Modeling and optimization of surface roughness and tool vibration in CNC turning of Aluminum alloy using hybrid RSM-WPCA methodology

Sahoo, Priyabrata; Pratap, Ashwani; Bandyopadhyay, Asish

doi:10.5267/j.ijiec.2016.11.003

Cited by 22 publications

(18 citation statements)

References 17 publications

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“…Cutting power and Ra. Sahoo et al (2017) selected response surface methodology (RSM) to design an experiment of DOC, FR and CS on aluminum alloy 63400 for optimizing variation of tool and Ra. Workpieces were machined by using carbide tool in dry environment.…”

Section: Review Of Literaturementioning

confidence: 99%

Optimization of turning parameters of Aluminum 6351 T6 using Taguchi decision making technique

Ahmed¹,

Arora²

2017

10.5267/j.ijdns

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Turning is the utmost elementary process among several CNC machine tool operations which is a versatile, efficient and widely used in industries including mechanical, aerospace and automotive sectors. Most frequently the manufacturing industries face the problem of cutting down the production cost without any oblation in the quality of products. Selection of process parameter is essential for assuring a quality product. Convenient choice of the cutting conditions, parameters and tools for the maximum Material removal rate (MRR) and surface finish needs a sophisticated approach adopting mathematical and statistical models as well as experimental method. Taguchi's method is a very compelling tool to optimize the process variables. The intention of the present study is to determine surface roughness (Ra) and MRR which are key responses to justify the quality of turning operation. Four process parameters have been selected viz. Nose radius (NR), depth of cut (DOC), feed rate (FR) and spindle speed or cutting speed (CS) that influences these responses. During machining operations, the influence of the process parameters and their interaction have been analyzed using a statistical tool ANOVA. Aluminum alloy is widely acceptable by industries owing to its good strength to weight ratio, high resistance to corrosion, malleability and excellent resistance during elevated temperatures.

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Section: Review Of Literaturementioning

confidence: 99%

Optimization of turning parameters of Aluminum 6351 T6 using Taguchi decision making technique

Ahmed¹,

Arora²

2017

10.5267/j.ijdns

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“…Their experimental results showed that increasing cutting speed leads to decreasing surface roughness, and this has been interpreted as a result of the decreasing built-up edge formation tendency which is a consequence of the increase the cutting speed. Sahoo et al [12] carried out statistical modeling using both the weighted principal component analysis and RSM in order to investigate the effect of the process parameters on the surface roughness and tool vibration in CNC turning of aluminum alloy 63400. They concluded that the interaction effects of the spindle speed (Vc) and feed rate (f) were significant for the model in the case of tool vibration.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum Alloy

Khrouf

Tebassi

Yallese

et al. 2022

International Journal of Applied Mechanics and Engineering

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An experimental investigation is carried out to examine the effects of various cutting parameters on the response criteria when turning EN-AW-1350 aluminum alloy under dry cutting conditions. The experiments related to the analysis of the influence of turning parameters on the surface roughness (Ra) and material removal rate (MRR) were carried out according to the Taguchi L27 orthogonal array (313 ) approach. The analysis of variance (ANOVA) was applied to characterizing the main elements affecting response parameters. Finally, the desirability function (DP) was applied for a bi-objective optimization of the machining parameters with the objective of achieving a better surface finish (Ra) and a higher productivity (MRR). The results showed that the cutting speed is the most dominant factor affecting Ra followed by the feed rate and the depth of cut. Moreover, the Artificial Neural Network (ANN) approach is found to be more reliable and accurate than its Response Surface methodology (RSM) counterpart in terms of predicting and detecting the non-linearity of the surface roughness and material removal rate mathematical models. ANN provided prediction models with a precision benefit of 8.21% more than those determined by RSM. The latter is easier to use, and provides more information than ANN in terms of the impacts and contributions of the model terms.

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“…Priyabrata Sahoo et al (2017) developed an empirical model using weighted principal component analysis (WPCA) and response surface methodology (RSM) to investigate the effect of feed rate, depth of cut and spindle speed on tool vibration and surface roughness on Aluminum Alloy using coated carbide tool inserts in dry environment for turning process. Box-Behnken methodology was employed to RSM model was used to optimize the response that is influenced by various process parameters and WPCA was employed to achieve the better combination of process parameters to obtain optimum machining conditions.…”

Section: Application Of Taguchi Methods In Manufacturing Processesmentioning

confidence: 99%

Review on Optimization Techniques used for Determining Machining Conditions to get Effective Tool Life and Surface Finish

Kumar¹

2017

DAAAM International Scientific Book

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Modeling and optimization of surface roughness and tool vibration in CNC turning of Aluminum alloy using hybrid RSM-WPCA methodology

Cited by 22 publications

References 17 publications

Optimization of turning parameters of Aluminum 6351 T6 using Taguchi decision making technique

Optimization of turning parameters of Aluminum 6351 T6 using Taguchi decision making technique

Modeling and Optimization of Cutting Parameters When Turning EN-AW-1350 Aluminum Alloy

Review on Optimization Techniques used for Determining Machining Conditions to get Effective Tool Life and Surface Finish

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