Optimization and prediction of tribological behaviour of filled polytetrafluoroethylene composites using Taguchi Deng and hybrid support vector regression models

Ibrahim, Musa Alhaji; Çamur, Hüseyin; Savas, Mahmut A.; Abba, S.I.

doi:10.1038/s41598-022-14629-5

Cited by 12 publications

(7 citation statements)

References 56 publications

(54 reference statements)

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“…During training, the network updates these prior distributions based on the observed data, resulting in posterior distributions that reflect the updated beliefs about the weights. Instead of providing a single prediction, BRANNs can generate a distribution of predictions, [27][28][29] which represents the uncertainty associated with each prediction. This uncertainty estimation can be valuable in decision-making processes where knowing the confidence or reliability of the predictions is crucial.…”

Section: Discussionmentioning

confidence: 99%

Prediction of QCE using ANN and ANFIS for milling Alloy 2017A

Bousnina,

Hamza,

Ben Yahia

2023

Advances in Mechanical Engineering

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Population growth and economic development, particularly in developing market nations, are driving up global energy consumption at an alarming rate. Despite increased wealth, growing demand presents new obstacles. Computer Numerical Control (CNC) machine tools are widely used in most metal machining processes due to their efficiency and repeatability in achieving high-precision machining. It has been shown that figuring out the best cutting parameters can improve the results of machining, leading to high efficiency and low costs. This study identifies and examines thoroughly the scientific contributions of the influence of strategies, machining sequences, and cutting parameters on surface quality, machining cost, and energy consumption (QCE) using artificial intelligence (ANN and ANFIS). The results show that the 3.10−3 architecture with the Bayesian Regularization (BR) algorithm is the optimal neural architecture that yields an overall mean square error (MSE) of 2.74 10−3. The correlation coefficients ( R2) for Etot, Ctot, and Ra are 0.9992, 1, and 0.9117 respectively. Similarly, for the adaptive neuro-fuzzy inference system (ANFIS), the optimal structure which gives a better error and better correlation is the {2, 2, 2} structure, and this for the three output variables (Etot, Ctot, and Ra). The correlation coefficient ( R2) for the variables Etot, Ctot, and Ra are respectively 0.95, 0.965, and 0.968. The results show that the use of the Bayesian Regularization algorithm with a multi-criteria output response can give good results when compared with the adaptive neuro-fuzzy inference system.

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Section: Discussionmentioning

confidence: 99%

Prediction of QCE using ANN and ANFIS for milling Alloy 2017A

Bousnina,

Hamza,

Ben Yahia

2023

Advances in Mechanical Engineering

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“…Previous researchers made similar observations. 11,40,42 Figure 11 shows the wear rates of PTFE, PA, and PP composites. Compared with PTFE, PTFE-Cu shows much better wear resistance.…”

Section: Wear Rates For Different Compositesmentioning

confidence: 99%

“…Thus, increased damage on the surface increases the coefficient of friction when sliding speed is high.When the contact pressure increases to 6 MPa, the friction behavior becomes unstable and coefficient of friction increase for PTFE (Figure6(a)). Some researchers have made similar observations40 while others reached opposite results 15. It is possible that an increasing applied load makes the contact interface closer, promoting transfer film formation, which result in more reduction in coefficient of friction.…”

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confidence: 91%

Tribological characteristics and wear mechanisms of reinforced composites against steel

Qian,

Pan,

2023

Journal of Thermoplastic Composite Materials

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To explore the potential wear performance of commonly utilized hydraulic system polymer components, we investigated the effects of different working conditions on the frictional and wear behaviors of three polymer sealing materials in this study. Polytetrafluoroethylene (PTFE), nylon (PA), and polypropylene (PP) composites were prepared using copper (Cu) and glass fiber (GF) as fillers. The tribological and wear properties (wear rate, surface appearance, wear patterns) of the sealing composites were evaluated. The results showed consistent trends toward increasing average coefficient of friction as load and speed increased for PTFE. The incorporation of filler improved the wear resistance of the composites. The addition of Cu particles showed lower coefficient under higher load and sliding speed. PA with continuous glass fiber (CGF) sealing composites showed lower coefficient, less wear volume, and relatively smoother wear zone surfaces than PTFE-based composites or PP with short glass fiber (SGF). We propose that PA-CGF outperforms other materials in regards to fatigue and friction and wear even under extremely high silding speed,as well as sealing properties.

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“…The evolutionary operation, factorial, regression, response surface, and Taguchi methods are the most used for experimental design [34][35][36] . Ibrahim et al 37 presented Taguchi optimization of tribological behaviors of composite materials. They concluded that Taguchi and analysis of variance (ANOVA) techniques are promising for predicting tribological behavior and can then be used to guide the design and implementation of tribological materials.…”

Section: List Of Symbolsmentioning

confidence: 99%

Parametric optimisation for the design of gravity energy storage system using Taguchi method

Elsayed

Abdo

Attia

et al. 2022

Sci Rep

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Gravitational energy storage systems are among the proper methods that can be used with renewable energy. However, these systems are highly affected by their design parameters. This paper presents a novel investigation of different design features of gravity energy storage systems. A theoretical model was developed using MATLAB SIMULINK to simulate the performance of the gravitational energy storage system while changing its design parameters. A parametric optimization study was also conducted using Taguchi and analysis of variance (ANOVA) techniques for optimizing the energy storage rate. Six parameters were studied; three are related to the piston design (diameter, height, and material density). The other parameters are the return pipe diameter, length, and charging/discharging time. Results revealed that the piston diameter and height are the two most significant parameters for the system performance compared to the other parameters, as they contributed by 35.11% and 30.28%, respectively. The optimization results indicated that the optimal piston diameter, height, and return pipe diameter were 0.25, 0.5, and 0.01 of the container height. The outcomes of this paper can significantly improve energy storage and power generation from renewable energy systems as it provides a reliable, economical, sustainable, and durable energy storage system.

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Optimization and prediction of tribological behaviour of filled polytetrafluoroethylene composites using Taguchi Deng and hybrid support vector regression models

Cited by 12 publications

References 56 publications

Prediction of QCE using ANN and ANFIS for milling Alloy 2017A

Prediction of QCE using ANN and ANFIS for milling Alloy 2017A

Tribological characteristics and wear mechanisms of reinforced composites against steel

Parametric optimisation for the design of gravity energy storage system using Taguchi method

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