Energy Optimization for Milling 304L Steel using Artificial Intelligence Methods

Bousnina, Kamel; Hamza, Asma; Yahia, Noureddine Ben

doi:10.15282/ijame.19.3.2022.05.0765

Cited by 11 publications

(2 citation statements)

References 16 publications

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“…70% of the data is devoted to the learning phase, 15% to the testing phase, and finally 15% to validation. 20 Table 6 shows the values of MSE and R 2 for the different algorithms and the different architectures. After several tests, the optimal architecture in this study for a single output is {3-10-1} for the output variable E tot and {3-14-1} for the variables C tot and Ra.…”

Section: Second Casementioning

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: Second Casementioning

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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“…The success of an ANN depends on how well the fundamental parameters of the network (weights and bias) are optimized. However, conventional learning algorithms are often limited when it comes to solving complex problems (Bousnina et al, 2022; Rebouh et al, 2017). To overcome these limitations, a hybrid PSO-ANN model was developed to optimize the network architecture and minimize errors by determining the best values for weights and biases (Nguyen et al, 2020; Shariati et al, 2019).…”

Section: Prediction With the Rsmmentioning

confidence: 99%

A combination of the particle swarm optimization-artificial neurons network algorithm and response surface method to optimize energy consumption and cost during milling of the 2017A alloy

Bousnina,

Hamza,

Yahia

2023

Energy Exploration & Exploitation

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This research aims to predict the cost and energy consumption associated with pocket and groove machining using the hybrid particle swarm optimization-artificial neurons network (PSO-ANN) algorithm and the response surface method (RSM). A parametric study was conducted to determine the best predictions by adjusting the swarm population size (pop) and the number of neurons (n) in the hidden layer. The results showed that machining strategies and sequences can have a significant impact on energy consumption, reaching a difference of 99.25% between the minimum and maximum values. The cost ( Ctot) and energy consumption ( Etot) values with the PSO-ANN algorithm increased significantly by 99.99% and 92.41%, respectively, compared to the RSM model. The minimum mean square error values for Etot and Ctot with the PSO-ANN models are 3.0499 × 10−5 and 4.6296 × 10−10, respectively. This study highlights the potential of the hybrid PSO-ANN algorithm for multi-criteria prediction and highlights the potential for improved machining of 2017A alloy.

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Intelligent Diagnostic Study Using Machine Learning of Mechanical Faults in Rotating Machinery

Hamza,

Yahia

2024

Lecture Notes in Mechanical Engineering

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Energy Optimization for Milling 304L Steel using Artificial Intelligence Methods

Cited by 11 publications

References 16 publications

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

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

A combination of the particle swarm optimization-artificial neurons network algorithm and response surface method to optimize energy consumption and cost during milling of the 2017A alloy

Intelligent Diagnostic Study Using Machine Learning of Mechanical Faults in Rotating Machinery

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