Response Surface Methodology Using Observational Data: A Systematic Literature Review

Abstract

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.

Sensitivity Analysis and Uncertainty Quantification of Nanoparticle Deposition from Tongue Morphological Variations

“…It estimated the propagation of input uncertainty to the simulation outputs. It is noted that the Gaussian process prediction is a distribution, not a number, which is different from conventional correlations (1D) or response surfaces (2D) [ 39 , 40 , 41 ].…”

Measuring Total Filtration Efficiency of Surgical and Community Face Masks: Impact of Mask Design Features

“…Using RSM it is possible to develop empirical polynomial equations relating the response to the factors. This methodology was originally developed to model experimental response [51] and then migrated into the modelling of numerical simulations [52] and observational data [53]. The use of RSM to model observational data was recently summarized in a review [53].…”

“…This methodology was originally developed to model experimental response [51] and then migrated into the modelling of numerical simulations [52] and observational data [53]. The use of RSM to model observational data was recently summarized in a review [53]. In our specific case the factors and their levels were production parameters set by the face mask manufacturers, while the TFE was an experimentally measured output, summarizing mask performance in terms of outward filtration efficacy.…”

Adoption of Response Surface Methodology for Optimization of Benzotriazole Additive in Ester Fluids as Transformer Insulant

“…The Response surface methodology (RSM) using central composite design (CCD) formulates the design of experiments (DOE) based on the limits provided on the input variables [22]. This technique is more effective in identifying the interaction between different independent input variable towards output responses with the analysis of variance (ANOVA) suggesting the prediction of suitable model [23]. Raj et al [24] have studied the transesterification reaction of Pongamia Pinnata oil considering the parameters of time, temperature, catalyst where its optimization using RSM provided a higher breakdown strength and flash point with less viscosity suitable for transformer applications.…”