Optimization of the effect of thermal barrier coating (TBC) on diesel engine performance by Taguchi method

Özel, Serkan; Vural, Erdinç; Binici, Murat

doi:10.1016/j.fuel.2019.116537

Cited by 38 publications

(13 citation statements)

References 28 publications

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“…The Taguchi method is a statistical approach using an orthogonal array (OA), consisting of factors and levels, to classify the results to optimize process parameters [16]. In the Taguchi method, the experimental design using OA tables enables the optimization process.…”

Section: Taguchi Methodsmentioning

confidence: 99%

Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images

2020

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Lung cancer is one of the common causes of cancer deaths. Early detection and treatment of lung cancer is essential. However, the detection of lung cancer in patients produces many false positives. Therefore, increasing the accuracy of the classification of diagnosis or true detection by computed tomography (CT) is a difficult task. Solving this problem using intelligent and automated methods has become a hot research topic in recent years. Hence, we propose a 2D convolutional neural network (2D CNN) with Taguchi parametric optimization for automatically recognizing lung cancer from CT images. In the Taguchi method, 36 experiments and 8 control factors of mixed levels were selected to determine the optimum parameters of the 2D CNN architecture and improve the classification accuracy of lung cancer. The experimental results show that the average classification accuracy of the 2D CNN with Taguchi parameter optimization and the original 2D CNN in lung cancer recognition are 91.97% and 98.83% on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset, and 94.68% and 99.97% on the International Society for Optics and Photonics with the support of the American Association of Physicists in Medicine (SPIE-AAPM) dataset, respectively. The proposed method is 6.86% and 5.29% more accurate than the original 2D CNN on the two datasets, respectively, proving the superiority of proposed model.

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Section: Taguchi Methodsmentioning

confidence: 99%

Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images

2020

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“…The problem of the complicated parameter setting process in CNNs can be overcome by using the Taguchi method, which can obtain the optimum parameters in a relatively short time with a relatively low cost and a small number of experiments. (21) Lin et al (9) used the Taguchi method to determine the optimum parameter combination for a CNN structure to improve the accuracy of lung cancer classification. On the basis of the aforementioned discussion, in this paper, we propose a CNN with Taguchi parametric optimization for facial image detection applications.…”

Section: Introductionmentioning

confidence: 99%

Integrated Image Sensor and Hyperparameter Optimization of Convolutional Neural Network for Facial Skin Detection

Lin¹,

Lin²,

Jeng³

et al. 2021

Sensors and Materials

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Detection of the facial skin condition enables people to better understand skin problems and helps them select appropriate treatment methods and maintenance products. We collected facial images of different skin problems by using an image optical sensor. To overcome the problem of insufficient training data, the collected dataset was expanded through data augmentation. In the field of computer vision, deep learning is often used for solving image recognition problems with high accuracy. Therefore, we used a convolutional neural network (CNN) to detect facial images of different skin problems. To overcome the difficulty of parameter selection and increase the recognition rate and robustness of the CNN, the parameters of the CNN were optimized using the Taguchi method. Eight control factors in the convolutional layer and the L36 orthogonal array (OA) were used in experiments. Analysis of variance was used for statistical analysis in the design of the experiments to obtain the optimal parameter combination for the developed CNNs. The experimental results indicate that the CNN optimized using the Taguchi method had an accuracy of 86.95%. The accuracy of the optimized CNN was 7.24% higher than that of the original CNN. The experimental results prove that the proposed hyperparameter optimization method can effectively improve the accuracy of network detection.

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“…They stated that the addition of hydrogen and nano-additives improves performance and reduces emissions. Özel et al [32] investigated the effects of thermal barrier coating on piston and valve surfaces on torque, power and BSFC experimentally and using Taguchi method at different engine loads. According to the results obtained from the study, they emphasized that they found p-values less than 0.05 for all outputs and accordingly obtained statistically significant results.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Biodiesel and Diethyl Ether Doped Diesel Engine by Taguchi Method

Uslu

2020

International Journal of Automotive Science and Technology

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In present study, it was targeted to optimize multiple impacts of palm oil ratio, diethyl ether (DEE) percentage and engine load on the performance and emissions of a single cylinder diesel engine. The experiments were de-signed on the basis of the Taguchi L27 orthogonal array, which accepts palm oil percentage, DEE ratio and load as variables, brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), nitrogen oxide (NOx) emis-sion and smoke emission as responses. The impacts of engine running factors on responses were defined by analysis of variance (ANOVA). S / N ratio and interactions plots were created for each answer to show optimum factor lev-els. The results showed that the most effective parameters in general on all engine outputs are the load and DEE ratio. In addition, considering the find-ings obtained, the best working variables of the engine were determined as 5% DEE percentage, 6% palm oil percentage, and 850-Watt load. The best BTE, BSFC, NOx and smoke emission obtained according to optimum work-ing parameters were found as 30.73%, 824.59 g/kWh, 292.20 ppm and 68.91%, respectively. Mean errors between experimental and optimized re-sults were found as 0.46%, 1.75%, 3.72% and 0.16% for BTE, BSFC, NOx and smoke emission, respectively. These results showed that the Taguchi de-sign method can be used as an effective tool to determine the impact rates of engine operating parameters and to optimize operating parameters, engine performance and emissions based on the determined impact rates.

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Optimization of the effect of thermal barrier coating (TBC) on diesel engine performance by Taguchi method

Cited by 38 publications

References 28 publications

Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images

Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images

Integrated Image Sensor and Hyperparameter Optimization of Convolutional Neural Network for Facial Skin Detection

Multi-Objective Optimization of Biodiesel and Diethyl Ether Doped Diesel Engine by Taguchi Method

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