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Taylor, Samantha

Publications and source records attributed to Taylor, Samantha.

16 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Methods and apparatus for improved thermal monitoring by correlating infrared emissivity to surface topography

Embodiments of the present disclosure provide an improved method and apparatus for thermal monitoring for a metal additive manufacturing system. An emissivity value of a top surface of an object to be manufactured is determined based, at least in part, on an arithmetic product of a predetermined roughness value and a predetermined slope-related value. The predetermined roughness value and slope-related values are predetermined based, at least in part, on measurements of a previously manufactured object. The system sinters a metal to form the top surface of the object to be manufactured. An infrared sensor detects radiation from at least a portion of the top surface of the object to be manufactured. A temperature is generated based, at least in part, on the detected infrared radiation and the emissivity value and the generated temperature is applied to a temperature utilization component of the system.

Taylor, Samantha↗
DOE OSTI2020

Investigating relationship between surface topography and emissivity of metallic additively manufactured parts

Due to the direct relationship between thermal history and mechanical behavior, in situ thermal monitoring is key in gauging quality of parts produced with additive manufacturing (AM). Accurate monitoring of temperatures in an AM process requires knowledge of environment and object parameters including object emissivity. The emissivity is dependent on several variables, including: wavelength, material composition, temperature, and surface topography. Researchers have been concerned with the thermal emissivity dependence on temperature since large ranges are seen in metal powder bed processes, but there is also an extensive range of surfaces produced by AM. This work focused on discovering what roughness characteristics control thermal emissivity through investigation of prototypic 316 stainless steel AM samples produced with a range of build conditions on a laser powder bed fusion machine. Through experimental measurements of emissivity using hemispherical directional reflectance (HDR), guided by simulations using a finite-difference time-domain (FDTD) Maxwell solver, it was found that combinations of existing roughness parameters describing both height and slope of the surface correlate well with emissivity changes. Furthermore, these parameters work well due to their apt description of surface features encouraging internal reflection, which is the phenomenon that increases emissivity when a surface falls under the geometric optical region conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Born Qualified Grand Challenge LDRD Final Report

This SAND report fulfills the final report requirement for the Born Qualified Grand Challenge LDRD. Born Qualified was funded from FY16-FY18 with a total budget of ~$13M over the 3 years of funding. Overall 70+ staff, Post Docs, and students supported this project over its lifetime. The driver for Born Qualified was using Additive Manufacturing (AM) to change the qualification paradigm for low volume, high value, high consequence, complex parts that are common in high-risk industries such as ND, defense, energy, aerospace, and medical. AM offers the opportunity to transform design, manufacturing, and qualification with its unique capabilities. AM is a disruptive technology, allowing the capability to simultaneously create part and material while tightly controlling and monitoring the manufacturing process at the voxel level, with the inherent flexibility and agility in printing layer-by-layer. AM enables the possibility of measuring critical material and part parameters during manufacturing, thus changing the way we collect data, assess performance, and accept or qualify parts. It provides an opportunity to shift from the current iterative design-build-test qualification paradigm using traditional manufacturing processes to design-by-predictivity where requirements are addressed concurrently and rapidly. The new qualification paradigm driven by AM provides the opportunity to predict performance probabilistically, to optimally control the manufacturing process, and to implement accelerated cycles of learning. Exploiting these capabilities to realize a new uncertainty quantification-driven qualification that is rapid, flexible, and practical is the focus of this effort.

36 MATERIALS SCIENCE↗