Surface Roughness Identification Using the Grey Relational Analysis with Multiple Performance Characteristics in Turning Operations

Suhail, Adeel H.; Wong, Shaw Voon; Jalil, Nawal Aswan Abdul

doi:10.1007/s13369-012-0229-y

Cited by 33 publications

(18 citation statements)

References 10 publications

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“…Grey Relational Analysis (GRA) is adopted for identifying the best combination of input factors for obtaining better output variables [24], [25]. GRA is largely used for judging or to evaluate the dependent variable performance that has a little amount of information, but in grey analysis, the data must be initially pre-processed for conversion into some sort of indices that can quantify the data through normalizing the raw data for further analysis [26]- [28].…”

Section: Grey Relational Approachmentioning

confidence: 99%

Enhancing the Tool Die Steel P rofile C utting P erformance in WEDM P rocess

Saravanan*¹,

Thiagarajan²,

Somasundaram³

2019

IJEAT

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This work elaborates the experimental work on Material Removal Rate (MRR) and Surface Roughness (SR) output conditions of Wire-Electrical Discharge Machining (WEDM) and finding the optimal input conditions through Taguchi method coupled with Grey relational analysis for lower SR and higher MRR during profile cutting of high strength D3 tool steel, which is the need of the hour in industries. The machining factors considered for investigation which influences MRR and SR were: cutting speed, pulse on-time and off-time, input current, wire tension and feed and servo feed and voltage. A L18 orthogonal array was considered for mixed-level experimental design through Taguchi’s approach and for multi-criteria optimization Grey Relational Analysis was applied. Outcome shows that SR increases with the increase of pulse on-time and decreases with increase in pulse off-time and MRR increases as the pulse on-time increases due to longer spark duration. Both SR and MRR are well within the control limits and servo voltage is the most influential parameter contributing by 48.48%, followed by wire feed rate, input current and servo feed rate with an R2 value of 95.85%, identified through Analysis of Variance (ANOVA). With obtained optimum conditions, a validation experiment was conducted to authenticate the results, which indicates a worthy agreement with predicted output characteristics.

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Section: Grey Relational Approachmentioning

confidence: 99%

Enhancing the Tool Die Steel P rofile C utting P erformance in WEDM P rocess

Saravanan*¹,

Thiagarajan²,

Somasundaram³

2019

IJEAT

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“…They observed that cutting speed and feed rate were highly influenced on the surface finish. Suhail et al [10] also worked on turning of the mild steel and optimized the controlled parameters using grey relational analysis (GRA) technique.…”

Section: A Research In Turning Of Ferrous Metas and Their Alloysmentioning

confidence: 99%

“…RSM and Taguchi Techniques for modeling and optimization of the parameters were tested by several researchers [78,79]. The application of GRA optimization technique also tested to optimize the process parameters [10]. The application genetic algorithm in turning process are also tested by several researchers to optimize the process parameters and get a lot of optimal solution within the range of selected parameters, which provided additional facilities to manufacturer to set the input turning conditions according to the available resources or as per design requirements [2,[79][80].…”

Section: B Research In Optimization Of Turning Processmentioning

confidence: 99%

An Overview on Turning Process

2015

International Conference on Advancements and Recent Innovations in Mechanical, Production and Industrial Engineering

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Abstract-Turning is one of the most popular convectional machining processes to remove the extra material from the rotating surfaces. In this process, a wedge shaped cutting tool penetrated toward the workpiece and removed the extra material in the form of chips. Almost all known materials can be machined by this process in different forms like external turning, internal turning, plain/cylindrical turning, step turning, taper turning, face turning etc. with selection of appropriate cutting tool for particular material. The aim of the present study is to summarize a report on the turning process including present and past research works. This paper present a brief overview on turning operation considering the different aspects and focus the future scopes of works in the same area, which is beneficial for the researches who work in the same field.

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“…Sahin and Motorcu (2008) employed the response surface methodology to minimize the surface roughness of AISI 1050 hardened steel bars using CBN and TiC cutting tools. Suhail et al (2012) investigated the influence of cutting speed, feed rate and depth of cut on surface roughness using Grey Relational Analysis (GRA) technique. Gunay and Yucel (2013) used orthogonal array and analysis of variance (ANOVA) to evaluate the effect of cutting parameters on average surface roughness in turning high alloy white cast iron.…”

Section: Introductionmentioning

confidence: 99%

Selection of a suitable multiresponse optimization technique for turning operation

Nayak¹,

Rana²

2016

10.5267/j.dsl

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The present work deals with the comparison of four multi response optimization methods, viz. multiple response signal-to-noise (MRSN) ratio, weighted signal-to-noise (WSN) ratio, Grey relational analysis (GRA), and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian) methods taking a case study in turning mild steel specimen using HSS cutting tool. The various factors like cutting speed, feed rate, depth of cut and coolant flow rate are considered as the input process variables, while the material removal rate (MRR), surface roughness (SR) and specific energy consumption (SEC) are considered as various performance characteristics. One set of experimental data is analyzed using the standardized procedures. The optimization performances of these four methods are compared. The results show that MRSN ratio method proves to be the best optimization method. It is found that the feed rate has a highest impact on the overall performance as compared to other process parameters.

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Surface Roughness Identification Using the Grey Relational Analysis with Multiple Performance Characteristics in Turning Operations

Cited by 33 publications

References 10 publications

Enhancing the Tool Die Steel P rofile C utting P erformance in WEDM P rocess

Enhancing the Tool Die Steel P rofile C utting P erformance in WEDM P rocess

An Overview on Turning Process

Selection of a suitable multiresponse optimization technique for turning operation

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