Grey Relational Analysis-Based Optimization of Machining Processes: a Comprehensive Review

Chakraborty, Santonab; Datta, Himalaya Nirjhar; Chakraborty, Shankar

doi:10.1007/s41660-023-00311-4

Cited by 9 publications

(2 citation statements)

References 183 publications

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“…Grey relational analysis (GRA) is a method used for analyzing the relationships between multiple factors or responses within a dataset. It was developed by Deng in the late 1980s [ 39 ] and has since been applied in various fields such as engineering, economics, and decision-making [ 40 , 41 , 42 ]. The fundamental principle of GRA lies in its ability to assess the similarity or correlation between different responses, even when they are measured using different units or scales.…”

Section: Methodsmentioning

confidence: 99%

Parametric Optimization of FDM Process for PA12-CF Parts Using Integrated Response Surface Methodology, Grey Relational Analysis, and Grey Wolf Optimization

Almuflih,

Abas,

Khan

et al. 2024

Polymers

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Efficiently managing multiple process parameters is critical for achieving optimal performance in additive manufacturing. This study investigates the relationship between eight key parameters in fused deposition modeling (FDM) and their impact on responses like average surface roughness (Ra), tensile strength (TS), and flexural strength (FS) of carbon fiber-reinforced polyamide 12 (PA 12-CF) material. The study integrates response surface methodology (RSM), grey relational analysis (GRA), and grey wolf optimization (GWO) to achieve this goal. A total of 51 experiments were planned using a definitive screening design (DSD) based on response RSM. The printing process parameters, including layer thickness, infill density, and build orientation, significantly affect Ra, TS, and FS. GRA combines responses into a single measure, grey relational grade (GRG), and a regression model is developed. GWO is then employed to optimize GRG across parameters. Comparison with GRA-optimized parameters demonstrates GWO’s ability to discover refined solutions, reducing average surface roughness to 4.63 μm and increasing tensile strength and flexural strength to 88.5 MPa and 103.12 MPa, respectively. Practical implications highlight the significance of GWO in industrial settings, where optimized parameters lead to reduced costs and improved product quality. This integrated approach offers a systematic methodology for optimizing FDM processes, ensuring robustness and efficiency in additive manufacturing applications.

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Section: Methodsmentioning

confidence: 99%

Parametric Optimization of FDM Process for PA12-CF Parts Using Integrated Response Surface Methodology, Grey Relational Analysis, and Grey Wolf Optimization

Almuflih,

Abas,

Khan

et al. 2024

Polymers

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“…Subsequently, the Grey Relational Grade (γ), presenting the level of correlation between the reference sequence and the comparability sequence and is the overall representative of all the quality characteristics is calculated as in Equation (6). Notably, by determining the Grey Relational Grade, the multiresponse problem is converted into a single response optimization problem [29,30].…”

Section: The Multiparametric Optimization Algorithmmentioning

confidence: 99%

Multi-Attribute Decision Making: Parametric Optimization and Modeling of the FDM Manufacturing Process Using PLA/Wood Biocomposites

Morvayová,

Contuzzi,

Fabbiano

et al. 2024

Materials

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The low carbon footprint, biodegradability, interesting mechanical properties, and relatively low price are considered some of the reasons for the increased interest in polylactic acid-based (PLA-based) filaments supplied with natural fillers. However, it is essential to recognize that incorporating natural fillers into virgin PLA significantly impacts the printability of the resulting blends. The complex inter-relationship between process, structure, and properties in the context of fused deposition modeling (FDM)-manufactured biocomposites is still not fully understood, which thus often results in decreased reliability of this technology in the context of biocomposites, decreased accuracy, and the increased presence of defects in the manufactured biocomposite samples. In light of these considerations, this study aims to identify the optimal processing parameters for the FDM manufacturing process involving wood-filled PLA biocomposites. This study presents an optimization approach consisting of Grey Relational Analysis in conjunction with the Taguchi orthogonal array. The optimization process has identified the combination of a scanning speed of 70 mm/s, a layer height of 0.1 mm, and a printing temperature of 220 °C as the most optimal, resulting in the highly satisfactory combination of good dimensional accuracy (Dx = 20.115 mm, Dy = 20.556 mm, and Dz = 20.220 mm) and low presence of voids (1.673%). The experimentally determined Grey Relational Grade of the specimen manufactured with the optimized set of process parameters (0.782) was in good agreement with the predicted value (0. 754), substantiating the validity of the optimization process. Additionally, the research compared the efficacy of optimization between the integrated multiparametric method and the conventional monoparametric strategy. The multiparametric method, which combines Grey Relational Analysis with the Taguchi orthogonal array, exhibited superior performance. Although the monoparametric optimization strategy yielded specimens with favorable values for the targeted properties, the analysis of the remaining characteristics uncovered unsatisfactory results. This highlights the potential drawbacks of relying on a singular optimization approach.

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Optimization of input parameters during drilling of glass fiber reinforced polymer composite using grey relational analysis

Pathak,

Malik,

Mali

2024

Int J Interact Des Manuf

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Grey Relational Analysis-Based Optimization of Machining Processes: a Comprehensive Review

Cited by 9 publications

References 183 publications

Parametric Optimization of FDM Process for PA12-CF Parts Using Integrated Response Surface Methodology, Grey Relational Analysis, and Grey Wolf Optimization

Parametric Optimization of FDM Process for PA12-CF Parts Using Integrated Response Surface Methodology, Grey Relational Analysis, and Grey Wolf Optimization

Multi-Attribute Decision Making: Parametric Optimization and Modeling of the FDM Manufacturing Process Using PLA/Wood Biocomposites

Optimization of input parameters during drilling of glass fiber reinforced polymer composite using grey relational analysis

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