NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.
Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.
An official website of the United States government
Here’s how you know
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
A SUPER-METAMODELLING FRAMEWORK TO OPTIMIZE SYSTEM PREDICTABILITY
Published
Author(s)
Yan Lu, Douglas Eddy, Sundar Krishnamurty, Ian Grosse
Abstract
Statistical metamodels can robustly predict manufacturing process and engineering systems design results. Various techniques, such as Kriging, polynomial regression, artificial neural network and others, are each best suited for different scenarios that can range across a design space. Thus, methods are needed to identify the most appropriate metamodel or model composite for a given problem. To account for pros and cons of different metamodeling techniques for a wide diversity of data sets, in this paper we introduce a super-metamodel optimization framework (SMOF) to improve overall prediction accuracy by integrating different metamodeling techniques without a need for additional data. The SMOF defines an iterative process first to construct multiple metamodels using different methods and then aggregate them into a weighted composite and finally optimize the super-metamodel through advanced sampling. The optimized super-metamodel can reduce an overall prediction error and sustains the performance regardless of dataset variation. To verify the method, we apply it to 24 test problems representing various scenarios. A case study conducted with additive manufacturing process data shows method effectiveness in practice.
Proceedings Title
38th Computers and Information in Engineering Conference (CIE)
Conference Dates
August 27-29, 2018
Conference Location
Quebec, CA
Conference Title
International Design Engineering Technical Conferences
& Computers and Information in Engineering Conference
Lu, Y.
, Eddy, D.
, Krishnamurty, S.
and Grosse, I.
(2018),
A SUPER-METAMODELLING FRAMEWORK TO OPTIMIZE SYSTEM PREDICTABILITY, 38th Computers and Information in Engineering Conference (CIE), Quebec, CA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=925533
(Accessed October 9, 2025)