Grey relational analysis coupled with principal component analysis for optimisation design of the process parameters in in-feed centreless cylindrical grinding

Siddiquee, Arshad Noor; Khan, Zahid A.; Mallíck, Zulquernain

doi:10.1007/s00170-009-2159-8

Cited by 107 publications

(38 citation statements)

References 16 publications

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“…Therefore, the selection of the appropriate MPC tool for a given application is very important to achieve the desired results in micro-EDM. This issue has been convincingly addressed by [18]. They demonstrated that the application of Taguchi-based grey relational analysis (GRA) compounded with principal component analysis (PCA) in precise machining.…”

Section: Introductionmentioning

confidence: 94%

“…Grey relational analysis generates [23] the linear normalization of S/N ratios in order to convert incomparable data into a comparable sequence in the range between zero and unity. The normalized S/N ratio x ij for the ith performance characteristic in the jth experiment can be expressed as in Equation (5) [18]:…”

Section: Grey Relational Analysismentioning

confidence: 99%

“…This is further processed into the "Grey Relational Coefficient (GRC): ξij", which for the ith performance characteristic in the jth experiment can be expressed as per Equation (6). (6) where is the normalized S/N ratio for ith response parameter and ξ is the distinguishing coefficient, which is defined in the range 0 ≤ ξ ≤ 1 and is usually taken as 0.5 [18].…”

Section: Grey Relational Analysismentioning

confidence: 99%

“…where x o i is the normalized S/N ratio for ith response parameter and ξ is the distinguishing coefficient, which is defined in the range 0 ≤ ξ ≤ 1 and is usually taken as 0.5 [18].…”

Section: Grey Relational Analysismentioning

confidence: 99%

See 3 more Smart Citations

An Investigation of the Micro-Electrical Discharge Machining of Nickel-Titanium Shape Memory Alloy Using Grey Relations Coupled with Principal Component Analysis

Abidi

Al-Ahmari

Siddiquee

et al. 2017

Metals

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Shape memory alloys (SMAs) are advanced engineering materials which possess shape memory effects and super-elastic properties. Their high strength, high wear-resistance, pseudo plasticity, etc., makes the machining of Ni-Ti based SMAs difficult using traditional techniques. Among all non-conventional processes, micro-electric discharge machining (micro-EDM) is considered one of the leading processes for micro-machining, owing to its high aspect ratio and capability to machine hard-to-cut materials with good surface finish.The selection of the most appropriate input parameter combination to provide the optimum values for various responses is very important in micro-EDM. This article demonstrates the methodology for optimizing multiple quality characteristics (overcut, taper angle and surface roughness) to enhance the quality of micro-holes in Ni-Ti based alloy, using the Grey-Taguchi method. A Taguchi-based grey relational analysis coupled with principal component analysis (Grey-PCA) methodology was implemented to investigate the effect of three important micro-EDM process parameters, namely capacitance, voltage and electrode material.The analysis of the individual responses established the importance of multi-response optimization. The main effects plots for the micro-EDM parameters and Analysis of Variance (ANOVA) indicate that every parameter does not produce same effect on individual responses, and also that the percent contribution of each parameter to individual response is highly varied. As a result, multi-response optimization was implemented using Grey-PCA. Further, this study revealed that the electrode material had the strongest effect on the multi-response parameter, followed by the voltage and capacitance. The main effects plot for the Grey-PCA shows that the micro-EDM parameters "capacitance" at level-2 (i.e., 475 pF), "discharge voltage" at level-1 (i.e., 80 V) and the "electrode material" Cu provided the best multi-response.

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Section: Introductionmentioning

confidence: 94%

Section: Grey Relational Analysismentioning

confidence: 99%

Section: Grey Relational Analysismentioning

confidence: 99%

“…where x o i is the normalized S/N ratio for ith response parameter and ξ is the distinguishing coefficient, which is defined in the range 0 ≤ ξ ≤ 1 and is usually taken as 0.5 [18].…”

Section: Grey Relational Analysismentioning

confidence: 99%

See 2 more Smart Citations

An Investigation of the Micro-Electrical Discharge Machining of Nickel-Titanium Shape Memory Alloy Using Grey Relations Coupled with Principal Component Analysis

Abidi

Al-Ahmari

Siddiquee

et al. 2017

Metals

View full text Add to dashboard Cite

show abstract

“…Based on ANOVA, pulse on time is found to be the most significant factor, which affects micro-EDM process. Siddiquee et al [4] investigates optimization design of an in-feed centre less cylindrical grinding process performed on EN52 austenitic valve steel. In this study, since the process is with multiple-performance characteristics, therefore, the grey relational analysis that uses the grey relational grade as performance index is specially adopted to determine the optimal combination of centre less cylindrical grinding parameters.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of ELID Grinding Process Using Grey Relational Analysis Coupled with Principal Component Analysis

Prabhu

Vinayagam

2014

Advances in Mechanical Engineering

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Carbon nanotube (CNT) mixed grinding wheel has been used in the electrolytic in-process dressing (ELID) grinding process to analyze the surface characteristics of AISI D2 Tool steel material. CNT grinding wheel is having an excellent thermal conductivity and good mechanical property which is used to improve the surface finish of the work piece. The multiobjective optimization of grey relational analysis coupled with principal component analysis has been used to optimize the process parameters of ELID grinding process. Based on the Taguchi design of experiments, an L9 orthogonal array table was chosen for the experiments. The confirmation experiment verifies the proposed that grey-based Taguchi method has the ability to find out the optimal process parameters with multiple quality characteristics of surface roughness and metal removal rate. Analysis of variance (ANOVA) has been used to verify and validate the model. Empirical model for the prediction of output parameters has been developed using regression analysis and the results were compared for with and without using CNT grinding wheel in ELID grinding process.

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Experimental analysis with grey relational analysis–analytic hierarchy process‐based hybrid optimization to analyze the effect of graphene nanoparticle reinforced ultra‐high molecular weight polyethylene/n‐HAp for hip joint

2022

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Ultra‐high molecular weight polyethylene (UHMWPE) is utilized in hip joints because of its high impact strength, outstanding fracture resistance, near‐zero moisture absorption, superior chemical inertness, and lower coefficient of friction than other polymers. However, because of the high melt viscosity of UHMWPE, the melt state approach gives unsatisfactory mechanical strength results. In this research, composite materials are fabricated by heat‐assisted compression molding having graphene nanoplatelets (GNPs) used as a filler material in a composition of (UHMWPE + 5 wt% hydroxyapatite nanoparticles) at three different wt% ages (i.e., 0, 1, 2, and 3 wt%). Here mechanical characteristics such as flexural, compression, and impact strength measurements according to ISO standards and SEM analysis are used to determine the aggregation of n‐HAp and GNP in UHMWPE. According to the authors' experimental results, biocomposite (with UHMWPE + 5 wt n‐HAp + 1 wt% GNP) has the lowest surface roughness value and excellent mechanical characterization compared to other compositions for hip joint. Flexural and compression strength improve by 25% and 40%, respectively, compared to pure UHMWPE. The multi‐objective optimization based on GRA‐AHP gives a better result at 5 wt% of hydroxyapatite nanoparticles and 1 wt% graphene nanoparticle reinforced in UHMWPE compared to other alternative materials.

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Grey relational analysis coupled with principal component analysis for optimisation design of the process parameters in in-feed centreless cylindrical grinding

Cited by 107 publications

References 16 publications

An Investigation of the Micro-Electrical Discharge Machining of Nickel-Titanium Shape Memory Alloy Using Grey Relations Coupled with Principal Component Analysis

An Investigation of the Micro-Electrical Discharge Machining of Nickel-Titanium Shape Memory Alloy Using Grey Relations Coupled with Principal Component Analysis

Multiobjective Optimization of ELID Grinding Process Using Grey Relational Analysis Coupled with Principal Component Analysis

Experimental analysis with grey relational analysis–analytic hierarchy process‐based hybrid optimization to analyze the effect of graphene nanoparticle reinforced ultra‐high molecular weight polyethylene/n‐HAp for hip joint

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