2018
DOI: 10.15376/biores.14.1.1110-1126
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Integration of Taguchi-Grey relational analysis technique in parameter process optimization for rice husk composite

Abstract: Injection molding is a widely used manufacturing process operation that generates polymer products. The selection of optimal injection molding process settings is essential due to the distinct influences of process parameters on polymeric material behavior and quality, particularly during the injection process. Therefore, it is vital to determine the optimized process parameters to enhance the mechanical properties of the products and ensure the most favorable performance. This paper examined the integration o… Show more

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Cited by 13 publications
(5 citation statements)
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“…The combination of 20% BF and 15% CF content is crucial for maximising mechanical properties. The taguchi technique, based on GRA, was effectively utilised in a variety of manufacturing domains to optimise multi-response characteristics of complex issues [32][33][34][35][36][37]. The best fabrication parameters for MPP composite are found in this work using the Taguchi based GRA, while ANOVA is performed to confirm the findings results.…”
Section: Introductionmentioning
confidence: 65%
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“…The combination of 20% BF and 15% CF content is crucial for maximising mechanical properties. The taguchi technique, based on GRA, was effectively utilised in a variety of manufacturing domains to optimise multi-response characteristics of complex issues [32][33][34][35][36][37]. The best fabrication parameters for MPP composite are found in this work using the Taguchi based GRA, while ANOVA is performed to confirm the findings results.…”
Section: Introductionmentioning
confidence: 65%
“…Additionally, it mentions the limitation of Taguchi's approach for optimizing a single response attribute and introduces the GRG developed by Deng (1989) for quantifying relatedness between sequences. Using GRA along with the Taguchi approach increases optimisation process efficiency [35][36][37][38][39]. When there is inadequate data from the Taguchi technique, the GRA effectively predicts the rank of the process parameter.…”
Section: Gramentioning
confidence: 99%
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“…The optimization of the performance characteristics of the turning operations such as the material removal rate and the surface roughness concluded that the complicated multiple performance characteristics of the processes can be greatly simplified by using the grey‐based Taguchi method [20]. The integration of grey relational analysis and Taguchi method successfully assessed the feasibility of each process parameter in injection molding towards the mechanical properties of rice husk composites [21]. In this work, an attempt has been made to analyse the influences of reinforcement of marble dust and basalt in aluminium 7075 on multi performance characteristics such as wear rate and coefficient of friction simultaneously using grey Taguchi method.…”
Section: Introductionmentioning
confidence: 99%
“…These studies are generally based on optimizing the production methods or determining the filler quantities for mechanical properties of the composite. [42][43][44][45][46][47][48][49] Multi-criteria decision-making (MCDM) methods to optimize the mixing amount with biopolymers (PLA, PHB) and wood filler into the petroleum-based polymers (PP) have not been encountered in the literature. The aim of this study was to conduct parameter optimization with Fuzzy and Grey relational analysis of physical, mechanical, thermal, and morphological properties of wood flour reinforced polypropylene/biopolymer blend composites.…”
Section: Introductionmentioning
confidence: 99%