Analysis of individual and interaction effects of processing parameters on wet grinding performance in ball milling of alumina ceramics using statistical methods

Yu, Jungwon; Jin, So-Hyun; Raju, Kati; Lee, Young Jae; Lee, Hyun-Kwuon

doi:10.1016/j.ceramint.2021.07.296

Cited by 8 publications

(1 citation statement)

References 36 publications

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“…The Taguchi method is an economical and efficient experimental design method. It emphasises optimisation through design and not through many experiments and introduces the influence of uncontrollable factors into the experimental design [ 28 , 29 , 30 , 31 , 32 , 33 ]. The Taguchi method combines orthogonal experiments and analysis of variance (ANOVA) [ 34 , 35 , 36 , 37 , 38 , 39 ] to determine the best combination of controllable factors to reduce the impact of uncontrollable factors on system quality fluctuations.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimisation of Curing Cycle of Thick Aramid Fibre/Epoxy Composite Laminates

et al. 2021

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Aramid fibre-reinforced epoxy composites (AF/EP) are promising materials in the aerospace, transportation, and civil fields owing to their high strength, high modulus, and light weight. Thick composite laminates are gradually being applied to large composite structures such as wind turbine blades. During curing, temperature overheating is a common problem in thick composites, which leads to matrix degradation, thermal residual stresses, and uneven curing. This paper proposes a signal-to-noise ratio (SNR) method to optimise the curing cycle of thick AF/EP laminates and reduce the overheating temperature. During curing, the temperature and strain evolution in a thick AF/EP laminate were monitored using fibre Bragg grating sensors. The effects of the curing factors on the overheating temperature of the thick AF/EP laminate were evaluated using the Taguchi method and predicted via the SNR method and analysis of variance. The results indicate that the dwelling temperature is the main factor affecting the overheating temperature. The optimal curing cycle involves an overheating temperature of 192.72 °C, which constitutes an error of 2.58% compared to the SNR method predictions. Additionally, in comparison to the initial curing cycle, the overshoot temperature in the optimised curing cycle was reduced by 58.48 °C, representing a reduction ratio of 23.28%.

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Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimisation of Curing Cycle of Thick Aramid Fibre/Epoxy Composite Laminates

et al. 2021

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A machine learning approach for ball milling of alumina ceramics

Raju

Jin

et al. 2022

Int J Adv Manuf Technol

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In this work, machine learning (ML) approach based on polynomial regression was explored to analyze the optimal processing parameters and predict the target particle sizes for ball milling of alumina ceramics. Data points were experimentally obtained by measuring the particle sizes. Prediction interval (PI)-based optimization methods using polynomial regression analysis are proposed. As a first step, functional relations between the inputs and the responses are derived by applying the regression analysis. Later, based on these relations, objective functions to be maximized are defined by desirability approach. Finally, the proposed PI-based methods optimize both parameter points and intervals of the target mill for accomplishing userspecified target responses. The optimization results show that the PI-based point optimization methods can select and recommend statistically reliable optimized parameter points even though unique solutions for the objective functions do not exist. From the results of confirmation experiments, it is established that the optimized parameter points can produce desired particle sizes with quality responses quite similar to the target responses.

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Development of design method for wet stirred ball milling by simulation using DEM

Kushimoto,

Kondo,

Kozawa

et al. 2024

Advanced Powder Technology

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Analysis of individual and interaction effects of processing parameters on wet grinding performance in ball milling of alumina ceramics using statistical methods

Cited by 8 publications

References 36 publications

Multi-Objective Optimisation of Curing Cycle of Thick Aramid Fibre/Epoxy Composite Laminates

Multi-Objective Optimisation of Curing Cycle of Thick Aramid Fibre/Epoxy Composite Laminates

A machine learning approach for ball milling of alumina ceramics

Development of design method for wet stirred ball milling by simulation using DEM

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