Cold Spray is an emerging solid-state material deposition process in which coatings are applied by exposing a substrate to a high velocity jet of particles accelerated by a supersonic jet of compressed gas.  The process is considered "cold" because of the relatively low temperatures of the expanded gas and particle stream impacting the substrate.  Using this process, researchers have successfully deposited stainless steel, nickel, copper, aluminum, and many other alloys.
 
By using gas jet and particle temperatures well below material melting temperatures, cold spray offers many advantages over conventional gas-thermal spray methods.  These include compressive rather than tensile residual stresses, wrought like microstructure, near theoretical density, and freedom from oxides and other inclusions.  Moreover, with a narrow particle stream footprint, fast growth rates of coating thickness with better shape control, often eliminating the need for masking, are possible.
 
To aid in the transition of cold spray from the laboratory into production, a method to determine optimized spray parameters is required.  Process variables such as carrier gas pressure and temperature, nozzle travel speed, nozzle design, and powder feed rate all influence the coating quality and its properties.  Depending on the application, users may be interested in optimizing coating density, bond strength, deposition efficiency, etc., or a combination of these and/or other properties.
 

This talk describes one such methodology.  A design of experiments test plan was established, based on selected independent processing variables.  Coating trials were performed and the dependent variables of interest measured for each test condition.  Influence coefficients were then determined, including higher order cross-coupling terms, to assess the effect of the processing parameters on the properties of interest.  Based on these influence coefficients, process parameters yielding the optimized predicted properties of the coating were selected.  Finally, coatings were produced using this optimized parameter set to validate the model predictions.