Multi-response optimisation in CNC turning of Al-6082 T6 using grey Taguchi method coupled with principal component analysis

Kumar, G. Suresh; Reddy, P. Venkateshwar; Krishnudu, D. Mohana

doi:10.1504/ijcaet.2020.110487

Cited by 4 publications

(3 citation statements)

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“…GRA is a way of transforming two or more responses into a single response. In the sub-sections, the method for measuring the GRA is explained (Reddy et al , 2020a, b, c, d; Kumar et al , 2020). Step 1: Normalization…”

Section: Resultsmentioning

confidence: 99%

“…The MRR, CF and SR were the three responses examined in this work. Using a Talysurf roughness tester measurement tool, the machined workpiece material surface was conducted in five different locations, and the overall surface roughness (Ra) value was determined derived from the literature study (Kumar et al , 2020). The rate of removal of materials was determined using Eqn (1).…”

Section: Methodsmentioning

confidence: 99%

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Modeling of the machining parameters in turning of Al-5052/TiC/SiC composites: a statistical modeling approach using grey relational analysis (GRA) and response surface methodology (RSM)

Reddy¹,

Reddigari

Anna

et al. 2021

MMMS

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PurposeMetal matrix composites (MMC) has been a section which gives an overview of composite materials and owing to those exceptional physical and mechanical properties, particulate-reinforced aluminum MMCs have gained increasing interest in particular engineering applications. Owing to the toughness and abrasive quality of reinforcement components such as silicon carbide (SiC) and titanium carbide (TiC), such materials are categorized as difficult materials for machining. The work aims to develop the model for evaluating the machinability of the materials via the response surface technique by machining three distinct types of hybrid MMCs.Design/methodology/approachThe combined effects of three machining parameters, namely “cutting speed” (s), “feed rate” (f) and “depth of cut” (d), together with three separate composite materials, were evaluated with the help of three performance characteristics, i.e. material removal rate (MRR), cutting force (CF) and surface roughness (SR). Response surface methodology and analysis of variance (ANOVA) both were initially used for analyzing the machining parameters results.FindingsThe contours were developed to observe the combined process parameters along with their correlations. The process variables were concurrently configured using grey relational analysis (GRA) and the composite desirability methodology. Both the GRA and composite desirability approach obtained similar results.Practical implicationsThe results obtained in the present paper will be helpful for decision-makers in manufacturing industries, who work on metal cutting area especially composites, to select the suitable solution by implementing the Grey Taguchi and modeling techniques.Originality/valueThe originality of this research is to identify the suitability of process parameters combination based on the obtained research results. The optimization of machining parameters in turning of hybrid metal matrix composites is carried out with two different methods such as Grey Taguchi and composite desirability approach.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Modeling of the machining parameters in turning of Al-5052/TiC/SiC composites: a statistical modeling approach using grey relational analysis (GRA) and response surface methodology (RSM)

Reddy¹,

Reddigari

Anna

et al. 2021

MMMS

View full text Add to dashboard Cite

show abstract

“…Finally, the results were compared with the ANOVA technique optimized results. The speed and feed were the most significant factors for deciding the machined components' tool life and surface quality [ 17 ]. The CNC turning operation of aluminium alloy (various grades) was performed and numerically predicts the effect of cutting tool inserts with additions of input process parameters like machining time, surface roughness, and MRR on tool life.…”

Section: Literature Reviewmentioning

confidence: 99%