Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network

Alharthi, Nabeel H.; Bingöl, Sedat; Abbas, Adel T.; Ragab, Adham E.; El‐Danaf, Ehab A.; Alharbi, Hamad F.

doi:10.1155/2017/7560468

Cited by 19 publications

(23 citation statements)

References 13 publications

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“…While Bajić et al [20] examined the influence of three cutting parameters (cutting speed, feed per tooth and depth of cut) on surface roughness, tool wear and cutting force components in a face milling as part of the off-line process control. Alharthi et al [21] developed ANN and regression analysis models for the prediction of surface roughness in a face milling of an AZ61 magnesium alloy workpiece, for different spindle speed [rpm], depth of cut [mm], and table feed [mm/ min]. The coefficient of determination was found to be sufficiently accurate for the best neural network and regression analysis model from the comparison of the models with thirteen experimental validation tests.…”

Section: Methodsmentioning

confidence: 99%

Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies

2018

SV-JME

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Many developed approaches for the improvement of sustainability during machining operations; one of which is the optimized utilization of cutting tools. Increasing the efficient use of cutting tool results in better product quality and longer tool life. Drilling is one of the most popular manufacturing processes in the metal-cutting industry. It is usually carried out at the final steps of the production process. In this study, the effects of cutting parameters (cutting velocity, feed rate) and tool diameter on thrust force (Fz) and torque (Mz) are investigated in the drilling of an Al7075 workpiece using solid carbide tools. The full factorial experimental design is implemented in order to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and response surface methodology (RSM) approaches are used to acquire mathematical models for both the thrust force (Fz) and torque (Mz) related to the drilling process. RSM-and ANN-based models are compared, and it is clearly determined that the proposed models are capable of predicting the thrust force (Fz) and torque (Mz). Nevertheless, the ANN models estimate in a more accurate way the outputs used in comparison to the RSM models.

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Section: Methodsmentioning

confidence: 99%

Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies

2018

SV-JME

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“…Milling of magnesium alloy AZ61 with coated carbide end mill [13] at the rotational speed range of 500÷2000 rev./min (i.e. 400 m/min) produced the surface characterised by Ra surface roughness in the range of approx.…”

Section: State Of Knowledgementioning

confidence: 99%

Application of neural network in determination of parameters for milling AZ91HP magnesium alloy with surface roughness constraint

Kulisz

Zagórski

2019

MATEC Web Conf.

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This paper presents the model for milling AZ91HP magnesium alloy with TiAlN coated carbide end mill. The model was developed on the basis of experimental data from the neural network training data set. The milling process was conducted at constant parameters of tool geometry, workpiece strength properties, technological machine properties, radial and axial depth of cut. The range of changeable machining parameters specified in this study included cutting speed, feed per tooth, and the output variable: the arithmetical mean roughness parameter (Ra). The process was modelled by means of MatLab software and its Neural Network Toolbox. The developed model was implemented in the algorithm designed to determine optimal milling conditions, exploring the space of acceptable parameters in search of those which would meet the specified roughness parameter at maximum efficiency.

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“…The motivations of this work are a project that optimizes the design parameters in the bending hinge DAR of the bridge-matching mechanism using gray relational analysis based on the Taguchi method [31][32][33][34][35], FEM in ANSYS, and artificial neural networks [36][37][38][39][40][41]. A gap is often present in many kinds of classical joints, leading to friction and vibration, causing the wear of the joints.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design for Compliant Mechanism Flexure Hinges: Bridge-Type

et al. 2021

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Compliant mechanisms’ design aims to create a larger workspace and simple structural shapes because these mechanical systems usually have small dimensions, reduced friction, and less bending. From that request, we designed optimal bridge-type compliant mechanism flexure hinges with a high magnification ratio, low stress by using a flexure joint, and especially no friction and no bending. This joint was designed with optimal dimensions for the studied mechanism by using the method of grey relational analysis (GRA), which is based on the Taguchi method (TM), and finite element analysis (FEA). Grey relational grade (GRG) has been estimated by an artificial neural network (ANN). The optimal values were in good agreement with the predicted value of the Taguchi method and regression analysis. The finite element analysis, signal-to-noise analysis, surface plot, and analysis of variance demonstrated that the design dimensions significantly affected the equivalent stress and displacement. The optimal values of displacement were also verified by the experiment. The outcomes were in good agreement with a deviation lower than 6%. Specifically, the displacement amplification ratio was obtained as 65.36 times compared with initial design.

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Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network

Cited by 19 publications

References 13 publications

Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies

Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies

Application of neural network in determination of parameters for milling AZ91HP magnesium alloy with surface roughness constraint

Optimal Design for Compliant Mechanism Flexure Hinges: Bridge-Type

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