Using Taguchi's experiments Part III: Choosing Raw Materials According to Process Yields

Objective: The aim of this work is to show the application of the Taguchi Experiment in a Chemical Industry Process. Theoretical framework: A Taguchi design, or orthogonal array, is a method for designing experiments that usually require only a fraction of the complete factorial combinations. The orthogonal array means that the design is balanced, so that the factor levels are weighted equally. Therefore, each factor can be evaluated independently of all the other factors, so that the effect of one factor does not influence the estimate of another factor: Static experiments (without a sign factor) and dynamic experiments (with a sign factor) (Moura et al., 2023). Method: Experimental data was taken from a Chemical Industry in the South of the State of Rio de Janeiro and the data was treated with the Taguchi Experiment. Final Considerations: It can be concluded that the Taguchi experiment is a very viable option for optimizing the performance of industrial processes. In this specific case of the factory, it can be concluded that the most significant factors in order of importance are Factors A, B and C. Implications of the research: The use cases of the Taguchi experiment are multiplying in the scientific literature and have proven to be highly effective in dealing with data where the assumptions of Normality are confirmed. Originality/value: Despite being well-known statistical tools, the Taguchi Experiment is widely used and can bring innovations in its application, as in the case of the company in question.

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Publication Details

Journal
Revista de Gestão Social e Ambiental
Published
2025-04-03
DOI
https://doi.org/10.24857/rgsa.v19n4-016
Citations
1
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
1.24
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article

Using Taguchi's experiments Part III: Choosing Raw Materials According to Process Yields

Ignacio Miranda, Carlos Eduardo da Silva, Sílvio Sérgio Silveira de Siqueira, Nilo Antônio de Souza Sampaio et al.
1 citations
Revista de Gestão Social e Ambiental
Manufacturing Process and Optimization
1.24
article

Using Taguchi's experiments Part III: Choosing Raw Materials According to Process Yields

Ignacio Miranda, Carlos Eduardo da Silva, Sílvio Sérgio Silveira de Siqueira, Nilo Antônio de Souza Sampaio, Fabíola Freitas de Miranda, Gláucia Aparecida Prates, Mariane Ribeiro Vieira
article en
1 citations

Abstract

Objective: The aim of this work is to show the application of the Taguchi Experiment in a Chemical Industry Process. Theoretical framework: A Taguchi design, or orthogonal array, is a method for designing experiments that usually require only a fraction of the complete factorial combinations. The orthogonal array means that the design is balanced, so that the factor levels are weighted equally. Therefore, each factor can be evaluated independently of all the other factors, so that the effect of one factor does not influence the estimate of another factor: Static experiments (without a sign factor) and dynamic experiments (with a sign factor) (Moura et al., 2023). Method: Experimental data was taken from a Chemical Industry in the South of the State of Rio de Janeiro and the data was treated with the Taguchi Experiment. Final Considerations: It can be concluded that the Taguchi experiment is a very viable option for optimizing the performance of industrial processes. In this specific case of the factory, it can be concluded that the most significant factors in order of importance are Factors A, B and C. Implications of the research: The use cases of the Taguchi experiment are multiplying in the scientific literature and have proven to be highly effective in dealing with data where the assumptions of Normality are confirmed. Originality/value: Despite being well-known statistical tools, the Taguchi Experiment is widely used and can bring innovations in its application, as in the case of the company in question.

Revista de Gestão Social e AmbientalVol. 19(4)
Openalex Percentile: Top 21%
Manufacturing Process and Optimization
1.24
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