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- MakaleAçık Erişim2024Scopus SJRQ1Optimization of mechanochemical surface modification parameters on planetary mill using an I-optimal experimental design
Özdemir, Akın; Uçurum, Metin
Several micro-particle-size additives and filler minerals are added to many industrial products, especially plastic, in order to reduce cost and improve mechanical properties and appearance. Also, grinding and surface modification are two fundamental properties of the filler minerals used in plastic products. Notably, Bayburt stone (BS) used as a natural raw material from Bayburt, Türkiye contains 76% SiO2 and 15% Al2O3. In this paper, the hydrophilic surface structure of micronized Bayburt stone (MBS) was made hydrophobe by mechanochemical surface modification using calcium stearate [Ca(C17H35COO)2] in a laboratory-type planetary mill. Next, the optimum conditions of the surface modification design factors, which are (1) ball filling ratio = 30.53%, (2) powder filling ratio = 10.43%, (3) mill speed = 475.91 rpm, (4) chemical dosage = 1.99% of powder, and (5) modification time = 4.83 min., were obtained using the proposed I-optimal design-focused optimization model when predicting the model parameters. In addition, a coated micronized Bayburt stone (CMBS) product with an active ratio (AR) of 99.90% and d10, d50, and d90 values of 1.32 μm, 3.48 μm and 9.55 μm were achieved. Finally, particle size distribution (PSD), thermo-gravimetric and differential thermal analyzer (TG-DTA), Fourier transform infrared (FTIR), scanning electron microscope (SEM), electron dispersive spectrum (EDS), and contact angle analyses were performed on the CMBS product.
AtıfScopus5WoS6OpenAlex6
İncele - MakaleAçık Erişim2021Scopus SJRQ2An I-optimal experimental design-embedded nonlinear lexicographic goal programming model for optimization of controllable design factors
Ozdemir, Akin
Optimal experimental designs are effective offline quality improvement techniques used to enhance existing products and develop new products for a constrained design region. For some practical situations, I-optimal experimental designs may be the most suitable way to construct optimal experimental design points, by addressing prediction variance to generate a measure of prediction performance over the non-standard experimental design region. The purpose of this article is three-fold: (1) the I-optimality criterion is selected for minimizing the average prediction variance; (2) fitted mean and variance response models are found; and (3) a nonlinear lexicographic goal programming model incorporating an I-optimal experimental design is proposed to find the optimum settings of controllable design factors. Comparison studies and a numerical example are provided to illustrate the effectiveness of the proposed methodology for improvement of the quality of products.
AtıfScopus15WoS15OpenAlex16İncele