Development of RSM- and ANN-based models to predict and analyze the effects of process parameters of laser-hardened commercially pure titanium on heat input and tensile strength

Badkar, Duradundi Sawant; Pandey, K.S.; Buvanashekaran, G.

doi:10.1007/s00170-012-4259-0

Cited by 34 publications

(4 citation statements)

References 21 publications

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“…These results imply that ANNs provide more reasonable performance than the MRA and PRA. Previous studies also reported similar results, confirming that the predictive performance of ANNs is satisfactory [15][16][17][18]36,37]. PRA methods also show better predictive performance than the most commonly used MRA methods.…”

Section: Summary and Discussionsupporting

confidence: 74%

Comparison between Multiple Regression Analysis, Polynomial Regression Analysis, and an Artificial Neural Network for Tensile Strength Prediction of BFRP and GFRP

Kim

2021

Materials

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In this study, multiple regression analysis (MRA) and polynomial regression analysis (PRA), which are traditional statistical methods, were applied to analyze factors affecting the tensile strength of basalt and glass fiber-reinforced polymers (FRPs) exposed to alkaline environments and predict the tensile strength degradation. The MRA and PRA are methods of estimating functions using statistical techniques, but there are disadvantages in the scalability of the model because they are limited by experimental results. Therefore, recently, highly scalable artificial neural networks (ANN) have been studied to analyze complex relationships. In this study, the prediction performance was evaluated in comparison to the MRA, PRA, and ANN. Tensile strength tests were conducted after exposure for 50, 100, and 200 days in alkaline environments at 20, 40, and 60 °C. The tensile strength was set as the dependent variable, with the temperature (TP), the exposure day (ED), and the diameter (D) as independent variables. The MRA and PRA results showed that the TP was the most influential factor in the tensile strength degradation of FRPs, followed by the exposure time (ED) and diameter (D). The ANN method provided the best correlation between predictions and experimental values, with the lowest error and error rate. The PRA method applied to the response surface method outperformed the MRA method, which is most commonly used. These results demonstrate that ANN can be the most efficient model for predicting the durability of FRPs.

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Section: Summary and Discussionsupporting

confidence: 74%

Comparison between Multiple Regression Analysis, Polynomial Regression Analysis, and an Artificial Neural Network for Tensile Strength Prediction of BFRP and GFRP

Kim

2021

Materials

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“…The step-wise regression method detected that this model term was insignificant however, it was necessary by other significant model terms. Moreover, in Table 8, the adequacy measures of R 2 , adjusted R 2 and predicted R 2 were near to 1; they all agree well which is indicative of sufficient model [23]. The adequacy precision was greater than 4 which indicated sufficient model discrimination.…”

Section: Modeling and Analysis Of Variance For Coefficient Of Frictionmentioning

confidence: 72%

Mathematical Modeling and Analysis of Tribological Properties of AA6063 Aluminum Alloy Reinforced with Fly Ash by Using Response Surface Methodology

et al. 2020

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Lightweight, high-strength metal matrix composites have attracted considerable interest because of their attractive physical, mechanical and tribological properties. Moreover, they may offer distinct advantages due to good strength and wear resistance. In this research, AA6063 was reinforced with FA particles using compocasting methods. The effects of fly ash content, load, sliding speed and performance tribology of AA6063 –FA composite were evaluated. Dry sliding wear tests were carried out according to experimental design using the pin-on-disc method with three different loads (24.5, 49 and 73.5 N) and three speeds (150, 200 and 250 rpm) at room temperature. Response surface methodology (RSM) was used to analyze the influence of the process parameters on the tribological behavior of the composites. The surface plot showed that the wear rate increased with increasing load, time and sliding velocity. In contrast, the friction coefficient decreased with increasing these parameters. Optimal models for wear rate and friction coefficient showed appropriate results that can be estimated, hence reducing wear testing time and cost.

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“…In this study, 70% of the CCRD data (Table 2) were used to train the neural network model, 15% of the CCRD data were used to test, and 15% of the CCRD data were used to verify. The experimental data was divided into three parts in order to measure the performance of the neural network and predict the unobserved data [23]. Various learning algorithms were tested for training neural network models, and the best ANN model with a 4-10-1 topology was finally established (Figure 2).…”

Section: Resultsmentioning

confidence: 99%

Comparison of Artificial Neural Networks and Response Surface Methodology towards an Efficient Ultrasound-Assisted Extraction of Chlorogenic Acid from Lonicera japonica

Huang

Lin

et al. 2019

Molecules

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Chlorogenic acid (CGA), a bioactive compound commonly found in plants, has been demonstrated possessing nutraceutical potential in recent years. However, the more critical issue concerning how to improve production efficacy of CGA is still limited. It is a challenge to harvest a large amount of CGA without prolonging extraction time. In this study, the feasibility of using ultrasound for CGA extraction from Lonicera japonica was investigated. A central composite design (CCD) was employed to evaluate the effects of the operation parameters, including temperature, ethanol concentration, liquid to solid ratio, and ultrasound power on CGA yields. Meanwhile, the process of ultrasound-assisted extraction was optimized through modeling response surface methodology (RSM) and artificial neural network (ANN). The data indicated that CGA was efficiently extracted from the flower of Lonicera japonica by ultrasound assistance. The optimal conditions for the maximum extraction of CGA were as follows: The temperature at 33.56 °C, ethanol concentration at 65.88%, L/S ratio at 46:1 mL/g and ultrasound power at 150 W. ANN possessed greater optimization capacity than RSM for fitting experimental data and predicting the extraction process to obtain a maximum CGA yield. In conclusion, the process of ultrasound-assisted extraction can be well established by a methodological approach using either RSM or ANN, but it is worth mentioning that the ANN model used here showed the superiority over RSM for predicting and optimizing.

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Development of RSM- and ANN-based models to predict and analyze the effects of process parameters of laser-hardened commercially pure titanium on heat input and tensile strength

Cited by 34 publications

References 21 publications

Comparison between Multiple Regression Analysis, Polynomial Regression Analysis, and an Artificial Neural Network for Tensile Strength Prediction of BFRP and GFRP

Comparison between Multiple Regression Analysis, Polynomial Regression Analysis, and an Artificial Neural Network for Tensile Strength Prediction of BFRP and GFRP

Mathematical Modeling and Analysis of Tribological Properties of AA6063 Aluminum Alloy Reinforced with Fly Ash by Using Response Surface Methodology

Comparison of Artificial Neural Networks and Response Surface Methodology towards an Efficient Ultrasound-Assisted Extraction of Chlorogenic Acid from Lonicera japonica

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