Least Squares Boosting Ensemble and Quantum-Behaved Particle Swarm Optimization for Predicting the Surface Roughness in Face Milling Process of Aluminum Material

Alajmi, Mahdi S.; Almeshal, Abdullah M.

doi:10.3390/app11052126

Cited by 12 publications

(11 citation statements)

References 37 publications

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“…Each particle represents a potential optimal solution of the search space and the characteristics of particle are displayed by three indexes of position, velocity and fitness value. The particle moves in the solution space [17]. By tracking the trajectories of the particle, it updates the position and speed of individual constantly to follow the optimal output.…”

Section: ) Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Application of Optimized SVM in Sample Classification

Yao

2022

IJACSA

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Support vector machines (SVM) have unique advantages in solving problems with small samples, nonlinearity and high dimension. It has a relatively complete theory and has been widely used in various fields. The classification accuracy and generalization ability of SVMs are determined by the selected parameters, for which there is no solid theoretical guidance. To address this parameter optimization problem, we applied random selection, genetic algorithms (GA), particle swarm optimization (PSO) and K-fold cross validation (k-CV) method to optimize the parameters of SVMs. Taking the classification accuracy, mean squared error and squared correlation coefficient as the goal, the K-fold cross validation method is chosen as the best way to optimize SVM parameters. In order to further verify the best performance of the SVM whose parameters are optimized by the K-fold cross validation method, the back propagation neural network and decision tree are used as the contrast models. The experimental results show that the SVM-cross validation method has the highest classification accuracy in SVM parameter selection, which lead to SVM classifiers that outperform both BP neural networks and decision tree method.

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Section: ) Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Application of Optimized SVM in Sample Classification

Yao

2022

IJACSA

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“…When compared to the old technique, this approach is capable of developing superior predictive models. Situations where there are uncertainties in data representation, solution objectives, modelling techniques, or the presence of random initial seeds in a model are all good reasons to use the EML method [10]. The basic learners are the instances or candidate methods.…”

Section: Ensemble Machine Learning (Eml) Algorithmmentioning

confidence: 99%

“…Each base learner operates separately, as in a standard machine learning technique, and the results are eventually integrated to generate a single robust output. For regression and classification methods, the combination could be done using any of the averaging (simple or weighted) methods and voting (majority or weighted) methods [11][12][13].…”

Section: Ensemble Machine Learning (Eml) Algorithmmentioning

confidence: 99%

Fast and accurate Brillouin frequency shift extraction in Brillouin optical time domain reflectometry (BOTDR) distributed fiber sensor by using ensemble machine learning algorithm

Almoosa¹,

Zan²,

Ibrahim³

et al. 2022

J. Phys.: Conf. Ser.

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To improve the Brillouin frequency shift (BFS) resolution measurement and processing time of the differential cross-spectrum Brillouin optical time domain reflectometry (DCS-BOTDR) fiber sensor, our team suggests employing the ensemble machine learning (EML) technique. Because it gave the best BFS resolution compared to the other TL cases, we used the BFS distribution data recorded by the pulse duration TL =14 ns case as ground truth to train the EML model in this work. After that, we tested the EML model for TL =4, 60, and 90 ns cases. We improved the BFS resolution for all TL situations by approximately 2.85 MHz, comparable to our resolution when TL was equal to 14 ns. This result demonstrates that the EML algorithm is reliable, efficient, and highly accurate in its predictive capabilities. Additionally, we have documented a rapid processing time of approximately one second. In addition, we have successfully demonstrated 20 cm spatial resolution measurement for TL =60 and 90 ns, which was not previously possible with the usual DCS-BOTDR signal processing method.

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“…The outcomes demonstrate that KPCA IRBF can lower the RMSE of RVM by over 30% and decrease the confidence interval (CI) width by over 90 percent. Alajmi and Almeshal [10][11][12] proposed different machine learning methods such as ANFIS-PSO, XGBoost-SDA, Gaussian process regression algorithm, and least squares boosting ensemble, and quantum-behaved PSO to solve manufacturing processes (e.g., drilling, turning, and milling).…”

Section: Introductionmentioning

confidence: 99%

Estimation and Optimization of Tool Wear in Conventional Turning of 709M40 Alloy Steel Using Support Vector Machine (SVM) with Bayesian Optimization

Alajmi

Almeshal

2021

Materials

Self Cite

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Cutting tool wear reduces the quality of the product in production processes. The optimization of both the machining parameters and tool life reliability is an increasing research trend to save manufacturing resources. In the present work, we introduced a computational approach in estimating the tool wear in the turning process using artificial intelligence. Support vector machines (SVM) for regression with Bayesian optimization is used to determine the tool wear based on various machining parameters. A coated insert carbide tool 2025 was utilized in turning tests of 709M40 alloy steel. Experimental data were collected for three machining parameters like feed rate, depth of cut, and cutting speed, while the parameter of tool wear was calculated with a scanning electron microscope (SEM). The SVM model was trained on 162 experimental data points and the trained model was then used to estimate the experimental testing data points to determine the model performance. The proposed SVM model with Bayesian optimization achieved a superior accuracy in estimation of the tool wear with a mean absolute percentage error (MAPE) of 6.13% and root mean square error (RMSE) of 2.29%. The results suggest the feasibility of adopting artificial intelligence methods in estimating the machining parameters to reduce the time and costs of manufacturing processes and contribute toward greater sustainability.

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Least Squares Boosting Ensemble and Quantum-Behaved Particle Swarm Optimization for Predicting the Surface Roughness in Face Milling Process of Aluminum Material

Cited by 12 publications

References 37 publications

Application of Optimized SVM in Sample Classification

Application of Optimized SVM in Sample Classification

Fast and accurate Brillouin frequency shift extraction in Brillouin optical time domain reflectometry (BOTDR) distributed fiber sensor by using ensemble machine learning algorithm

Estimation and Optimization of Tool Wear in Conventional Turning of 709M40 Alloy Steel Using Support Vector Machine (SVM) with Bayesian Optimization

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