Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling

Xia, Jian Sheng; Khabaz, Mohamad Khaje; Patra, Indrajit; Khalid, Imran; Álvarez, José R.; Rahmanian, Alireza; Eftekhari, S. Ali; Toghraie, Davood

doi:10.1016/j.isatra.2022.06.009

Cited by 36 publications

(8 citation statements)

References 46 publications

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“…The algorithm focuses mainly on speeding up the optimization process by reducing the number of function evaluations to reach the local minimum. In addition, the algorithm keeps the moving average of the squared gradients for each weight and divides the gradient by the square root of the mean square, equations (1) , (2) , (3) ) [ 22 , 23 ],

…”

Section: Methodsmentioning

confidence: 99%

Machine learning regression algorithms to predict emissions from steam boilers

Ross-Veitía,

Palma-Ramírez,

Arias-Gilart

et al. 2024

Heliyon

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…”

Section: Methodsmentioning

confidence: 99%

Machine learning regression algorithms to predict emissions from steam boilers

Ross-Veitía,

Palma-Ramírez,

Arias-Gilart

et al. 2024

Heliyon

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“…Although many advancements in these methods have been made and their accuracy has been improved over the two decades, they have some fundamental drawbacks, like high computational costs. As an alternative, using intelligent techniques, like Artificial intelligence (AI) and machine learning (ML) methods, for the estimation of parameters that are useful in engineering designs, has gained popularity in recent years [20][21][22][23][24][25][26][27][28][29][30][31]. Naturally, MLbased models offer several pros over CFD simulations and traditional mathematical fitting methods, particularly in terms of accuracy, generalizability, rapid response, and retraining ability.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Alipour Bonab,

Yazdani-Asrami

2024

Nano Ex.

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The rising demand for advanced energy systems requires enhanced thermal management strategies to maximize resource utilization and productivity. This study intends to address the critical need for efficient heat transfer mechanisms in industrial energy systems, particularly those relying on pool boiling conditions, by mainly focusing on Critical Heat Flux (CHF). In fact, CHF keeps a limit in thermal system design, beyond which the efficiency of the system drops. Recent research materials have highlighted nanofluids' superior heat transfer properties over conventional pure fluids, like water, which makes them a considerable substitution for improving CHF in cooling systems. However, the broad variability in experimental outcomes challenges the development of a unified predictive model. Besides, Machine Learning (ML) based prediction has shown great accuracy for modelling of the designing parameters, including CHF. Utilizing ML algorithms—Cascade Forward Neural Network (CFNN), Extreme Gradient Boosting (XGBoost), Extra Tree, and Light Gradient Boosting Method (LightGBM)— four predictive models have been developed and the benchmark shows CFNN's superior accuracy with an average goodness of fit of 89.32%, significantly higher than any available model in the literature. Also, the iterative stability analysis demonstrated that this model with a 0.0348 standard deviation and 0.0268 mean absolute deviation is the most stable and robust method that its performance minorly changes with input data. The novelty of the work mainly lies in the prediction of CHF with these advanced algorithms models to enhance the reliability and accuracy of CHF prediction for designing purposes, which are capable to consider many effective parameters into account with very higher accuracy than mathematical fittings. This study not only explains the complex interplay of nanofluid parameters affecting CHF, but also offers practical implications for the design of more efficient thermal management systems, thereby contributing to the broader field of energy system enhancement through innovative cooling solutions.

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“…Soltani et al [ 12 ] utilized an ANN to determine the thermal conductivity of a tungsten oxide–MWCNTs/hybrid engine oil. Xia et al [ 13 ] applied a feed‐forward perceptron ANN model to predict the rolling force, power, and slip of tandem cold‐rolling. These studies demonstrate the potential of ANNs in accurately predicting parameters in different materials and processes.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Multi‐Segment Work Roll Profile for 1340 mm HC Tandem Cold‐Rolling Mills Based on DBSCAN and NSGA‐II Algorithms

Chen

Liang-gui

Pan

et al. 2023

steel research int.

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To address issues with saturated work roll bending force (WRB), maximum contact pressure between the work roll and intermediate roll (QWI), and challenging flatness lifting in a four‐stand, six‐high tandem cold mill with a 1340 mm width, the density‐based spatial clustering of applications with noise algorithm and the non‐dominated sorting genetic algorithm II are used for optimal design and online application research of a five‐segment work roll profile. When compared to a flat roll and a parabolic roll profile with a maximum crown of 15 μm, the optimized roll profile decreases the secondary crown of the roll gap (CW2) by 5.99 and 1.055 μm, respectively, and the fourth crown of the roll gap (CW4) by 0.21 and 0.39 μm, respectively. Additionally, QWI in the field of roll gap crown adjustment decreases by 1.04 and 0.445 kN mm−1. Industrial testing shows that the average integrated value of flatness in rolling the four extreme specifications of 1340 mm tandem cold‐rolling mills lowers by 2.65 IU following the application of the new roll profile, and the average reduction of WRB in the last stand is 2.215 MPa.

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Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling

Cited by 36 publications

References 46 publications

Machine learning regression algorithms to predict emissions from steam boilers

Machine learning regression algorithms to predict emissions from steam boilers

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Optimization of Multi‐Segment Work Roll Profile for 1340 mm HC Tandem Cold‐Rolling Mills Based on DBSCAN and NSGA‐II Algorithms

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