Powder Metallurgy Processing: LENS™ (Laser Engineered Net-Shaping) & Kinetic Metallization

CAMM has long been a leader in exploiting advanced powder metallurgy processing techniques in novel ways. Currently, CAMM has two powder processing routes, including both the LENS™ (Laser Engineered Net Shaping from Optomec in Albuquerque, NM), a technique based on directed laser deposition that relies on the localized melting of metal powder and substrate, and a solid-state “cold-spray” Kinetic Metallization (KM™ from Inovati in Santa Barbara, CA) technique that uses subsonic helium and significant amounts of localized deformation to effect the additive manufacturing route. The LENS™ unit (the first commercial sale was to CAMM) has allowed researchers to pioneer the use of elemental blends and composition gradients as a tool for understanding composition-property relationships. Researchers have also performed research involving ultra-fine in-situ products, such as TiB and Er2O3 that occur as a result of the reaction of elemental powder blends. While much of the work is based on a variety of Ti-based alloys, other alloys, including Ni based superalloys, ferrous alloys, and structural intermetallics, have also been deposited using LENS™. The Kinetic Metallization unit is currently being investigated as a method of producing composites in a solid-state process that would otherwise be difficult or impossible to produce using solidification-based processing approaches given solution thermodynamics. This includes initial work into the direct incorporation of hydroxyapatite in a titanium matrix.

The LENS™ system has been employed in the development of a novel combinatorial scheme for alloy development. Examples of this research are given below.

The application of a novel combinatorial approach to alloy optimization for a ternary titanium alloy
This research involves an attempt to develop a novel combinatorial approach to allow for the rapid investigation of compositional variations in multi-component alloys. The techniques used in this novel approach are two-fold. Firstly, the approach incorporates a rapid material processing route based upon a type of directed laser deposition using elemental powder blends to vary alloy composition, so as to populate databases relating composition, microstructure, and properties. Secondly, the approach relies upon a neural network approach to interrogate the databases and develop functional dependencies. The approach has been applied to the a/b Ti alloy, Ti-6Al-4V. Using this technique, alloy variations of Ti-xAl-yV, where 2<x<6 and 2<y<7 (wt.%) have been produced, thermo-mechanically processed, and heat treated. This technique has resulted in a well-populated database containing compositional, microstructural, and property information. This database has been interrogated using neural networks, and a quantitative analysis of the composition-microstructure and composition-property relationships has been determined.

A novel combinatorial approach to the development of beta titanium alloys for orthopaedic implants
In recent years there has been a significant thrust directed towards the development of novel implant alloys based on h-Ti. Two recently developed and promising biocompatible h-Ti alloys are Ti–35Nb–7Zr–5Ta and Ti–29Nb–4.6Zr–13Ta. While both these alloy compositions, based on the quaternary Ti–Nb–Zr–Ta system, are promising, there is still a tremendous scope for improvement in terms of alloy design in this and other systems via optimization of alloy composition and thermo-mechanical treatments. Here a novel combinatorial approach has been used for the development of implant alloys with optimized compositions and microstructures. Using directed laser deposition, compositionally graded alloy samples based on the Ti–Nb–Zr–Ta system have been fabricated. These samples have been heat treated to affect different microstructures in terms of the volume fraction and distribution of the a phase in the h matrix as a function of composition. Subsequently, composition-specific indentation-based hardness and modulus information has been obtained from these samples to construct a database relating the composition and microstructure to the mechanical properties. These databases have been used to train and test fuzzy-logic based neural-network models for predicting the mechanical properties. The trained models have also been used to predict the influence of different alloying additions on the hardness and modulus. These predictions have subsequently been verified by detailed experimental characterization, shedding light on the factors influencing the strength and modulus in these alloys. Such modeling approaches for the development of novel implant alloys can be highly beneficial since they offer the possibility of identifying promising compositions without the necessity for extensive experimental test cycles.