Multi-Response Optimization of Rigid Flange Coupling Using Taguchi Design, ANOVA, and FEA in Dual Environmental Conditions

Abstract

This study investigates the mechanical behaviour and optimization of rigid flange couplings operating under two distinct environmental conditions: normal atmospheric air and high-pressure oil surroundings. A Taguchi L9 orthogonal array was employed to evaluate material combinations for the shaft, flange, and bolt based on four mechanical responses: total deformation, equivalent stress, shear stress, and normal stress. Analysis of variance (ANOVA) and regression modelling were used to identify significant parameters, with flange material consistently emerging as the most influential factor. Desirability analysis was conducted to determine the optimal material configurations for each environment. Under atmospheric conditions, the combination of C30 shaft, FG200 flange, and C45 bolt achieved a composite desirability of 0.6667. In high-pressure oil conditions, the optimal configuration was C45 shaft, FG260 flange, and C45 bolt, with a desirability of 0.7185. These optimal settings, not present in the original matrix, were independently validated using finite element analysis (FEA). The comparison between regression predictions and FEA results showed strong agreement, with a maximum percentage error of 6.02%, within acceptable engineering limits. This study confirms that environmental pressure significantly influences coupling performance and that material selection should be tailored accordingly. The integration of statistical optimization and simulation offers a robust framework for designing couplings in pressure-sensitive applications.

Keywords

Rigid flange coupling / Taguchi method / ANOVA / Regression modeling / Finite element analysis / Environmental pressure

Acknowledgments

The authors would like to thank the technical staff at Bhagwant University’s Mechanical Engineering Department for their assistance with simulation support.

Author Contributions

Conceptualization, M.D. and S.B.; Methodology, M.D.; Software, S.B.; Validation, M.D., S.B. and M.V.; Formal Analysis, M.D.; Investigation, M.D.; Resources, M.D.; Data Curation, S.B.; Writing—Original Draft Preparation, M.D.; Writing—Review & Editing, M.V.; Visualization, S.B.; Supervision, M.V.; Project Administration, M.V.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to institutional policy.

Funding

This research received no external funding.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Use of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this manuscript, the authors used OpenAI’s ChatGPT to assist with language refinement, formatting, and technical editing in accordance with SCIEPublish’s editorial policies. After using this tool, the authors critically reviewed and manually edited the content to ensure accuracy, clarity, and scientific integrity. The authors take full responsibility for the content of the published article.

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