Experimental Analysis on the Turning of Aluminum Alloy 7075 Based on Taguchi Method and Artificial Neural Network

Joshy, Arjun; D'souza, R J; Muthirulan, Veerakumar; Sachidananda, Krishnamurthy H.

doi:10.18280/jesa.520501

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Experiments were conducted as per Taguchi's L27 orthogonal array [21,22] and detailed as in Table 4. Spindle speed (s), feed rate (f) and axial depth of cut (d) are the input process parameters and surface roughness (Ra) is the output response.…”

Section: Experiments Detailsmentioning

confidence: 99%

Study and Experimental Investigation of the Effect of Progressive Feed Rate on Surface Roughness in CNC End Milling Process Using RSM

Bommisetty¹,

Chettu²,

Hanumanthappa³

2022

JESA

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The objective of this research work is to investigate the influence of cutting parameters on the average surface roughness (Ra) in an end milling process. Feed rate (f), Spindle speed (s) and Depth of cut (d) are the cutting parameters considered as significant factors. A case study on the progressive feed (PF) and conventional constant feed (CF) machining characteristics of Aluminum alloy BS L168-T6511 using end milling is considered. Taguchi's design of experiments (DoE) technique is applied for various combinations of cutting factors and average surface roughness was measured using Mitutoyo surftest SJ-301 surface roughness tester. The experimental results of Ra are analyzed by response surface methodology (RSM). The predicted values using the developed regression mathematical model are compared against experimental results and were found in close agreement. ANOVA technique was applied to further analyze the data for checking the model adequacy and to predict the influence of each parameter on output response Ra. Main effect plots, Interaction plots, 3D surface plots, and Contour plots are established. The investigation reveals that output response (Ra) is predominantly affected by feed rate and progressive feed machining (PFM) yields better surface finish than the conventional constant feed machining (CFM) for the end milling.

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Section: Experiments Detailsmentioning

confidence: 99%

Study and Experimental Investigation of the Effect of Progressive Feed Rate on Surface Roughness in CNC End Milling Process Using RSM

Bommisetty¹,

Chettu²,

Hanumanthappa³

2022

JESA

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show abstract

“…They concluded that the feed rate was the most important factor followed by cutting speed and approach angle. Joshy et al [16] performed CNC turning operation on the material of aluminum alloy 7075 and the surface roughness was conducted by considering machining parameters like feed rate, depth of cut, and spindle speed. The optimization of the process was carried out utilizing "analysis of variance (ANOVA)".…”

Section: Introductionmentioning

confidence: 99%

Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)

Abdulateef

2021

alkej

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Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 mm/rev and a depth of cut 0.4 mm was found to achieve lower surface roughness with, an increase in the cutting speed and feed rate with the increases of the surface roughness. In addition, an increase in the depth of cut was found to reduces the surface roughness. The outcome of this study showed that ANN is a versatile tool for prediction of surface roughness and may be easily extended with greater confidence to various metal cutting processes.

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Experimental Analysis on the Turning of Aluminum Alloy 7075 Based on Taguchi Method and Artificial Neural Network

Cited by 2 publications

References 22 publications

Study and Experimental Investigation of the Effect of Progressive Feed Rate on Surface Roughness in CNC End Milling Process Using RSM

Study and Experimental Investigation of the Effect of Progressive Feed Rate on Surface Roughness in CNC End Milling Process Using RSM

Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)

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