P. Samimi

A Constitutive Equation Relating Composition and Microstructure to Properties in Ti-6Al-4V: As Derived Using a Novel Integrated Computational Approach

While it is useful to predict properties in metallic materials based upon the composition and microstructure, the complexity of real, multi-component, and multi-phase engineering alloys presents difficulties when attempting to determine constituent-based phenomenological equations. This paper applies an approach based upon the integration of three separate modeling approaches, specifically artificial neural networks, genetic algorithms, and Monte Carlo simulations to determine a mechanism-based equation for the yield strength of α+β processed Ti-6Al-4V (all compositions in weight percent) which consists of a complex multi-phase microstructure with varying spatial and morphological distributions of the key microstructural features. Notably, this is an industrially important alloy yet an alloy for which such an equation does not exist in the published literature. The equation ultimately derived in this work not only can accurately describe the properties of the current dataset but also is consistent with the limited and dissociated information available in the literature regarding certain parameters such as intrinsic yield strength of pure hexagonal close-packed alpha titanium. In addition, this equation suggests new interesting opportunities for controlling yield strength by controlling the relative intrinsic strengths of the two phases through solid solution strengthening.

Modeling of Ti-W Solidification Microstructures Under Additive Manufacturing Conditions

Additive manufacturing (AM) processes have many benefits for the fabrication of alloy parts, including the potential for greater microstructural control and targeted properties than traditional metallurgy processes. To accelerate utilization of this process to produce such parts, an effective computational modeling approach to identify the relationships between material and process parameters, microstructure, and part properties is essential. Development of such a model requires accounting for the many factors in play during this process, including laser absorption, material addition and melting, fluid flow, various modes of heat transport, and solidification. In this paper, we start with a more modest goal, to create a multiscale model for a specific AM process, Laser Engineered Net Shaping (LENS™), which couples a continuum-level description of a simplified beam melting problem (coupling heat absorption, heat transport, and fluid flow) with a Lattice Boltzmann-cellular automata (LB-CA) microscale model of combined fluid flow, solute transport, and solidification. We apply this model to a binary Ti-5.5 wt pct W alloy and compare calculated quantities, such as dendrite arm spacing, with experimental results reported in a companion paper.

Crystallisation behaviour during tensile loading of laser treated Fe–Si–B metallic glass

Effect of tensile loading on crystallisation behaviour of as-cast and laser thermal treated Fe–Si–B metallic glass foils was investigated. Tensile loading lacked any marked influence on the crystallisation behaviour of as-cast and structurally relaxed laser-treated metallic glass foils. Furthermore, the average crystallite/grain size in partially crystallised laser-treated metallic glass foil was nearly equal to the average crystallite/grain size in the region away from the fracture of the same partially crystallised laser-treated metallic glass foil after tensile loading. However, a significant crystallite/grain growth/coarsening of the order of two and half times was observed in the fractured region compared to the region around it for the laser-treated partially crystallised metallic glass foils. The simultaneous effects of stress generation and temperature rise during tensile loading were considered to play a key role in crystallite/grain growth/coarsening.