Whether you are designing a traction motor for an electric vehicle, wind power 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 the magnetic properties of various grades offered by diverse manufacturers. Such databases can help you to rank them. By selecting the best possible magnetic material, you can gain a competitive edge. MagWeb offers two databases that can give you such a competitive edge:

Steels Database SMAG : Magnetic Properties of all Soft Magnetic Materials (e.g Electrical Steels, Carbon Steels, Stainless Steels etc.) 

Magnets Database PMAG – Magnetic Properties of all Hard Magnetic Materials  (Permanent Magnet Materials, e.g. Neodymium, Samarium Cobalt, Ferrite etc.)

STEELS DATABASE SMAG (Version 6, Released on 1 Aug. 2020)

  • Lists magnetic properties of Grades produced by all manufacturers worldwide
  • More than 3000 Digital B(H) and Core Loss Curves   
  • 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


  • Description of 11 categories of soft magnetic materials
  • Discover superior grade of Electrical Steel that best suits your needs 
  • Magnetic Properties of bulk steels such as
    • 143 Carbon Steels    
    •  49 Stainless Steels  


  • Lists all Soft Magnetic Materials produced by various manufacturers worldwide
  • Lists frequencies at which data is available
  • Lists Saturation Flux Density of each Grade
  • Searchable by manufacturer, Grade

DIGEST and INDEX files:

  • Lists Source of Data, Composition, Resistivity, Density, Thermal Conductivity  etc


Version 6 resolves two outstanding issues – roughness in data and Approach to Saturation.

B(H) data smoother –   The measured B(H) data can be rough due to noise introduced during 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 .  Smooth B(H) data is thus essential for fast convergent solutions.   Roughness occurs only at some data points, but typical B(H) will not show it. It can be revealed only by plotting B'(H) slope permeability curve.

MagWeb recently perfected a proprietary spot-cleaner tool that locates and smooths these rough data points. 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 rao@magweb.us   

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’ [1]. 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.


[1] 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/

[2] 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.

[3] G.F.T Widger, “Representation of magnetization curves over extensive range by rational-fraction approximations, Proc. Electrical Engineers, Jan. 1969,  pp. 156-160.

[4] Kameari, J., FEM Computation of magnetic fields in Anisotropic magnetic materials, IEEJ Trans. Vol. 126, No.2, 2006 [1] Fujiwara, K., A proposal of finite element analysis considering two-dimensional properties, IEEE Trans Magnetics Vol. 38, No. 2, Mar 2002.


[1] 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.

[2] 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.

[3] 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

[4] Jastrzebski, R., Chwastek, K., Analytical expressions for magnetization curves, 2017 Progress in Applied Electrical Engineering, 2017 https://ieeexplore.ieee.org/document/8009019.

[5] 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.