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PUBLICATIONS

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
Pub Type
Conferences

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Keywords

predictive metamodeling, optimization
Statistical analysis, Artificial intelligence and Additive manufacturing

Citation

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 December 11, 2024)

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Created August 25, 2018, Updated April 19, 2022