2018
DOI: 10.1186/s40712-018-0092-z
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Taguchi-fuzzy multi-response optimization in fly cutting process and applying in the actual hobbing process

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Cited by 3 publications
(2 citation statements)
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“…In [23], the Taguchi method was utilized to optimize the operating conditions of a thermoelectric generator, where the hot side temperature, the heat sink size, and the wind airspeed were the parameters to be optimized. Moreover, with imprecise or subjective data, fuzzy and genetic algorithm-based Taguchi methods have been investigated, which allow for a more realistic representation of uncertainties, leading to robust and reliable optimization results [24][25][26][27][28]. In general, the ways to improve heat dissipation can be categorized as liquid cooling and air cooling.…”
Section: Introductionmentioning
confidence: 99%
“…In [23], the Taguchi method was utilized to optimize the operating conditions of a thermoelectric generator, where the hot side temperature, the heat sink size, and the wind airspeed were the parameters to be optimized. Moreover, with imprecise or subjective data, fuzzy and genetic algorithm-based Taguchi methods have been investigated, which allow for a more realistic representation of uncertainties, leading to robust and reliable optimization results [24][25][26][27][28]. In general, the ways to improve heat dissipation can be categorized as liquid cooling and air cooling.…”
Section: Introductionmentioning
confidence: 99%
“…The Taguchi method provides a simple, efficient, and systematic approach to determine optimal parameters (Gopalsamy et al 2009). Compared to the factorial method, instead of testing all possible combinations of parameters available, the Taguchi method provides a more simplified way to set up the combination of experimental parameters (Ngo et al 2018). In recent years, statistical experimental designs have been employed to determine optimal HEBM parameters (Radune et al, 2015).…”
Section: Introductionmentioning
confidence: 99%