Response Surface Methodology Using Observational Data: A Systematic Literature Review
In the response surface methodology (RSM), the designed experiment helps create interfactor orthogonality and interpretable response models for the purpose of process and design optimization. However, along with the development of data-recording technology, observational data have emerged as an alte...
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| Main Authors: | , , |
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| Format: | Article PeerReviewed |
| Language: | English |
| Published: |
MDPI
2022
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/282092/1/Hadiyat%20et%20al.%20-%202022%20-%20Response%20surface%20methodology%20using%20observational%20d.pdf https://repository.ugm.ac.id/282092/ https://www.mdpi.com/2076-3417/12/20/10663 |
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| Institution: | Universitas Gadjah Mada |
| Language: | English |
| Summary: | In the response surface methodology (RSM), the designed experiment helps create interfactor orthogonality and interpretable response models for the purpose of process and design optimization. However, along with the development of data-recording technology, observational data have emerged as an alternative to experimental data, and they contain potential information on design/process parameters (as factors) and product characteristics that are useful for RSM analysis. Recent studies in various fields have proposed modifications to the standard RSM procedures to adopt observational data and attain considerable results despite some limitations. This paper aims to explore various methods to incorporate observational data in the RSM through a systematic literature review. More than 400 papers were retrieved from the Scopus database, and 83 were selected and carefully reviewed. To adopt observational data, modifications to the procedures of RSM analysis include the design of the experiment (DoE), response modeling, and design/process optimization. The proposed approaches were then mapped to capture the sequence of the modified RSM analysis. The findings highlight the novelty of observational-data-based RSM (RSM-OD) for generating reproducible results involving the discussion of the treatments for observational data as an alternative to the DoE, the refinement of the RSM model to fit the data, and the adaptation of the optimization technique. Future potential research, such as the improvement of factor orthogonality and RSM model modifications, is also discussed. © 2022 by the authors. |
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