Application of Taguchi Method and Anova Techniques to Maximize HVOF Spraying to WC-12Co

Dinh, Văn Chien; Nguyen, Tuan Hai; Xuan, Nguyen Van

doi:10.4028/www.scientific.net/kem.854.109

Cited by 3 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the literature, coating process modeling methods can be classified into statisticalmodel-based methods and artificial-intelligence-based methods [3,4]. Response surface methodology [5,6], analysis of variance [7], the finite element method [8], the Taguchi design method [9,10], and other statistical analyses [11,12] are frequently used statistical methods. However, statistical-model-based methods have the limitation of subjectively selecting coating control conditions by executing the designs of experiments.…”

Section: Introductionmentioning

confidence: 99%

Self-Attention-Augmented Generative Adversarial Networks for Data-Driven Modeling of Nanoscale Coating Manufacturing

Zhu

Yang

et al. 2022

Micromachines

View full text Add to dashboard Cite

Nanoscale coating manufacturing (NCM) process modeling is an important way to monitor and modulate coating quality. The multivariable prediction of coated film and the data augmentation of the NCM process are two common issues in smart factories. However, there has not been an artificial intelligence model to solve these two problems simultaneously. Focusing on the two problems, a novel auxiliary regression using a self-attention-augmented generative adversarial network (AR-SAGAN) is proposed in this paper. This model deals with the problem of NCM process modeling with three steps. First, the AR-SAGAN structure was established and composed of a generator, feature extractor, discriminator, and regressor. Second, the nanoscale coating quality was estimated by putting online control parameters into the feature extractor and regressor. Third, the control parameters in the recipes were generated using preset parameters and target quality. Finally, the proposed method was verified by the experiments of a solar cell antireflection coating dataset, the results of which showed that our method performs excellently for both multivariable quality prediction and data augmentation. The mean squared error of the predicted thickness was about 1.6~2.1 nm, which is lower than other traditional methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Self-Attention-Augmented Generative Adversarial Networks for Data-Driven Modeling of Nanoscale Coating Manufacturing

Zhu

Yang

et al. 2022

Micromachines

View full text Add to dashboard Cite

show abstract

A study of investigating the effects of variables and assessing the efficiency of air plasma spray as a coating technique

Gaur,

Pandel,

Sharma

2023

Materials Today: Proceedings

View full text Add to dashboard Cite

Optimal Process Parameters for a Thermal-Sprayed Molybdenum-Reinforced Zirconium Diboride Composite on a Dummy Substrate

et al. 2022

View full text Add to dashboard Cite

Thermal spray is an effective process for the fabrication of a metal matrix composite (MMC), where a zirconium diboride reinforcement is embedded in a molybdenum matrix to enable the combining of favorable properties in a new composite. The combination of two leading materials in the category of ultra-high-temperature ceramics (UHTCs) is due to a very high melting point (Mo: 2623 °C and ZrB2: 3245 °C), high thermal conductivity (Mo: 139 W/m°C and ZrB2: 24 W/m°C), good thermal shock resistance, low coefficient of thermal expansion (Mo: 5.35 µm/m°C and ZrB2: 5.9 × 10−6 K−1), retention of strength at elevated temperatures and stability in extreme environments. Thermal spraying of the Mo/ZrB2 composite possesses a non-linear behavior that is influenced by many coating variables. This characteristic makes finding the optimal factor combination difficult. Therefore, an effective and strategic statistical approach incorporating systematic experimental data is needed to optimize the process. In this study, the L9 orthogonal array in the Taguchi approach was utilized to optimize the spraying distance (SD), number of passes (NP), pressure (P) and coat-face temperature (TCF) using a dummy fiberglass substrate. The performance was evaluated based on the coating density (Cd) of the surfaces. Based on confirmation tests, our Taguchi analysis determined the ideal process parameters, which considerably enhanced the coating process. From the output response of the ANOVA, the most influential parameters for achieving a high coating density (Cd) were determined to be SD = 20 cm, NP = 24, P = 4 bar and TCF = 330 °C ((SD.)1-(NP.)3-P2-(S.T.)3). These observations show that the coating density (Cd) was significantly influenced by the coat-face temperature, followed by the number of passes, spraying distance and pressure with the following contributions 6.29, 17.89, 17.42 and 3.35%, respectively.

show abstract

Application of Taguchi Method and Anova Techniques to Maximize HVOF Spraying to WC-12Co

Cited by 3 publications

References 8 publications

Self-Attention-Augmented Generative Adversarial Networks for Data-Driven Modeling of Nanoscale Coating Manufacturing

Self-Attention-Augmented Generative Adversarial Networks for Data-Driven Modeling of Nanoscale Coating Manufacturing

A study of investigating the effects of variables and assessing the efficiency of air plasma spray as a coating technique

Optimal Process Parameters for a Thermal-Sprayed Molybdenum-Reinforced Zirconium Diboride Composite on a Dummy Substrate

Contact Info

Product

Resources

About