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Displaying 26 - 38 of 38

Towards a Lifecycle Information Framework and Technology in Manufacturing

July 14, 2016
Author(s)
Thomas D. Hedberg, Allison Barnard Feeney, Moneer M. Helu, Jaime A. Camelio
Industry has been chasing the dream of integrating and linking data across the product lifecycle and enterprises for decades. However, industry has been challenged by the fact that the context in which data is used varies based on the function in the

Technical Data Packages (TDPs) from the Smart Manufacturing Systems (SMS) Test Bed

July 1, 2016
Author(s)
Thomas D. Hedberg, Moneer M. Helu, Allison Barnard Feeney
Data is collected from the Manufacturing Lab using the MTConnect standard. That data is aggregated and published internally and externally of NIST via web services. Three channels of data dissemination are available or becoming available from the SMS Test

Identifying Performance Assurance Challenges for Smart Manufacturing

November 11, 2015
Author(s)
Moneer M. Helu, Katherine C. Morris, Kiwook Jung, Kevin W. Lyons, Swee K. Leong
Smart manufacturing has the potential to address many of the innovation drivers faced by industry. However, the manufacturing community often needs assistance to develop and use the variety of available technologies to improve their systems. To assure the

Measurement Science for Prognostics and Health Management for Smart Manufacturing Systems: Key Findings from a Roadmapping Workshop

October 23, 2015
Author(s)
Brian A. Weiss, Gregory W. Vogl, Moneer Helu, Guixiu Qiao, Joan Pellegrino, Mauricio Justiniano, Anand Raghunathan
The National Institute of Standards and Technology (NIST) hosted the Roadmapping Workshop - Measurement Science for Prognostics and Health Management for Smart Manufacturing Systems (PHM4SMS) in Fall 2014 to discuss the needs and priorities of stakeholders

Cloud-Enabled Prognosis for Manufacturing

June 22, 2015
Author(s)
Robert Gao, Lihui Wang, Roberto Teti, David Dornfeld, Soundar Kumara, Masahiko Mori, Moneer Helu
Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries for improved accuracy and reliability in predicting resource needs and allocation, maintenance

A Generalized Data-Driven Energy Prediction Model with Uncertainty for a Milling Machine Tool using Gaussian Process

June 8, 2015
Author(s)
Jinkyoo Park, Raunak Bhinge, Nishant Biswas, Amrita Srinivasan, Kincho H. Law, David Dornfeld, Moneer Helu, Sudarsan Rachuri
Using a machine learning approach, this study investigates the effects of machining parameters on the energy consumption of a milling machine tool, which would allow selection of optimal operational strategies to machine a part with the minimum energy