that you employ. For optimal design, you need databases that list their magnetic properties. They can help you to rank and select the best magnetic materials so that you can gain a competitive edge. MagWeb offers two databases:
Steel Database (SMAG ): Magnetic Properties of all Soft Magnetic Materials
Magnet Database (PMAG )– Magnetic Properties of all Hard Magnetic Materials (Permanent Magnets)
SMAG (Version 6, Released on 1 Aug. 2020)
- 3000 Digital B(H) and Core Loss Data Sets
- New B(H) Data Smoother (for fast convergent solutions)
- New B(H) Data Extrapolator (for robust over-fluxed designs)
- Accurate, evenly spaced B(H)/Core Loss Data (< 2% away from measured data)
- Saturation Flux Density Js for more than 1000 materials
- Each Grade’s Data in a Single Excel File
SMAG Handbook :
- Description of 11 categories of soft magnetic materials
- Discover superior grade of Electrical Steel that best suits your needs
- Hard to find Magnetic Properties of
- 143 Carbon Steels
- 49 Stainless Steels
- Searchable by manufacturer, Grade, Js, f, T
DIGEST and INDEX files:
- Lists Source of Data, Composition, Resistivity, Density, Thermal Conductivity
WHAT IS NEW IN VERSION 6
Version 6 resolves two outstanding issues – roughness in data and Approach to Saturation.
B(H) data smoother – It is well known that smoothness of B(H) data is essential for obtaining convergent solutions. The measured B(H) data can be rough at some spots due to errors in measurement, digitization, overheating etc. Roughness in B(H) data slows any magnetic field solver. If B(H) data is too rough, solution may not converge .
Roughness causes spurious hidden ripples that are unphysical. MagWeb recently perfected a proprietary spot-cleaner tool that locates these spurious ripple spots and smooths them locally. Version 6 furnishes spot-cleaned raw measured data to ensure faster convergent solutions. Magweb can return such spot-cleaned, solution-convergent data free, if you send your raw B(H) data to firstname.lastname@example.org
B(H) Data Extrapolator – At present, B(H) data is measured only up to 1.8T. But in severe duty, machines may be over-fluxed, i.e., operate far beyond measured data – close to saturation flux density Js. Then the flux will leak from designated paths unknowingly into neighboring structures, causing them to overheat.
But existing field simulators do not input Js so they extrapolate ‘blind’ . Over-fluxed machines therefore face the century-old problem of Approach to Saturation. Several fertile minds have proposed different approaches over past century. But none of them can estimate Js accurately when the data is too far from it. Magweb recently developed a model, called Generalized Frohlich Model (GFM), that extracts Js from spot-cleaned measured data. Version 6 uses GFM to accurately extrapolate all data to saturation. Using such data will result in machines that will perform better in severe duty.
SOME REFERENCES ON B(H) DATA SMOOTHNG:
 Liu, L., How the B-H curve affects a magnetic analysis (and how to improve it) , Nov. 2019 , comsol.com, https://www.comsol.com/blogs/how-the-b-h-curve-affects-a-magnetic-analysis-and-how-to-improve-it/
 Rao,,D. K., et al. Effective use of magnetization data in the design of machines with overfluxed regions, IEEE Trans. Magnetics Vol. 51, No. 7, July 2015. pp 6100709.
 G.F.T Widger, “Representation of magnetization curves over extensive range by rational-fraction approximations, Proc. Electrical Engineers, Jan. 1969, pp. 156-160.
 Kameari, J., FEM Computation of magnetic fields in Anisotropic magnetic materials, IEEJ Trans. Vol. 126, No.2, 2006  Fujiwara, K., A proposal of finite element analysis considering two-dimensional properties, IEEE Trans Magnetics Vol. 38, No. 2, Mar 2002.
SOME REFERENCES ON EXTRAPOLATION TO SATURATION:
 Umenei, A.E., Melikhov,Y., Jiles, D.C., Models for extrapolation of magnetization data on magnetic cores to high fields, IEEE Trans. Magnetics, Vol. 47, No. 12, Dec. 2011, pp. 4707-4711.
 Rao,,D. K., et al. Effective use of magnetization data in the design of machines with overfluxed regions, IEEE Trans. Magnetics, Vol. 51, No. 7, July 2015. pp 6100709.
 Chai, S.H., et al. Extrapolating B-H Curve data using common electrical steel characteristics for high magnetic saturation applications, J. Magnetics, Vol. 20, No. 3, p. 258-264. Sept. 2015. https://hanyang.elsevierpure.com/en/publications/extrapolating-bh-curve-data-using-common-electrical-steel-charact
 Jastrzebski, R., Chwastek, K., Analytical expressions for magnetization curves, 2017 Progress in Applied Electrical Engineering, 2017 https://ieeexplore.ieee.org/document/8009019.
 Subbiah, A., Laldin, O., Cubic extrapolation of steel magnetization curves for highly saturated electric machines, IEEE Trans. Energy Conversion, Vol. 32, No. 4, Dec. 2017, pp. 1624-1625.