Combining synchrotron X-ray diffraction, mechanistic modeling and machine learning for in situ subsurface temperature quantification during laser melting.

Laser melting, such as that encountered during additive manufacturing, produces extreme gradients of temperature in both space and time, which in turn influence microstructural development in the material. Qualification and model validation of the process itself and the resulting material necessitate the ability to characterize these temperature fields. However, well established means to directly probe the material temperature below the surface of an alloy while it is being processed are limited. To address this gap in characterization capabilities, a novel means is presented to extract subsurface temperature-distribution metrics, with uncertainty, from in situ synchrotron X-ray diffraction measurements to provide quantitative temperature evolution data during laser melting. Temperature-distribution metrics are determined using Gaussian process regression supervised machine-learning surrogate models trained with a combination of mechanistic modeling (heat transfer and fluid flow) and X-ray diffraction simulation. The trained surrogate model uncertainties are found to range from 5 to 15% depending on the metric and current temperature. The surrogate models are then applied to experimental data to extract temperature metrics from an Inconel 625 nickel superalloy wall specimen during laser melting. The maximum temperatures of the solid phase in the diffraction volume through melting and cooling are found to reach the solidus temperature as expected, with the mean and minimum temperatures found to be several hundred degrees less. The extracted temperature metrics near melting are determined to be more accurate because of the lower relative levels of mechanical elastic strains. However, uncertainties for temperature metrics during cooling are increased due to the effects of thermomechanical stress.

The effects of particle size distribution on the rheological properties of the powder and the mechanical properties of additively manufactured 17-4 PH stainless steel.

It is well known that changes in the starting powder can have a significant impact on the laser powder bed fusion process and subsequent part performance. Relationships between the powder particle size distribution and powder performance such as flowability and spreadability are generally known; however, links to part performance are not fully established. This study attempts to more precisely isolate the effect of particle size by using three customized batches of 17-4 PH stainless steel powders with small shifts in particle size distributions having non-intersecting cumulative size distributions, designated as Fine, Medium, and Coarse. It is found that the Fine powder has the worst overall powder performance with poor flow and raking during spreading while the Coarse powder has the best overall flow. Despite these differences in powder performance, the microstructures (i.e., porosity, grain size, phase, and crystallographic texture) of the built parts using the same process parameters are largely the same. Furthermore, the Medium powder produced parts with the highest mechanical properties (i.e., hardness and tensile strength) while the Fine and Coarse powders produced parts with effectively identical mechanical properties. Parts with good static mechanical properties can be produced from powders with a wide range of powder performance.

Demonstration of a laser powder bed fusion combinatorial sample for high-throughput microstructure and indentation characterization.

High-throughput experiments that use combinatorial samples with rapid measurements can be used to provide process-structure-property information at reduced time, cost, and effort. Developing these tools and methods is essential in additive manufacturing where new process-structure-property information is required on a frequent basis as advances are made in feedstock materials, additive machines, and post-processing. Here we demonstrate the design and use of combinatorial samples produced on a commercial laser powder bed fusion system to study 60 distinct process conditions of nickel superalloy 625: five laser powers and four laser scan speeds in three different conditions. Combinatorial samples were characterized using optical and electron microscopy, x-ray diffraction, and indentation to estimate the porosity, grain size, crystallographic texture, secondary phase precipitation, and hardness. Indentation and porosity results were compared against a regular sample. The smaller-sized regions (3 mm × 4 mm) in the combinatorial sample have a lower hardness compared to a larger regular sample (20 mm × 20 mm) with similar porosity (< 0.03 %). Despite this difference, meaningful trends were identified with the combinatorial sample for grain size, crystallographic texture, and porosity versus laser power and scan speed as well as trends with hardness versus stress-relief condition.

Synchrotron X-Ray Microbeam Diffraction Measurements of Full Elastic Long Range Internal Strain and Stress Tensors in Commercial-Purity Aluminum Processed by Multiple Passes of Equal-Channel Angular Pressing.

Synchrotron X-ray microbeam diffraction was used to measure the full elastic long range internal strain and stress tensors of low dislocation density regions within the submicrometer grain/subgrain structure of equal-channel angular pressed (ECAP) AA1050 after 1, 2, and 8 passes. This is the first time that full tensors were measured in plastically deformed metals at this length scale. This work supplements previous studies that measured long range internal stresses (LRIS) in ECAP AA1050 of multiple passes, but only for a single direction. The maximum (most tensile or least compressive) principal elastic strain directions for the unloaded 1 pass sample for the grain/subgrain interiors align well with the pressing direction, and are more random for the 2 and 8 pass samples. The measurements reported here indicate that the local stresses and strains become increasingly isotropic (homogenized) with increasing ECAP passes using route BC. The average maximum (in magnitude) LRISs are -0.43 σa for 1 pass, -0.44 σa for 2 pass, and 0.14 σa for the 8 pass sample. These LRISs appear to be larger than LRISs reported by previous works (using single reflection measurements).