Optimal surrogate building using SVR for an industrial grinding process

Inapakurthi, Ravi Kiran and Mitra, Kishalay (2022) Optimal surrogate building using SVR for an industrial grinding process. Materials and Manufacturing Processes. pp. 1-7. ISSN 1042-6914

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Transient states modeling of industrial grinding process with significant accuracy is extremely essential to run these energy intensive processes in optimal conditions ensuring sustainability. Traditional modeling using physics-based approach not only demands extensive process knowledge but also results in time-expensive models difficult to use during iterative processes like optimization. Proposing Support Vector Regression (SVR) as an alternative data driven tool, such a surrogate building task has been performed under an optimization framework. Minimizing square root of mean square error (RMSE) between ground truth and model predictions, optimal hyper-parameter combination is achieved using a novel genetic algorithm-based formulation indicating a paradigm shift as opposed to the usual practice of determining them heuristically. The RMSE of the grinding model obtained using the proposed formulation is reported as 0.00496. When compared with another model obtained using conventional approach, prediction plots indicate the effectiveness of the novel algorithm. Comparison with coefficient of correlation leads similar conclusion, where the least RMSE model has 99.88% and the conventional model has 71.40% correlation value. Such dynamic surrogates with optimal hyper-parameter settings can be extremely useful for control and optimization of grinding processes and can be easily extendable to design of experiment-based response surface modeling. © 2022 Taylor & Francis.

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IITH Creators:
IITH CreatorsORCiD
Mitra, Kishalayhttp://orcid.org/0000-0001-5660-6878
Item Type: Article
Additional Information: Authors acknowledge the support provided by the National Supercomputing Mission, Department of Science and Technology, Government of India [DST/NSM/R&D_HPC_Applications/2021/23], Ministry of Human Resources Development (MHRD), Government of India [SPARC/2018-2019/P1084/SL] and Department of Bio-Technology, Government of India [BT/PR34209/AI/133/19/2019] for this work.
Uncontrolled Keywords: algorithm; computation; Grinding; manufacturing; modeling; optimization; processing; surrogate
Subjects: Chemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 23 Jul 2022 10:13
Last Modified: 23 Jul 2022 10:13
URI: http://raiith.iith.ac.in/id/eprint/9890
Publisher URL: http://doi.org/10.1080/10426914.2022.2039699
OA policy: https://v2.sherpa.ac.uk/id/publication/5840
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