Hydraulic Shovel Digging Phase Simulation and Force Prediction Using Machine Learning Techniques

Azure, Jessica W.A.; Ayawah, Prosper; Kaba, Azupuri G.A.; Kadingdi, Forsyth A.; Frimpong, Samuel

doi:10.1007/s42461-021-00486-9

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Random forest is a machine learning algorithm based on ensemble learning which combines multiple classifiers for solving the problem and enhances the performance of the model [43]. This group learning approach utilizes bootstrap samples from a training dataset for creating forest of decision trees [44]. The decision nodes and leaves explain the decision tree, where leaves represent the final outcome and decision nodes are the points where the data are split.…”

Section: Random Forestmentioning

confidence: 99%

Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid

2022

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Surface roughness is considered as an important measuring parameter in the machining industry that aids in ensuring the quality of the finished product. In turning operations, the tool and workpiece contact develop friction and cause heat generation, which in turn affects the machined surface. The use of cutting fluid in the machining zone helps to minimize the heat generation. In this paper, minimum quantity lubrication is used in turning of AISI 304 steel for determining the surface roughness. The cutting fluid is enriched with alumina nanoparticles of two different average particle sizes of 30 and 40 nm. Among the input parameters chosen for investigation are cutting speed, depth of cut, feed rate, and nanoparticle concentration. The response surface approach is used in the design of the experiment (RSM). For the purpose of estimating the surface roughness and comparing the experimental value to the predicted values, three machine learning-based models, including linear regression (LR), random forest (RF), and support vector machine (SVM), are utilized in addition. For the purpose of evaluating the accuracy of the predicted values, the coefficient of determination (R2), mean absolute percentage error (MAPE), and mean square error (MSE) were all used. Random forest outperformed the other two models in both the particle sizes of 30 and 40 nm, with R-squared of 0.8176 and 0.7231, respectively. Thus, this study provides a novel approach in predicting the surface roughness by varying the particle size in the cutting fluid using machine learning, which can save time and wastage of material and energy.

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Section: Random Forestmentioning

confidence: 99%

Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid

2022

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“…Random forest is a machine learning algorithm based on ensemble learning which combines multiple classifiers for solving the problem and enhance the performance of the model. This group learning approach utilizes bootstrap samples from a training dataset for creating forest of decision trees (Azure et al 2021). The decision nodes and leaves explains the decision tree, where leaves represent the final outcome and decision nodes are the points where the data is split.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Prediction of cutting forces in MQL turning of AISI 304 Steel using machine learning algorithm

Dubey¹,

Sharma²,

Kumar³

et al. 2022

JER is an international, peer-reviewed journal that publishes f

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Cutting force play a significant role in enhancing the machining performance as it affects the cutting tool life, surface finish generated and also the energy consumed in obtaining the final product. The machining cost is reduced considerably by effectively minimizing the cutting forces. Minimum quantity lubrication (MQL) is a technique by which cutting fluid is employed in the machining zone in the form of mist, thereby reducing the wastage of cutting fluid and improving the machinability of the process. In this paper, AISI 304 steel is machined using carbide tool in alumina nanoparticle enriched lubrication environment. The calculation of average cutting force is done by varying the input parameters namely cutting speed, feed rate, depth of cut and nanoparticle concentration respectively. The design of experiment is made using response surface methodology (RSM) and further analysis of variance is performed. Furthermore three machine learning based models namely linear regression (LR), random forest (RF) and support vector machine (SVM) are used for predicting the cutting force and comparing the experimental value with that of the predicted value. For accessing the performance of the predicted values, three different error metrics were used namely, coefficient of determination (R2), mean absolute percentage error (MAPE) and mean square error (MSE) respectively. The predicted values obtained by linear regression model for cutting forces are more accurate as compared to other models.

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Experimental Mechanics in Relation to Mining Excavators

Lagunova,

Makarova,

Pobegailo

2024

Lecture Notes in Mechanical Engineering

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Hydraulic Shovel Digging Phase Simulation and Force Prediction Using Machine Learning Techniques

Cited by 5 publications

References 18 publications

Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid

Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid

Prediction of cutting forces in MQL turning of AISI 304 Steel using machine learning algorithm

Experimental Mechanics in Relation to Mining Excavators

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