Optimizing the Turning Operation via Using the Grey Relational Grade

Al-Durgham, Lamees; AlAlaween, Wafa’ H.; Albashabsheh, Nibal T.

doi:10.18178/ijmerr.11.6.452-459

Cited by 2 publications

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“…The factor is significant if it is greater than 0.05 (p > 0.05). If p is less than 0.05, the factor is deemed negligible [17]. The ANOVA findings for the responses (Ra, T°, and VB) are shown in Table .4.…”

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

confidence: 99%

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

confidence: 99%

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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“…The total cost of the experimentation is minimized by conducting only a few numbers of experiments as per orthogonal array matrix and some researchers were implemented with three process parameters [11,12]. Few researchers were conducted turning operations on steels by using multi objective optimization technique and concluded that the cutting speed contributes more on output response when compared with other parameters [13][14][15][16][17]. Rahul Sharma et al [18] revealed the effect of CNC turning parameters with AA6262-T6 aluminium alloy material and concluded that the both feed rate and depth of cut are affecting the material removal rate.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Optimization of Turning Operation Parameters for Aluminium Alloy Using Grey Relational Analysis Method

Nadikudi

2024

MSF

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In high strength aluminium alloy material turning operation, high material removal and good surface are desirable output characteristics. In this investigation, experiments were conducted according to design of experiments concept with a combination of turning input parameters such as cutting speed, feed rate and depth of cut and also studied the effect of process parameters on multi outputs for machining of Al2024 aluminium alloy. Grey relation analysis was applied to know the robust process parameters. The contribution each parameter was realized with analysis of variance statistical method. The cutting speed is the most influencing parameter with contribution of 62.3% followed by depth of cut with 26.4%, and feed rate with 9.9%.

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Optimizing the Turning Operation via Using the Grey Relational Grade

Cited by 2 publications

References 0 publications

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

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

Multi Objective Optimization of Turning Operation Parameters for Aluminium Alloy Using Grey Relational Analysis Method

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