Browsing by Subject "NIST"

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Continuous Laser Scan Strategy for Faster Build Speeds in Laser Powder Bed Fusion System
(University of Texas at Austin, 2017) Yeung, H.; Lane, B.; Fox, J.; Kim, F.; Heigel, J.; Neira, J.
Research has shown significant influence of laser scan strategy on various part qualities in the laser powder bed fusion additive manufacturing process. The National Institute of Standards and Technology developed the Additive Manufacturing Metrology Testbed, which provides open architecture for flexible control and monitoring during a laser powder bed fusion additive manufacturing process. This allows extended control of scan strategies, including control of laser power and speed within each scan line. A ‘continuous’ scan strategy can reduce build times and improve throughput by negating the need to turn the laser off between scan tracks (e.g., sky-writing). Also, less frequent laser power interruption can potentially improve the melt-pool continuity. Multiple experiments are performed utilizing the continuous and traditional scan strategies, and comparisons are made between build time and measured melt-pool qualities.
Design, Developments, and Results from the NIST Additive Manufacturing Metrology Testbed (AMMT)
(University of Texas at Austin, 2016) Lane, B.; Mekhontsev, S.; Grantham, S.; Vlasea, M.L.; Whiting, J.; Yeung, H.; Fox, J.; Zarobila, C.; Neira, J.; McGlauflin, M.; Hanssen, L.; Moylan, S.; Donmez, A.; Rice, J.
The National Institute of Standards and Technology (NIST) is developing a facility titled the Additive Manufacturing Metrology Testbed that will enable advanced research into monitoring, controls, process development, and temperature measurement for laser powder bed fusion additive manufacturing and similar processes. This system provides an open control architecture as well as a plethora of sensor systems and calibration sources that are primarily radiance-based and aligned co-axially with the laser beam and focused on the laser interaction zone. This paper briefly reviews the system requirements, and details the current progress of the facility design and construction. Mechanical, optical, and control systems designs are detailed with select highlights that may be relevant to additive manufacturing researchers and system developers. Recent experimental results from the prototype laser control and in-situ monitoring system are also highlighted.
Development of a Testbench for Additive Manufacturing Data Integration, Management, and Analytics
(University of Texas at Austin, 2023) Yang, Chen-Wei; Kuan, Alexander; Li, Sheng-Yen; Lu, Yan; Kim, Jaehyuk; Cheng, Fan-Tien; Yang, Haw-Ching
The NIST Additive Manufacturing (AM) Data Integration Testbench is a platform designed to evaluate data models, communication methods, and data analytics for AM industrialization. This paper describes a reference framework for AM data integration, named AMIF, and the design of the testbench based on AM Integration Framework (AMIF) for testing the integration of in-process data acquisition, real-time feature extraction, process control, and predictive models under a data management system. A specification of this testbench is developed to manage and stream voluminous data captured by high-speed cameras and performing data analytics using common information models and functional interfaces. The integration of the data, models, and computer tools sends operational decisions to an AM machine in real time. On top of the real-time control functions, AM data integration with MES and ERP systems is also included using a high-performance data warehouse for long-term data archiving and metadata management. The architecture of this testbench is illustrated in this work. AMIF can guide AM practitioners and system integrators to build their integrated AM manufacturing systems for production. The NIST AM testbench’s plug-and-play features allow both internal and external researchers and developers to assess the effectiveness of their individual data models, data analytics, and decision-making algorithms on the systems engineering level.
NIST and the CEIDP - Working Together to Advance Technology
(2001) Hebner, R.E.
The technology underpinning the electrification and the growth of telecommunications in the United States stimulated a long-term relationship between the Conference on Electrical Insulation and Dielectric Phenomena and the U. S. National Institute of Standards and Technology. This interaction, based on common technology, helped the National Institute of Standards and Technology with quality control, dissemination activities, and program planning. It helped the Conference in the development of technical thrusts and in staffing its activities.
Performance Characterization of Process Monitoring Sensors on the NIST Additive Manufacturing Metrology Testbed
(University of Texas at Austin, 2017) Lane, B.; Grantham, S.; Yeung, H.; Zarobila, C.; Fox, J.
Researchers and equipment manufacturers are developing in-situ process monitoring techniques with the goal of qualifying additive manufacturing (AM) parts during a build, thereby accelerating the certification process. Co-axial melt pool monitoring (MPM) is one of the primary in-situ process monitoring methods implemented on laser powder bed fusion (LPBF) machines. A co-axial MPM system is incorporated on the Additive Manufacturing Metrology Testbed (AMMT) at the National Institute of Standards and Technology (NIST); a custom LPBF and thermophysical property research platform where one of many research goals is to advance measurement science of AM process monitoring. This paper presents the methods used to calibrate and characterize the spatial resolution of the melt pool monitoring instrumentation on the AMMT. Results from the measurements are compared to real melt pool images, and analysis is provided comparing the effect on spatial resolution limits on image analysis.
Quantifying Accuracy of Metal Additive Processes Through a Standardized Test Artifact
(University of Texas at Austin, 2017) Weaver, Jason; Barton, TJ; Jenkins, Derrik; Linn, John; Miles, Mike; Smith, Robert
Two limitations of AM processes when compared to CNC subtractive processes are reduced dimensional accuracy and rougher surface finish. Accuracy and surface finish of metal additive processes, such as DMLS or SLM, are generally much looser than precision turning or grinding processes. Because of this, it is important to have an understanding of an AM machine’s capabilities—the designer must be satisfied with the tolerances and finishes possible, or additional post-processing must be added. One way to examine the capabilities of an AM process is by printing and measuring test artifacts. This paper examines a test artifact proposed by NIST that is intended to demonstrate many different capabilities and types of accuracy. Three identical builds are printed on a Concept Laser metal additive machine and measured. The capabilities of the machine are quantified and discussed, along with additional recommendations for improving the test structure design and the measurement process.
Tasking on natural statistics of infrared images
(2014-12) Goodall, Todd Richard; Bovik, Alan C. (Alan Conrad), 1958-
Natural Scene Statistics (NSS) provide powerful perceptually relevant tools that have been successfully used for image quality analysis of visible light images. NSS capture statistical regularities that arise in the physical world and thus are relevant to Long Wave Infrared (LWIR) images. LWIR images are similar to visible light images and mainly differ by the wavelengths captured by the sensors. The distortions unique to LWIR are of particular interest to current researchers. We analyze a few common LWIR distortions and how they relate to NSS models. Humans are the most important factor for assessing distortion and quality in IR images, which are often used in perceptual tasks. Therefore, predicting human performance when a task involving LWIR images needs to be performed can be critical to improving task efficacy. The National Institute for Standards and Technology (NIST) characterizes human Targeting Task Performance (TTP) by asking firefighters to identify the locations of fire hazards in LWIR images under distorted conditions. We find that task performance can be predicted using NSS features. We also report the results of a human study. We analyzed the NSS of LWIR images under pristine and distorted conditions using four databases of LWIR images. Each database was captured with a different camera allowing us to better evaluate the statistics of LWIR images independent of camera model. We find that models of NSS are also effective for measuring distortions in the presence of other independent distortions.