2017
DOI: 10.1108/gs-05-2017-0013
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Taguchi-based combined grey relational and principal component analyses for multi-response optimization of diesel engines

Abstract: Purpose Diesel engine can produce power more efficiently with lower exhaust emissions when operated at optimum input parameter settings. To achieve this goal, the purpose of this paper is to optimize the input parameters of diesel engine which will lead to optimum performance and exhaust emissions. Design/methodology/approach To achieve the goal of improving diesel engine performance and exhaust emissions, four input parameters were considered in the study. Five different levels of each input parameter were … Show more

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Cited by 9 publications
(6 citation statements)
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“…Taguchi's Robust Design methodology is a popular optimization technique to ensure the robustness of the product by accommodating both noise and control parameters (Almansoori et al , 2020). However, it has the limitation of optimizing a single output (Muqeem et al , 2017), while the GRA assists in drawing a robust inference by transforming the multi-output into a single output optimization problem (Naresh et al , 2014; Neeli et al , 2018). This approach is used to establish optimal solutions to uncertain problems having incomplete information and discrete data (Wu, 2002).…”
Section: Case Studymentioning
confidence: 99%
“…Taguchi's Robust Design methodology is a popular optimization technique to ensure the robustness of the product by accommodating both noise and control parameters (Almansoori et al , 2020). However, it has the limitation of optimizing a single output (Muqeem et al , 2017), while the GRA assists in drawing a robust inference by transforming the multi-output into a single output optimization problem (Naresh et al , 2014; Neeli et al , 2018). This approach is used to establish optimal solutions to uncertain problems having incomplete information and discrete data (Wu, 2002).…”
Section: Case Studymentioning
confidence: 99%
“…MADM techniques are best suited when selection problem involves attributes/criteria that are conflicting in nature. Literature reveals application of these techniques for solving decision problems pertaining to different knowledge domain [15][16][17][18][19]. For instance, Mufazzal and Muzakkir [20] proposed a novel MADM technique called Proximity Index Value (PIV) method to minimize the rank reversal problems arising due to either addition or deletion of alternatives.…”
Section: Introductionmentioning
confidence: 99%
“…The measured output values from the Taguchi design OA are accepted as input and normalized as the initial step of data pre-processing. The normalized values now fall inside the interval of 0 to 1 [37]. The normalization equation for the smaller the better characteristic is written as shown in Equation 3.…”
Section: 52normalizing the S/n Ratio By Gramentioning
confidence: 99%