Variant selection in laser powder bed fusion of non-spherical Ti-6Al-4V powder

The presence ofα/αon priorβ/βgrain boundaries directly impacts the final mechanical properties of the titanium alloys.Theβ/βgrain boundary variant selection of titanium alloys has been assumed to be unlikely owing to the high cooling rates in laser powder bed fusion(L-PBF).However,we hypothesize that powder characteristics such as morphology(non-spherical)and particle size(50–120μm)could affect the initial variant selection in L-PBF processed Ti-6Al-4V alloy by locally altering the cooling rates.Despite the high cooling rate found in L-PBF,results showed the presence ofβ/βgrain boundaryαlath growth inside two adjacent priorβgrains.Electron backscatter diffraction micrographs confirmed the presence ofβ/βgrain boundary variant selection,and synchrotron X-ray high-speed imaging observation revealed the role of the“shadowing effect”on the locally decreased cooling rate because of keyhole depth reduction and the consequentβ/βgrain boundaryαlath growth.The self-accommodation mechanism was the main variant selection driving force,and the most abundantα/αboundary variant was type 4(63.26°//[10553¯]).The dominance of Category IIαlath clusters associated with the type 4α/αboundary variant was validated using the phenomenological theory of martensite transformations and analytical calculations,from which the stress needed for theβ→αtransformation was calculated.

Data analytics using canonical correlation analysis and Monte Carlo simulation

A canonical correlation analysis is a generic parametric model used in the statistical analysis of data involving interrelated or interdependent input and output variables.It is especially useful in data analytics as a dimensional reduction strategy that simplifies a complex,multidimensional parameter space by identifying a relatively few combinations of variables that are maximally correlated.One shortcoming of the canonical correlation analysis,however,is that it provides only a linear combination of variables that maximizes these correlations.With this in mind,we describe here a versatile,Monte-Carlo based methodology that is useful in identifying non-linear functions of the variables that lead to strong input/output correlations.We demonstrate that our approach leads to a substantial enhancement of correlations,as illustrated by two experimental applications of substantial interest to the materials science community,namely:(1)determining the interdependence of processing and microstructural variables associated with doped polycrystalline aluminas,and(2)relating microstructural decriptors to the electrical and optoelectronic properties of thin-film solar cells based on CuInSe_(2) absorbers.Finally,we describe how this approach facilitates experimental planning and process control.