Tool wear, 3D surface topography, and comparative analysis of GRA, MOORA, DEAR, and WASPAS optimization techniques in turning of cold work tool steel

Safi, Khaoula; Yallese, Mohamed Athmane; Belhadi, Salim; Mabrouki, Tarek; Laouissi, Aissa

doi:10.1007/s00170-022-09326-6

Cited by 10 publications

(6 citation statements)

References 53 publications

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“…The higher ( f ), the deeper and wider these grooves. A similar explanation is proposed by Safi et al 49 On the other hand, ( Ra ) decreases with the increase of ( Vc ), and it also increases slightly with the increase of ( ap ). Also, ( Ra ) varies according to the nature of the cutting material grades.…”

Section: Resultssupporting

confidence: 84%

Experimental investigation on the performance of ceramics and CBN cutting materials during dry machining of cast iron: Modeling and optimization study using RSM, ANN, and GA

Gasmi,

Yallese,

Boucherit

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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This study focuses on the performance evaluation of CBN and ceramic tools in dry machining of gray cast iron EN GJL-350. The machining factors taken into account during turning are: cutting speed ( Vc), feed rate ( f), depth of cut ( ap), and cutting tool material (CBN, white ceramic, mixed ceramic, and silicon nitride). The first part of this investigation concerns the evaluation of the four cutting materials performance used in terms of tool wear evolutions, 2D and 3D surface roughness and cutting forces variation according to working parameters. The second part exposes the results according to L32 Taguchi design of experiment. Statistical treatment by ANOVA allowed to quantify the impact of the input factors on the performance parameters, namely the surface roughness ( Ra), the cutting force ( Fz), the cutting power ( Pc), and the specific cutting energy ( Ecs). The response surface methodology (RSM), and the artificial neural network (ANN) approach were adopted to develop mathematical models for predicting the different output parameters. The results of the two methods were compared and discussed. A multi-criteria optimization was performed using the desirability function (DF) approach. The genetic algorithm (GA) was also applied to find pareto fronts. The results found show that CBN is the most efficient material in terms of lower tool wear, surface roughness and cutting forces. The DF method allowed to find an optimal combination ( Vc = 660 m/min, f = 0.13 mm/rev, ap = 0.232 mm, and the CBN material) leading to a compromise between the minimization of ( Ra, Fz, Pc, and Ecs) and the maximization of (MRR). The Pareto fronts found by the (GA) method make it possible to propose a multitude of solutions according to the desired objectives.

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Section: Resultssupporting

confidence: 84%

Experimental investigation on the performance of ceramics and CBN cutting materials during dry machining of cast iron: Modeling and optimization study using RSM, ANN, and GA

Gasmi,

Yallese,

Boucherit

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…The ANOVA of (VB) shows that the factors (Vc) and (f) have the greatest influence on (VB) with cont% of 66.44% and 25.95%, respectively, because, in addition to the increase in temperature in the cutting zone, the increase in friction promotes wear by abrasion, as shown in Figure 3 by the streaks on the strip of flank wear, while the factors (r) and (ap) are insignificant with P-values of 0.565 and 0.083. These results are in perfect agreement with those of [22]. 3).…”

Section: Anova For (Ra T° and Vb)supporting

confidence: 92%

Minimizing Tool Wear, Cutting Temperature and Surface Roughness in the Intermittent Turning of AISI D3 Steel Using the DF and GRA Method

Khelfaoui¹,

Yallese²,

Boucherit³

et al. 2023

Tribol. Ind.

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Intermittent turning (IT) is characterized by a different context than continuous turning (CT). The cutting tool is shocked each time it goes off-load and engages a new surface. This interruption causes severe cutting conditions, which fatally affect the performance parameters. The purpose of this study is to assess the effects of four cutting factors, tool nose radius (r), cutting speed (Vc), feed rate (f), and depth of cut (ap), on the following output performance parameters: surface roughness (Ra), cutting temperature (T°), and cutting tool wear (VB) during turning (IT) AISI D3 cold work tool steel. A triple CVD (AI2O3/TiC/TiCN)-coated carbide cutting tool was used. A Taguchi L9 (3^4) experimental design was adopted for carrying out the experiments in intermittent turning. To improve the performance parameters based on three (3) highly particular scenarios that fulfill industrial criteria, the desirability function (DF) and the grey relational analysis method (GRA) were used. Finally, the optimization findings of the two strategies were compared in order to evaluate the performance of each method.

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“…Safi et al. used a self-developed tool dynanometer for measuring high frequency cutting force to evaluate the cutting force in micro milling 6061-T6 aluminum alloy (Bataineh and Dalalah, 2010), with focus on the cutting force signal and micro cutting force variations under different cutting speeds (Huang et al., 2020; Safi et al., 2022). Wang et al.…”

Section: Introductionmentioning

confidence: 99%

“…They analyzed the effect of cutting parameters on cutting force, chip geometry, surface roughness, and mean friction coefficient in orthogonal micro and nano-scale cutting of 7075-T6 aluminum alloy (Santos et al, 2015;, and compared with trends observed in conventional macro-scale cutting. Safi et al used a self-developed tool dynanometer for measuring high frequency cutting force to evaluate the cutting force in micro milling 6061-T6 aluminum alloy (Bataineh and Dalalah, 2010), with focus on the cutting force signal and micro cutting force variations under different cutting speeds (Huang et al, 2020;Safi et al, 2022). Wang et al (2021) used a selfdeveloped ceramic micro milling cutter to examine the micro machinability of 2024 aluminum alloy, considering the machining channel burrs, dimensional accuracy, tool wear, and chip geometry under different cutting parameters (Kuramoto et al, 2014;Zargar et al, 2012;.…”

mentioning

confidence: 99%

Effect of cutting parameters on the microstructure evolution and damage mechanism of 7075-T6 aluminum alloy in micro cutting

Zhang

Lin

Liu

et al. 2023

International Journal of Damage Mechanics

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This work aims to explore how cutting parameters affect the microstructure evolution and damage mechanism of 7075-T6 aluminum alloy in micro cutting. The effect of cutting parameters on micro cutting force and surface morphology is examined through single-factor test. By building a 3D micro finite element model for micro cutting based on crystal plasticity theory, the effect of cutting parameters on residual stress, microstructure evolution and damage behavior is analyzed to establish a mapping relation between residual stress and damage. The results show that as cutting speed increases, main cutting force first reduces then increases in all cases, but the cutting speed at the inflection point corresponding to main cutting force is different. The micro cutting surface morphology of 7075-T6 aluminum alloy displays obvious signs of plowing; detectable oxidation adhesion wear appears when the cutting depth is greater than 150 μm. Crack initiation and propagation on the machined surface of 7075-T6 aluminum alloy vary considerably under different cutting parameters. Residual stress distribution displays a ladle profile. The deeper the maximum residual compressive stress is from the surface, the harder it is for micro cracks to initiate and propagate. SEM and EDS analysis indicates that at smaller cutting depths, micro cutting tool wear is dominated by oxidation wear; at larger cutting depths, surface morphology is mostly better than at smaller cutting depths.

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Tool wear, 3D surface topography, and comparative analysis of GRA, MOORA, DEAR, and WASPAS optimization techniques in turning of cold work tool steel

Cited by 10 publications

References 53 publications

Experimental investigation on the performance of ceramics and CBN cutting materials during dry machining of cast iron: Modeling and optimization study using RSM, ANN, and GA

Experimental investigation on the performance of ceramics and CBN cutting materials during dry machining of cast iron: Modeling and optimization study using RSM, ANN, and GA

Minimizing Tool Wear, Cutting Temperature and Surface Roughness in the Intermittent Turning of AISI D3 Steel Using the DF and GRA Method

Effect of cutting parameters on the microstructure evolution and damage mechanism of 7075-T6 aluminum alloy in micro cutting

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