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Melkote, Shreyes N.

Publications and source records attributed to Melkote, Shreyes N..

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Analysis of structure-property gradients in orthogonally machined chips and workpiece subsurface

Production of machined surfaces with optimized mechanical properties requires fundamental process-structure-property knowledge. In this paper, we focus on structure-property gradients in orthogonally cut OFHC Copper chips and the workpiece subsurface. We utilize orientation imaging microscopy and statistical spatial correlation metrics to quantify the microstructure. Constitutive elastic-plastic properties of the workpiece subsurface and chips are measured using spherical nanoindentation. Analysis of the data reveals significant structure and property gradients in both regions. Differences and similarities in structure and property gradients near the workpiece surface and tool-chip interface, and in the chip middle are discussed and explained.

42 ENGINEERING↗
DOE OSTI2019

The effects of material anisotropy on secondary processing of additively manufactured CoCrMo

Components produced by near net shape additive manufacturing processes often require subsequent subtractive finishing operations to satisfy requisite surface finish and geometric tolerances. It is well established that the microstructure and properties of the as-built component are sensitive to the additive processing history. Therefore, downstream secondary processes may be affected by the as-built components’ mechanical behavior. In this work we study the sensitivity of secondary machining operations on CoCrMo samples produced via laser powder bed fusion. Utilizing novel high-throughput mechanical testing, microstructure characterization, and a rigorous statistical analysis we investigate the degree of material anisotropy present in the as-built material. We then study the effects of this anisotropy on secondary processing via slot milling experiments. Our results indicate that mechanical anisotropy is driven by both the morphology of the microstructure as well as crystallographic texture. Furthermore, the machining force response is correspondingly sensitive to these sources of anisotropy, which has the potential to impact how manufacturers finish additively built parts.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Process–structure–property relationships in bimodal machined microstructures using robust structure descriptors

Machining is a severe plastic deformation process that subjects materials to high rates of deformation and elevated temperatures. Dynamic recrystallization during severe plastic deformation drives grain refinement into the sub-micron range but the ductility and thermal stability of these structures is poor. In contrast, bimodal microstructures consisting of both fine and coarse grains have been shown to exhibit attractive strength and ductility properties at these temperatures. In this paper, we study the process–structure–property relationships for pure copper subject to a machining process where the cutting speeds are varied. Further, we investigate the role of thermal effects by varying the post-deformation cooling rates. Microstructures generated under these deformation conditions are quantified using angularly resolved chord length statistics. The derived metrics are found to be robust descriptors of the studied microstructures as they automatically capture complex features such as unimodal and bimodal distributions of grain structure, grain constituent length scales, and morphological anisotropy induced by shear deformation. The uniaxial equivalent yield strength of the generated structures are estimated using spherical nanoindentation and inverse modeling techniques. Lastly, we present a methodology for identifying the constrained property-process inverse mapping for machining using a Bayesian framework and the established forward process–structure–property mapping.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Process-Structure-Property Modeling for Severe Plastic Deformation Processes Using Orientation Imaging Microscopy and Data-Driven Techniques

Machining is a severe plastic deformation process, wherein the workpiece material is subjected to high deformation rates and temperatures. During metal machining, the dynamic recrystallization mechanism causes grain refinement into the sub-micron range. In this study, we investigate the microstructure evolution of oxygen-free high conductivity copper (OFHC Cu) subject to a machining process where the cutting speed and rake angle are controlled to manipulate the process strain, strain rate, and temperatures. Microstructures of the deformed chips are quantified using orientation imaging microscopy and novel statistical descriptors that capture the morphology and local lattice misorientations generated during the several mechanistic stages of the dynamic recrystallization process. Mechanical properties of the resulting chips are quantified using spherical nanoindentation protocols. A multiple output Gaussian process regression model is used to simultaneously model the structure-property evolution, which differs from more common approaches that establish such relationships sequentially. This modeling strategy is particularly attractive since it can flexibly provide both structure and property uncertainty estimates. In addition, the statistical modeling framework allows for the inclusion of multi-fidelity data. The statistical metrics utilized serve as efficient microstructure descriptors, which retain the physics of the observed structures without having to introduce ad hoc microstructure feature definitions.

36 MATERIALS SCIENCE↗