Development of a Precipitation Free High-Nitrogen Alloy

Abstract

High nitrogen steels provide excellent mechanical properties and corrosion resistance but are prone to form precipitates which adversely affect the corrosion resistance and toughness. High nitrogen steels available in the market are not claimed to be precipitate free. It is critical to avoid these precipitates and retain nitrogen in the dissolved form to realize the value of these alloys.

This study presents development of a high nitrogen ferrous powder precursor. The translation of this powder precursor to a functional coating is also discussed. To achieve dissolved nitrogen in the composition, two approaches are discussed. Retaining nitrogen by rapid solidification to bypass the L→δ reaction promoting the L→γ reaction directly, where the nitrogen solubility is high is one possible route. The initial alloy design was done using such rapid solidification process with pre-formed nitrides to determine the right composition for desired mechanical and corrosion properties. This rapid-solidification approach was studied in detail by a few team members from the AMPL group at the UM-Dearborn. This study elaborates more of the solid-state dissolution route to avoid the molten state of the material. A solid-state solution annealing is proposed to treat a ferritic powder alloy (α-Fe) at high temperature in presence of nitrogen further quenching it to stabilize austenite at room temperature in-order to retain the dissolved nitrogen in the composition. Pre-atomized nitrogen steel powders readily available in the market were also evaluated for their precipitations. A heat treatment of the nitrogen powder in inert argon media was considered to minimize /eliminate the nitrides.

In-order to evaluate the performance of these prepared precursors, coating development by cold-spray is considered. Cold spray being a solid-state deposition technique is carried out at low temperatures which do not lead to any thermal phase transformations in the coatings as of the starting precursors. Low process temperatures maintaining the thermal equilibrium make cold spray an ideal candidate for coating development. The dissolved nitrogen content in the powder precursor can thus be easily maintained during this solid-state deposition. The properties of the material can be thus assessed in terms of a coating as there is possibly no chemical/metallurgical change from powder precursor to coating. Deposition efficiency was considered as a key performance parameter for different precursors. Mechanical properties, tribological properties and corrosion properties of the coatings were evaluated.

In-order to make an optimized use of these recent techniques in thermal spray and additive manufacturing family, it has been driving researchers to make use of artificial intelligence and machine learning techniques. Narrowing down to the current study, cold-spray technique has numerous parameters which affect the deposition quality. In-order to achieve an optimum parameter for deposition, numerous experiments are required which in-turn is time consuming and cost ineffective. A prescriptive data model is proposed in this study which uses a set of few experiments to develop a training data set in-order to predict the coating quality in terms of the deposition efficiency as well as porosity. This supervised prescriptive approach can be used as a tool in-order to achieve an optimized coating parameter to get the desired properties before performing the experiments. This algorithm could also be used in the nozzle design of the system to optimize for maximum particle velocity of the precursors which directly affects the deposition quality.