Multi objective prediction and optimization of control parameters in the milling of aluminium hybrid metal matrix composites using ANN and Taguchi -grey relational analysis

Daniel, S. Ajith Arul; Pugazhenthi, R.; Kumar, R.; Vijayananth, S.

doi:10.1016/j.dt.2019.01.001

Cited by 103 publications

(25 citation statements)

References 26 publications

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“…Grey relational method is a branch of grey systems theory developed in 1980 (Lin & Liu, (2004), October) and has been largely applied to MCDA problems in wide range of facilities (Wang et al, 2008;Liu et al, 2017;Sarpkaya & Sabir, 2016;Daniel at al., 2019). Steps of the calculation are as follows.…”

Section: Grey Relational Analysis (Gra) Methodsmentioning

confidence: 99%

“…Data to be normalized Normalization in the theory of grey system projects is called Grey Relational Generating (GRG). The data normalization is considered to be one of the widely used methods of linear data preprocessing (Wang et al, 2009;Daniel et al, 2019;Sarpkaya & Sabir, 2016). It should be normalized according to the specific importance ("The Larger -The Better", "The Smaller -The Better") of the obtained series' criteria.…”

Section: Grey Relational Analysis (Gra) Methodsmentioning

confidence: 99%

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Application of AHP and GRA methods in energy efficiency potential’s assessment of envelopes from natural materials

Biks¹,

Ratushnyak²,

Ratushnyak³

et al. 2020

Theory build. pract.

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The best choice of energy efficient envelope from variety of available materials is still the challenge. Therefore, the attempt of thermal performance multi-criteria evaluation of some building materials of natural origin for energy-efficient envelopes is conducted in present paper. Such types of walls from natural energy-efficient materials are considered in comparison assessment: hempcrete, adobe, strawbale panel, earthbag, cordwood, SIP (plywood+ecofiber), hempcrete+straw and energy efficient block. The influence of thermal inertia time, internal areal heat capacity, as well dimensionless index of thermal inertia D, the total thermal resistance of the walls Rtot-value, mass of the wall assembly and its cost have been taken into consideration as important influence factors. The multi-criteria numerical assessment of envelope’s energy efficiency potential was performed by two popular methods – Analytic Hierarchy Process (AHP) as the subjective weighting method and Grey Relation Analysis (GRA) as the objective weighting method. Both of methods allow to arrange the alternatives and could be applied as decision support tools in decision making (DM) process of choosing the best alternative in terms of multi-criteria assessment. For more objective analysis, by taking into account the variety of physical and physical-mechanical parameters of the wall assembly material, the concept of generalized index of the envelope energy efficiency potential is proposed. Conducted research has shown that the best envelope type in terms of of generalized index of energy efficiency potential has the hempcrete wall and hemcrete+straw wall, almost three times smaller has the wall of the earthbags. The walls from adobe, cordwood and strawbale panels have practically the equal value of generalized index of energy efficiency potential. It could be observed that AHP method shown more inhomogeneous results, than GRA. The possible reason for that is the difference in evaluation attitude in techniques - AHP is considered as the subjective method with pairwise comparison matrixes, while GRA is objective method of comparison.

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Section: Grey Relational Analysis (Gra) Methodsmentioning

confidence: 99%

Section: Grey Relational Analysis (Gra) Methodsmentioning

confidence: 99%

Application of AHP and GRA methods in energy efficiency potential’s assessment of envelopes from natural materials

Biks¹,

Ratushnyak²,

Ratushnyak³

et al. 2020

Theory build. pract.

View full text Add to dashboard Cite

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“…1, the Taguchi optimization technique is well applied in MMC machinability analysis owing to its simplicity and its ability for process improvement. The major shortcoming of the Taguchi method of optimization is its inability to optimize multi-responses [12]- [15]. Research into the machinability studies of selected AMCs include those done by [16] which investigated the effect of bismuth on the surface roughness and cutting forces on Al-Mg2Si.…”

Section: Fig 1 Optimization Techniques Applied For the Machinability mentioning

confidence: 99%

“…This reason has led to research into combining grey relational analysis with Taguchi based optimization technique. This combination has led to the hybridization of the Taguchi based-grey relational analysis which is capable of optimizing multiple responses [12]. The integration of grey relational analysis with the Taguchi based optimization works by converting multioptimization problems into a single objective optimization problem [19].…”

Section: Grey Relational Analysis (Gra)mentioning

confidence: 99%

Characterization, machinability studies, and multi-response optimization of AA 6082 hybrid metal matrix composite

Ononiwu

Ozoegwu

Madushele

et al. 2021

Int J Adv Manuf Technol

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This work investigated the effect of carbonized eggshell and fly ash on the microstructure, mechanical properties and machinability of AA 6082. The fabrication method selected for this study was stir casting. For the hybrid metal matrix composite, the weight fraction was 2.5wt% carbonized eggshell and 2.5wt% fly ash. The microstructural examination of the cast composite showed an even dispersal of the reinforcing phases in the aluminium matrix. Density analysis saw a 10.66% reduction of the cast composite in comparison to the aluminium alloy. Improvements of 12.32%, 21.91%, and 8.30% were recorded for the microhardness, tensile strength and compressive strength respectively. The wear studies of the cast samples revealed coefficients of friction (CoF) of 0.499 and 0.290 for the base metal and the AMC respectively. For the machinability studies, the surface roughness and flank tool wear were the responses under consideration. The design of experiments was conducted using the Taguchi method. The input parameters for this investigation was the cutting speeds (100 mm/min, 200 mm/min, 300 mm/min), feeds (0.1 mm/rev, 0.2 mm/rev, 0.3 mm/rev) and depths of cut (0.5 mm, 1.0 mm, 1.5 mm). For the multi-response optimization, Taguchi based grey relational analysis was used. The analysis of variance (ANOVA) of the grey relational grade (GRG) revealed that the feed was the most influential factor on the surface roughness and tool wear. The initial optimization showed the optimal cutting speed, feed and depth of cut as 200 mm/min, 0.2 mm/rev and 1.5 mm respectively. The confirmatory experiments revealed that the optimal combination of factors was 200mm/min, 0.2 mm/rev and 0.5 mm for the cutting speed, feed and depth of cut respectively.

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“…Li et al (16) also used parameter correlation, RSM, and multiobjective swarm optimization (MOPSO) in the Taguchi method to assist in a search for optimal production efficiency. Ajith Arul Daniel et al (17) used an artificial neural network (ANN) to carry out prediction and parameter optimization research on Taguchi quality and grey relational analysis (GRA) to examine milling machine performance. Thankachan et al (18) used the Taguchi method, GRA, and an ANN to predict and optimize the surface roughness of products made of aluminum alloys and the material removal rate.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Lathe Cutting Parameters Using Taguchi Method and Grey Relational Analysis

Jian¹,

Wang²,

Yau³

et al. 2020

Sensors and Materials

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In the current precision industry, the rapid production of high-quality parts in bulk quantities has led to high competitiveness. In this study, the Taguchi method and grey relational analysis (GRA) approach were used in a practical investigation of precision lathe processing. The purpose was to find optimal parameters for single-target and multitarget cutting. The production of targets of the highest quality was the research focus, with the aim of strengthening the links between this study and the application to the processing industry. Precision, surface roughness, and material removal rate were selected as targets for improvement. The parameters commonly used for lathe processing were set as control factors, and cutting depth, spindle speed, feed rate, and material elongation were set as experimental factors. The results showed that in the cutting of materials, cutting precision was mainly affected by the depth of cut and spindle speed, surface roughness by spindle speed, and the material removal rate by the cutting depth. In a comparison of the quality loss for the same materials using previous parameters, the cutting precision has about 64 to 99% optimization, the surface roughness has 69 to 96% optimization, and the material removal rate has more than 90% optimization. GRA was also employed to analyze the sequences of parameters from the Taguchi experiments to obtain the target relationships and to find the various combinations of factors for improvement.

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Multi objective prediction and optimization of control parameters in the milling of aluminium hybrid metal matrix composites using ANN and Taguchi -grey relational analysis

Cited by 103 publications

References 26 publications

Application of AHP and GRA methods in energy efficiency potential’s assessment of envelopes from natural materials

Application of AHP and GRA methods in energy efficiency potential’s assessment of envelopes from natural materials

Characterization, machinability studies, and multi-response optimization of AA 6082 hybrid metal matrix composite

Optimization of Lathe Cutting Parameters Using Taguchi Method and Grey Relational Analysis

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