Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm

Alajmi, Mahdi S.; Almeshal, Abdullah M.

doi:10.3390/app11094055

Cited by 22 publications

(6 citation statements)

References 25 publications

(29 reference statements)

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“…With the increased DOC, for the same cutting speed, higher cutting forces were measured for all the samples. It is true that the increased DOC increases the cutting forces in turning operation [34]. It is also an important observation that the variations within the cutting forces were higher at all the cutting parameters in all the samples as reflected from the mean values (table 3).…”

Section: Resultsmentioning

confidence: 82%

Machining behaviour of AZ91E hybrid composite reinforced with granite and fly ash powders

Singaiah¹,

Babu²,

Singaiah³

et al. 2022

Eng. Res. Express

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In the present study, hybrid composites of AZ91E reinforced with granite powder and fly ash powder were fabricated by stir casting route. The reinforcing material was added in different weight fractions (2.5 and 5 % each). The densities of the composites were measured between 1.74−1.8 g/cm3. The role of different weight percentages of reinforcements on the machining behavior was investigated. Increased microhardness was measured for the composites due to the presence of the reinforcing material. Higher cutting forces were measured for the composites containing higher fraction of reinforcements. With the increased cutting speed (420 to 715 rpm) the cutting forces were observed as decreased and with the increased depth of cut (0.2 and 0.4 mm), higher cutting forces were recorded. At higher fraction of reinforcements, discontinuous chips were observed for the composites. The results demonstrate the significant role of granite powder and the fly ash powder on enhancing the mechanical properties (up to 66% compared with the base alloy) but also on increasing the difficulty level in machining.

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Section: Resultsmentioning

confidence: 82%

Machining behaviour of AZ91E hybrid composite reinforced with granite and fly ash powders

Singaiah¹,

Babu²,

Singaiah³

et al. 2022

Eng. Res. Express

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“…Gaussian process regression is a powerful machine learning technique based on the Bayesian theory [35]. This approach is particularly suitable for addressing small sample sizes, complex nonlinear problems, and high-dimensional data [36,37]. Unlike linear regression methods that rely on deterministic variables alone, GPR leverages a set of random variables with joint Gaussian distributions characterized by mean and covariance functions.…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

“…where i is a Gaussian noise with zero mean and variance σ 2 n and f (x i ) is the learning function. The output vector y i can be presented with a Gaussian distribution as follows [37]:…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Integration of Multiple Bayesian Optimized Machine Learning Techniques and Conventional Well Logs for Accurate Prediction of Porosity in Carbonate Reservoirs

Alatefi

Azim²,

Alkouh

et al. 2023

Processes

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The accurate estimation of reservoir porosity plays a vital role in estimating the amount of hydrocarbon reserves and evaluating the economic potential of a reservoir. It also aids decision making during the exploration and development phases of oil and gas fields. This study evaluates the integration of artificial intelligence techniques, conventional well logs, and core analysis for the accurate prediction of porosity in carbonate reservoirs. In general, carbonate reservoirs are characterized by their complex pore systems, with the wide spatial variation and highly nonlinear nature of their petrophysical properties. Therefore, they require detailed well-log interpretations to accurately estimate their properties, making them good candidates for the application of machine learning techniques. Accordingly, a large database of (2100) well-log records and core-porosity measurements were integrated with four state-of-the-art machine learning techniques (multilayer perceptron artificial neural network, MLP-ANN; Gaussian process regression, GPR; least squares gradient boosting ensemble, LS-Boost; and radial basis function neural network, RBF-NN) for the prediction of reservoir porosity. The well-log data used in this study include sonic acoustic travel time, Gamma-ray, and bulk density log records, which were carefully collected from five wells in a carbonate reservoir. This study revealed that all the artificial intelligence models achieved high accuracy, with R-squared values exceeding 90% during both the training and blind-testing phases. Among the AI models examined, the GPR model outperformed the others in terms of the R-squared values, root-mean-square error (RMSE), and coefficient of variation of the root-mean-square error (CVRMSE). Furthermore, this study introduces an artificially intelligent AI-based correlation for the estimation of reservoir porosity from well-log data; this correlation was developed using an in-house, Fortran-coded MLP-ANN model presented herein. This AI-based correlation gave a promising level of accuracy, with R-squared values of 92% and 90% for the training and blind-testing datasets, respectively. This correlation can serve as an accurate and easy-to-use tool for porosity prediction without any prior experience in utilizing or implementing machine learning models.

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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

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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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Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm

Cited by 22 publications

References 25 publications

Machining behaviour of AZ91E hybrid composite reinforced with granite and fly ash powders

Machining behaviour of AZ91E hybrid composite reinforced with granite and fly ash powders

Integration of Multiple Bayesian Optimized Machine Learning Techniques and Conventional Well Logs for Accurate Prediction of Porosity in Carbonate Reservoirs

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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