Abstract
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.
Highlights
Additive manufacturing of metals offers unique advantages for materials and design such as topology optimization and functionally graded materials [1, 2]
Additional micrographs from a second sample are provided in the supplementary material, which shows similar trends, along with the segmented images and a table of the results from both samples
A high-throughput method for characterizing additively manufactured alloys was demonstrated through the manufacturer of combinatorial samples characterized by rapid measurement tools
Summary
Additive manufacturing of metals offers unique advantages for materials and design such as topology optimization and functionally graded materials [1, 2]. High-throughput experiments (HTE) can help to address both needs through automation, combinatorial processing, and rapid measurements [9-14]. Combinatorial processing is the synthesis of a sample library, a single sample that contains sub-samples that vary one or more synthesis parameters (e.g., chemistry and thermo-mechanical processing for alloys), which is conducive to automated and rapid measurements. These methods are often applied in biology and chemistry where thousands of experiments are routine [14]. The argument for high-throughput mechanical tests is that for immature processes or new materials, the knowledge gained about the process or material outweighs the deficiencies, and the pursuit will lead to less overall time and effort to achieve the desired performance
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