Modelling and analysis of tool wear and surface roughness in hard turning of AISI D2 steel using response surface methodology

Mir, M. Junaid; Wàňi, M.F.

doi:10.5267/j.ijiec.2017.4.004

Cited by 12 publications

(5 citation statements)

References 16 publications

(17 reference statements)

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“…The results show that workpiece hardness has a significant effect on flank wear. Junaid et al (2018) analyzed a series of machinability studies focusing on tool wear and surface roughness in the finishing hard turning of AISI D2 steel utilizing PCBN, mixed ceramic, and coated carbide inserts. The findings indicate that tool wear was primarily influenced by cutting time and, to a lesser extent, by the hardness of the cutting tool.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the cutting tool wear during dry hard turning of AISI D2 steel by using models based on Learning process and GA polyfit

Djellouli,

Haddouche,

Belarbi

et al. 2023

J. Eng. Exact Sci.

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In this paper, we are interested in the prediction of flank wear through dry hard turning of AISI D2 steel with a mixed alumina insert. In the machining process, the cutting tool is principally affected by two kinds of wear: flank and crater wear. The latter are criteria for cessation of the tool function. In the absence of a real-time wear sensor, it is necessary to know or track wear with the view to prevent tool damage. For this purpose, the current research focuses on the development of predictive models of flank wear based on Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Polynomial Fit using Genetic Algorithm (GAPOLYFITN). The simulation process involves considering input variables including feed (f), cutting speed (Vc), and cutting time (tc); the output is the flank wear (VB). To assess the statistical efficacy of the predictive models, some performance indicators were employed, including the R-squared statistic-R2, Mean Square Error-MSE, Mean Absolute Error-MAE, and Mean Absolute Percentage Error-MAPE. The results, for the present case study, show that the R-squared statistic ranges from 0.85 to 0.99, the MSE is between 0.000046 and 0.000177, the MAE ranges from 0.002958 to 0.009336, and the value of MAPE varies from 3.50 to 9.60%. The predictive capability of GPR and GAPOLYFITN in determining flank wear are the best, as they exhibit high (R2), and lower values of MSE, MAE, and MAPE. The powerful predictive model of flank wear is the GPR because it provides R2 = 0.96, MSE = 4.6e-5, MAE = 0.002958, and MAPE = 3.50%.

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Section: Introductionmentioning

confidence: 99%

Prediction of the cutting tool wear during dry hard turning of AISI D2 steel by using models based on Learning process and GA polyfit

Djellouli,

Haddouche,

Belarbi

et al. 2023

J. Eng. Exact Sci.

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“…This approach can be applied for specific cases in which only several factors were considered in the investigation of their influence on the tool wear and surface roughness [7][8][9]. Therefore, many studies focused on experimental modeling methods with a limited amount of input parameters [15][16][17][18][19]. In the experimental modelling method, many approaches were applied to model the tool wear and surface roughness.…”

Section: Introductionmentioning

confidence: 99%

“…The response surface method was applied to analyze the effect of the technological parameters on the tool wear and surface roughness. Besides, using this method, the tool wear and surface roughness were also modeled [15][16][17][18][19]. Taguchi method was used to design the experimental matrix, to investigate the influence of technological parameters and cutting conditions on the tool wear and surface roughness.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of tool wear and surface roughness in high-speed milling process of aluminum alloy Al6061

et al. 2021

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In this study, the influence of cutting parameters and machining time on the tool wear and surface roughness was investigated in high-speed milling process of Al6061 using face carbide inserts. Taguchi experimental matrix (L9) was chosen to design and conduct the experimental research with three input parameters (feed rate, cutting speed, and axial depth of cut). Tool wear (VB) and surface roughness (Ra) after different machining strokes (after 10, 30, and 50 machining strokes) were selected as the output parameters. In almost cases of high-speed face milling process, the most significant factor that influenced on the tool wear was cutting speed (84.94 % after 10 machining strokes, 52.13 % after 30 machining strokes, and 68.58 % after 50 machining strokes), and the most significant factors that influenced on the surface roughness were depth of cut and feed rate (70.54 % after 10 machining strokes, 43.28 % after 30 machining strokes, and 30.97 % after 50 machining strokes for depth of cut. And 22.01 % after 10 machining strokes, 44.39 % after 30 machining strokes, and 66.58 % after 50 machining strokes for feed rate). Linear regression was the most suitable regression of VB and Ra with the determination coefficients (R2) from 88.00 % to 91.99 % for VB, and from 90.24 % to 96.84 % for Ra. These regression models were successfully verified by comparison between predicted and measured results of VB and Ra. Besides, the relationship of VB, Ra, and different machining strokes was also investigated and evaluated. Tool wear, surface roughness models, and their relationship that were found in this study can be used to improve the surface quality and reduce the tool wear in the high-speed face milling of aluminum alloy Al6061

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“…From the results it was revealed that the cutting time altered the tool wear. On the other hand, cutting time was the principal factor influencing the surface roughness of the workpiece succeeded by cutting speed [10]. Deshpande and Pant has conducted the experiments on EN8 alloy steel material with the PVD coated TiAlN insert and plain carbide insert by Taguchi method.…”

Section: Introductionmentioning

confidence: 99%

Comparative study on cutting performance of plain and coated carbide inserts in CNC turning of EN9 steel

Chauhan

Kumar

2020

Eng. Res. Express

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The present work deals with comparative study on cutting performance of plain carbide and coated carbide inserts in turning of EN9 steel. The design of experiments (DOE) was done using response surface methodology (RSM) technique. The cutting parameters were spindle speed, feed and depth of cut (DOC). The combined interactions of cutting parameters on surface roughness, flank wear and crater wear were analyzed. The crater and flank wear were observed using machine vision system. This work is concentrated on building up a mathematical relation between cutting parameters and output responses. Analysis of variance (ANOVA) was used for checking the adequacy of the model. The relation between input factors and the response parameters are determined using a cubic regression model. The optimization was done using desirability ramp plots. The main motive behind this research is to compare the performance of plain and coated carbide tool and analyzing which tool performs better and to optimize the cutting parameters for high quality and cost reduction. It was concluded from this research that at optimized speed, feed and DOC the coated carbide tool is suitable for turning of EN9 steel. The tool wear observed on coated carbide tool was less than plain carbide tool. The coated carbide tool showed a slight improvement in results as compared to the plain carbide tool.

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Modelling and analysis of tool wear and surface roughness in hard turning of AISI D2 steel using response surface methodology

Cited by 12 publications

References 16 publications

Prediction of the cutting tool wear during dry hard turning of AISI D2 steel by using models based on Learning process and GA polyfit

Prediction of the cutting tool wear during dry hard turning of AISI D2 steel by using models based on Learning process and GA polyfit

Analysis of tool wear and surface roughness in high-speed milling process of aluminum alloy Al6061

Comparative study on cutting performance of plain and coated carbide inserts in CNC turning of EN9 steel

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