article

Regression with small data sets: a case study using code surrogates in additive manufacturing

Authors:

Chandrika Kamath,

Ya Ju FanAuthors Info & Claims

Knowledge and Information Systems, Volume 57, Issue 2

Pages 475 - 493

https://doi.org/10.1007/s10115-018-1174-1

Published: 01 November 2018 Publication History

Abstract

There has been an increasing interest in recent years in the mining of massive data sets whose sizes are measured in terabytes. However, there are some problems where collecting even a single data point is very expensive, resulting in data sets with only tens or hundreds of samples. One such problem is that of building code surrogates, where a computer simulation is run using many different values of the input parameters and a regression model is built to relate the outputs of the simulation to the inputs. A good surrogate can be very useful in sensitivity analysis, uncertainty analysis, and in designing experiments, but the cost of running expensive simulations at many sample points can be high. In this paper, we use a problem from the domain of additive manufacturing to show that even with small data sets we can build good quality surrogates by appropriately selecting the input samples and the regression algorithm. Our work is broadly applicable to simulations in other domains and the ideas proposed can be used in time-constrained machine learning tasks, such as hyper-parameter optimization.

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Cited By

Mypati OMukherjee AMishra DPal SChakrabarti PPal A(2023)A critical review on applications of artificial intelligence in manufacturingArtificial Intelligence Review10.1007/s10462-023-10535-y56:Suppl 1(661-768)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s10462-023-10535-y
Heroth MSchmid HHofmann W(2022)Efficient Sampling Algorithm for Electric Machine Design Calculations incorporating Empirical Knowledge2022 International Conference on Electrical Machines (ICEM)10.1109/ICEM51905.2022.9910814(1089-1095)Online publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1109/ICEM51905.2022.9910814
Liu JYe JSilva Izquierdo DVinel AShamsaei NShao S(2022)A review of machine learning techniques for process and performance optimization in laser beam powder bed fusion additive manufacturingJournal of Intelligent Manufacturing10.1007/s10845-022-02012-034:8(3249-3275)Online publication date: 15-Sep-2022
https://dl.acm.org/doi/10.1007/s10845-022-02012-0
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Regression with small data sets: a case study using code surrogates in additive manufacturing
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cover image Knowledge and Information Systems

Knowledge and Information Systems Volume 57, Issue 2

November 2018

244 pages

ISSN:0219-1377

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Copyright © Copyright © 2018 Springer-Verlag London Ltd., part of Springer Nature.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 November 2018

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Cited By

Mypati OMukherjee AMishra DPal SChakrabarti PPal A(2023)A critical review on applications of artificial intelligence in manufacturingArtificial Intelligence Review10.1007/s10462-023-10535-y56:Suppl 1(661-768)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s10462-023-10535-y
Heroth MSchmid HHofmann W(2022)Efficient Sampling Algorithm for Electric Machine Design Calculations incorporating Empirical Knowledge2022 International Conference on Electrical Machines (ICEM)10.1109/ICEM51905.2022.9910814(1089-1095)Online publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1109/ICEM51905.2022.9910814
Liu JYe JSilva Izquierdo DVinel AShamsaei NShao S(2022)A review of machine learning techniques for process and performance optimization in laser beam powder bed fusion additive manufacturingJournal of Intelligent Manufacturing10.1007/s10845-022-02012-034:8(3249-3275)Online publication date: 15-Sep-2022
https://dl.acm.org/doi/10.1007/s10845-022-02012-0
Pham THoang TVan Tran XPham QFetni SDuchêne LTran HHabraken A(2022)Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learningJournal of Intelligent Manufacturing10.1007/s10845-021-01896-834:4(1701-1719)Online publication date: 7-Jan-2022
https://dl.acm.org/doi/10.1007/s10845-021-01896-8

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