PTFE in Wet and Dry Drilling: Two-Tier Modeling and Optimization through ANFIS

Elango, Sangeetha; Kaviarasan, Varadaraju; Natarajan, Elango; Morris, Ezra; Durairaj, R.; Mariappan, P.

doi:10.1155/2022/4812470

Cited by 5 publications

(7 citation statements)

References 26 publications

(22 reference statements)

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“…The output of the hidden layer at t is expressed in Equation ( 1) [32]: 6) for (every vulture (P i )) do // P i denotes the current vector location of the vulture (7) choose R(i) as the best vulture by using the below equation ( 8)…”

Section: Mernn Techniquementioning

confidence: 99%

“…In this work, the ECG stress signals based on effective FS and classification are done using African vulture optimization (AVO) and optimized modified Elman recurrent neural network (MERNN) methods. The proposed work contributed to several tasks: feature extraction, FS, and classification [7]. The feature extraction has extracted 13 features in a time domain, and the AVO technique is used to select the optimal best feature for classification.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Human Stress Using Optimized Feature Selection and Classification in ECG Signals

Vellaisamy

et al. 2023

Mathematical Problems in Engineering

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An autonomic nervous system (ANS) of humans is majorly affected by psychological stress. The changes in ANS may cause several chronic diseases in humans. The electrocardiogram (ECG) signal is used to observe the variation in ANS. Numerous techniques are presented for an ECG stress signal handling feature extraction and classification. This work managed a heart rate variability feature acquired from smaller peak waveforms such as P, Q, S, and T waves. Also, the R peak is detected, which is a significant part of the ECG waveform. In this work, the proposed stress classification work has been categorized into two main processes: feature selection (FS) and classification. The main aim of the proposed work is to propose an optimized FS and classifier model for the detection of stress in ECG signals. The Metaheuristics model of the African vulture optimization (AVO) technique is presented to perform an FS. This selection is made to choose the required features and minimize the data for classification. The AVO-based modified Elman recurrent neural network (MERNN) technique is proposed to perform an efficient classification. The AVO is used for fine-tuning the weight of the MERNN technique. The experimental result of this technique is evaluated in terms of Recall (91.56%), Accuracy (92.43%), Precision (92.78%), and F1 score (95.86%). Thus, the proposed performance achieved a superior result than the conventional techniques.

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Section: Mernn Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Human Stress Using Optimized Feature Selection and Classification in ECG Signals

Vellaisamy

et al. 2023

Mathematical Problems in Engineering

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“…e reason for using ANFIS in this research is that we wanted to generate as many datasets as possible from a small size of experimental data (27 datasets). Two-tier approach of using ANFIS and RSM was tested and reported as a better method [30]. Optimal searching was more accurate when data population was large.…”

Section: Adaptive Neurofuzzy Inference System (Anfis)mentioning

confidence: 99%

Gorilla Troops Optimizer Combined with ANFIS for Wire Cut EDM of Aluminum Alloy

Natarajan

Kaviarasan²,

Lim

et al. 2022

Advances in Materials Science and Engineering

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Wire cut EDM is a quite regularly used machining process in mechanical and electronic industries. This research has attempted to machine aluminum alloy for which experimental design was prepared using Box-Behnken design. Different combinational options of pulse-on time (P1), pulse-off time (P2), servo wire feed (WF), and current (I) were investigated and surface roughness after machining was observed. Collected 27 datasets were further used in Adaptive Neuro Fuzzy Inference System (ANFIS) to produce about 500 datasets. These 500 datasets are approximated data derived from experimental datasets, known as synthetic data. Data model was further developed and used in Gorilla Troops Optimizer (GTO) to locate the optimum machining parameters. With the excellent three search operators: move towards other gorillas, migrate towards unknown places, and migrate towards known places, GTO has produced the lowest surface roughness value of 0.500953 μm when the machining parameters of pulse-on time, pulse-off time, wire feed, and current values were set as 121 μs, 52 μs, 3 m/min, and 166A, respectively. To ensure the accuracy of the synthetic data-based model and optimality, verification and validation were conducted. Wilcoxon signed rank test was conducted for the pairwise comparison of GTO with each of its competing algorithms at the significance level of σ = 0.05. Friedman test was conducted to calculate the average ranking of each algorithm and to detect the global differences between all compared algorithms. Outperforming performance by GTO algorithm in machining of the selected material is found.

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“…The methodology has been widely used in material engineering, [32][33][34][35] food engineering, [36,37] and engineering applications. [38][39][40][41][42] Luo used RSM to study the effect of extraction temperature, extraction time, and ratio of water to material, as well as their interaction on the yield of total polysaccharide. [43] The results indicated that the interaction effect between extraction temperature and extraction time had interaction effects on the response and there were no interaction effects of extraction time and liquid/ solid ratio.…”

Section: Introductionmentioning

confidence: 99%

Modeling of the corrosion behavior of B30 alloy in a deep‐sea environment by using the response surface methodology

Yin,

Meng,

Zhao

et al. 2023

Materials & Corrosion

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The corrosion behavior of B30 copper‐nickel alloy in a deep‐sea environment was investigated by using the self‐built loop apparatus based on the response surface methodology. The results indicated that all the environmental factors accelerate the corrosion of alloys. The degree of effect of the individual factors decreased in the following order—dissolved oxygen > temperature > flow rate > pressure. Moreover, there exist interaction effects between any two of the environmental factors, of which the one between temperature and pressure is the most significant. A mathematical relationship between these factors and the corrosion behavior for B30 alloy was established. The accuracy of the corrosion model was verified by immersion and electrochemical testing.

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PTFE in Wet and Dry Drilling: Two-Tier Modeling and Optimization through ANFIS

Cited by 5 publications

References 26 publications

Detection of Human Stress Using Optimized Feature Selection and Classification in ECG Signals

Detection of Human Stress Using Optimized Feature Selection and Classification in ECG Signals

Gorilla Troops Optimizer Combined with ANFIS for Wire Cut EDM of Aluminum Alloy

Modeling of the corrosion behavior of B30 alloy in a deep‐sea environment by using the response surface methodology

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