Thermal Modeling and Control in Production of Intermetallic Coatings From Layered Precursors

[+] Author and Article Information
Marios Alaeddine

Department of Mechanical Engineering, Tufts University, Medford, MA 02155alae77@yahoo.com

Rajesh Ranganathan, Teiichi Ando

Mechanical, Industrial and Manufacturing Engineering Department, Northeastern University, Boston, MA 02115

Charalabos C. Doumanidis

Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, 1678, Cyprus

J. Dyn. Sys., Meas., Control 129(1), 56-65 (May 17, 2006) (10 pages) doi:10.1115/1.2397152 History: Received April 29, 2005; Revised May 17, 2006

Successful fabrication of intermetallic coatings on surfaces of manufacturing interest involves regulation of the temperature/concentration dynamic distributions that develop in the molten layer during the thermal and reaction process. Modeling the spatio-temporal dynamics of this metallurgical process, however, requires partial differential equations that are cumbersome to solve on-line, as part of a real time reference model to the controller. To this end, we present a computationally parallel and meshless model (i.e., decoupled with the capability to be solved numerically in real time) to decipher the dynamics of the thermal coating process and to permit real time monitoring and control of the resulting coating microstructure. The analytical model is based on kinetic growth theories, lumped energy and mass balances, and convolution expressions of distributed temperature and concentration Green’s fields (accounting for the orientation of their gradient and decomposing heat and mass transfer across the coating from substrate conduction). The model is validated with nickel aluminide coatings processed on a robotic plasma arc laboratory station, through in-process infrared thermal sensing and off-line metallographic analysis. A Monte Carlo sample control scheme, that involves on-line parameter identification and model adaptation, is also developed using the model as an in-process observer for successful production of binary metal system coatings that exhibit the desired microstructure geometry and characteristics.

Copyright © 2007 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Figure 1

Front view of the melting/solidification interface for an element located at the center (z=0) of the heat source (heat source moves in the positive y direction)

Grahic Jump Location
Figure 2

Schematic representation of the heat input distributions on the coating and substrate

Grahic Jump Location
Figure 3

Plasma arc welding station

Grahic Jump Location
Figure 4

Element location (y=3mm, z=0): (a) simulated and experimental (actual) temperature variation in the element; (b) simulated motion of the melting front; (c) simulated growth of the Ni2Al3 coating layer; and (d) experimental SEM micrograph

Grahic Jump Location
Figure 5

Schematic of a Monte Carlo sample control method

Grahic Jump Location
Figure 6

Model-reference control scheme

Grahic Jump Location
Figure 7

Actual and model-predicted peak temperature distributions using the estimated values of σ and n

Grahic Jump Location
Figure 8

Control objective: Ni2Al3 coating layer of an average thickness of 3μm along the diameter (z-axis) of the PA heat source

Grahic Jump Location
Figure 9

Control objective: Ni2Al3 coating layer of a maximum thickness of 5μm at the center (z=0) of the PA heat source

Grahic Jump Location
Figure 10

Control objective: a maximum peak temperature in the coating layers (i.e., at the center of the PA heat source (z=0)) of 1300K




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In