Metallurgical and Materials Transactions A

, Volume 47, Issue 1, pp 400–411 | Cite as

Zn Diffusion and α-Fe(Zn) Layer Growth During Annealing of Zn-Coated B Steel

  • Vit JanikEmail author
  • Yongjun Lan
  • Peter Beentjes
  • David Norman
  • Guido Hensen
  • Seetharaman Sridhar


Direct hot press forming of Zn-coated 22MnB5 steels is impeded by micro-cracks that occur in the substrate due to the presence of Zn during the forming process. A study was therefore undertaken to quantify concentration of Zn across the α-Fe(Zn) coating and on grain boundaries in the α-Fe(Zn) layer and the underlying γ-Fe(Zn) substrate after isothermal annealing of Zn-coated 22MnB5 at 1173 K (900 °C) and to link the Zn distribution to the amount and type of micro-cracks observed in deformed samples. Finite difference model was developed to describe Zn diffusion and the growth of the α-Fe(Zn) layer. The penetration of Zn into the γ-Fe(Zn) substrate after 600 seconds annealing at 1173 K (900 °C) through bulk diffusion is estimated to be 3 μm, and the diffusion depth of Zn on the γ-Fe(Zn) grain boundaries is estimated to be 6 μm, which is significantly shorter than the maximum length (15 to 50 μm) of the micro-cracks formed in the severely stressed conditions, indicating that the Zn diffusion into the γ-Fe(Zn) from the α-Fe(Zn) during annealing is not correlated to the depth of micro-cracks. On the other hand, the maximum amount of Zn present in α-Fe(Zn) layer decreases with annealing time as the layer grows and Zn oxidizes, and the amount of Zn-enriched areas inside the α-Fe(Zn) layer is reduced leading to reduced length of cracking. Solid-Metal-Induced Embrittlement mechanism is proposed to explain the benefit of extended annealing on reduced depth of micro-crack penetration into the γ-Fe(Zn) substrate.


Coating Layer Boundary Diffusion Effective Diffusion Coefficient Increase Annealing Time Finite Difference Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The financial assistance from the WMG Centre High Value Manufacturing Catapult with focus on low C mobility and Tata Steel is gratefully acknowledged. The authors are thankful for the assistance from Dr. Richard Beanland and Steven Hindmarsh at MAS, University of Warwick with the TEM analysis with FIB lift-out. The authors also wish to thank Dr Didier Farrugia for discussing aspects of the diffusion modeling.


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Copyright information

© The Minerals, Metals & Materials Society and ASM International 2015

Authors and Affiliations

  • Vit Janik
    • 1
    Email author
  • Yongjun Lan
    • 2
  • Peter Beentjes
    • 3
  • David Norman
    • 4
  • Guido Hensen
    • 3
  • Seetharaman Sridhar
    • 1
  1. 1.WMG, University of WarwickCoventryUK
  2. 2.Swinden Technology CentreTata Steel, R&DRotherhamUK
  3. 3.IJmuiden Technology CentreTata Steel, R&DIjmuidenThe Netherlands
  4. 4.Automotive Engineering GroupTata Steel, R&DCoventryUK

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