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

Detection of sub-surface damage in wind turbine bearings using acoustic emissions and probabilistic modelling

Bearings are the culprit of a large quantity of Wind Turbine (WT) gearbox failures and account for a high percentage of the total of global WT downtime. Damage within rolling element bearings have been shown to initiate beneath the surface which defies detection by conventional vibration monitoring as the geometry of the rolling surface is unaltered. However, once bearing damage reaches the surface, it generates spalling and quickly drives the deterioration of the entire gearbox through the introduction of debris into the oil system. There is a pressing need for performing damage detection before damage reaches the bearing surface. This paper presents a methodology for detecting sub-surface damage using Acoustic Emission (AE) measurements. AE measurements are well known for their sensitivity to incipient damage. However, the background noise and operational variations within a bearing necessitate the use of a principled statistical procedure for damage detection. This is addressed here through the use of probabilistic modelling, more specifically Gaussian mixture models. The methodology is validated using a full-scale rig of a WT bearing. The bearings are seeded with sub-surface and early-stage surface defects in order to provide a comparison of the detectability at each level of a fault progression