Towards understanding grain nucleation under Additive Manufacturing solidification conditions

By A. Prasad, L. Yuan, Peter D. Lee, Mitesh N. Patel, D. Qiu, M. Easton, D. StJohn. Acta Materialia, 2020

239 citations recorded by the source corpus.

Abstract This paper provides insights into the effect of high thermal gradients and cooling rates on equiaxed grain nucleation and growth in conditions similar to those experienced during Additive Manufacturing (AM) processes. Bridgman type solidification is numerically simulated with columnar grains growing at a fixed pull rate under a user-imposed thermal gradient. Controlled inoculants of known nucleation undercooling were placed ahead of the growing columnar grains to allow quantitative analysis of nucleation events. At low thermal gradient and cooling rate only the inoculants with low nucleation undercooling were activated due to low melt undercooling driven by constitutional supercooling (CS). As the cooling rate is increased, for a given thermal gradient, a larger number of inoculants with higher nucleation undercoolings were activated. At higher cooling rates, thermal undercooling was generated by a lag in the growth rate of the solid-liquid (S-L) interface compared to the theoretical pull rate. Thus, thermal undercooling becomes dominant leading to the facilitation of nucleation on less potent substrates requiring higher undercooling. The results show a transition from solute-driven undercooling to cooling rate driven thermal undercooling which contributes to the undercooling that activates the nucleation events. Invoking the Interdependence model, it is also shown that the high cooling rate induced thermal undercooling reduces the size of the nucleation free zone substantially.

Subfields: Automotive Engineering, Materials Chemistry, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/b1df0e197939666b0c06c6d691b863d0eff3db53

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

  1. M. Qian
  2. Han Huang
  3. M. Easton
  4. Milan Brandt

In Materials Chemistry

  1. D. Qiu
  2. M. Qian
  3. M. Easton
  4. D. StJohn
  5. C. J. Todaro
  6. Milan Brandt
  7. Mingxing Zhang
  8. G. Song

In Mechanical Engineering

  1. D. Qiu
  2. M. Qian
  3. M. Easton
  4. D. StJohn
  5. Mingxing Zhang
  6. C. J. Todaro
  7. Milan Brandt
  8. Han Huang

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