Optimization and Performance Analysis of Uncoated and Coated Carbide Inserts during Hard Turning AISI D2 Steel Using Hybrid GRA-PCA Technique

Senthilkumar, N.; Selvakumar, V.; Tamizharasan, T.

doi:10.4028/www.scientific.net/amm.852.151

Cited by 34 publications

(11 citation statements)

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“…Nominal-the-best is useful when an ultimate or target value is indicated toward the quality characteristics such as clearances and dimensional tolerances [26]. The nomenclature of uncoated carbide cutting insert selected for turning the AMMC specimens was TNMG 120404.…”

Section: Materials Methodology and Experimental Proceduresmentioning

confidence: 99%

Taguchi’s methodology of optimizing turning parameters over chip thickness ratio in machining P/M AMMC

Tamizharasan¹,

Senthilkumar²,

Selvakumar

et al. 2019

SN Appl. Sci.

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Effect of turning parameters on chip generation during machining aluminum composite is studied in this work. Turning of Al-4%Cu-7.5%SiC composite material prepared through powder metallurgy procedure was chosen as the workpiece, machined using uncoated carbide insert TNMG 120404. Chips produced during machining were studied by measuring the thickness and were used along with uncut chip thickness to determine the chip thickness ratio. 99.85% pure aluminum was added with 4% volume fractions of copper and with silicon carbide particulates of 7.5%. To visualize the distribution of reinforcement phases in matrix, scanning electron microscope is used. Taguchi's methodology of design of experiments was adopted for designing a L 9 (Latin square) orthogonal array for experimental investigation, and from analysis of variance, cutting speed influencing the formation of chip by 64.13%, continuing with depth of cut by 35.26%, was identified. Confirmation test accomplished with ideal conditions produces a better chip condition.

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Section: Materials Methodology and Experimental Proceduresmentioning

confidence: 99%

Taguchi’s methodology of optimizing turning parameters over chip thickness ratio in machining P/M AMMC

Tamizharasan¹,

Senthilkumar²,

Selvakumar

et al. 2019

SN Appl. Sci.

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“…Principal component analysis (PCA) is a statistical data reduction method that reduces a big collection of variables into a smaller number of variables that includes as much information as the set of observed variables (Senthilkumar et al 2016). PCA is a multivariate approach that summarizes data from the source variables into a smaller or newer collection of statistically independent possibilities with the least amount of information degradation.…”

Section: Principal Component Analysismentioning

confidence: 99%

Multiobjective optimization of electric discharge machining of an Al–SiCp composite using the Taguchi–PCA method as well as the firefly and cuckoo search algorithms

S²

2022

Trans. Can. Soc. Mech. Eng.

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Electric Discharge Machining (EDM) processes are extensively utilized in industries for cutting hard to machine materials and geometries that are complex which are not possible with conventional machining. In this research study, efforts are made to identify optimal process parameters of EDM during machining of AA6061-10%SiCp composite material. The novelty of the present work is copper electrode with different geometries such as circular, triangular and square are considered for machining along with input variables discharge current density (A), pulse on and off timing (Ton and Toff) which are varied through three values. The L27 (313) orthogonal array of Taguchi is used for experimental layout and responses measured are recast layer thickness (RCT), electrode tool wear rate (TWR) and material removal rate (MRR). Taguchi’s approach of signal-to-noise (S/N) ratio is integrated with principal component analysis (PCA) for multi-criteria optimization. Also, nature inspired cuckoo search (CS) and firefly algorithm (FA) is employed for identifying the optimal conditions and to predict the outputs for maximum MRR and minimum TWR and RCT. From S/N+PCA analysis the optimal conditions identified are: Circle (12A, 65µs, 2µs), Triangle (12A, 95µs, 6µs) and Square (12A, 65µs, 8µs) was obtained. In all the conditions, discharge current influences higher than the other inputs. Metallurgical examination conducted through micrographs on the machined surface clearly supports the predicted result.

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“…ISSN: 2278-3075, Volume-9 Issue-4, February 2020 126 48 190 3 7 10 2060 2 70 126 51 210 4 9 15 2080 3 70 126 54 230 5 11 20 2100 4 70 127 48 190 4 9 20 2100 5 70 127 51 210 5 11 10 2060 6 70 127 54 230 3 7 15 2080 7 70 128 48 210 3 11 15 2100 8 70 128 51 230 4 7 20 2060 9 70 128 54 190 5 9 10 2080 10 75 126 48 230 5 9 15 2060 11 75 126 51 190 3 11 20 2080 12 C. Multi-Criteria Optimization Method Grey Relational Analysis (GRA) is implemented for perceiving the ideal assemblage of input factors for attaining improved responses [22]- [24]. GRA is applied for assessing the dependent parameter influence with meager data information [25]. In grey approach, pre-processing of raw data's is made initially for further analysis, pre-processing is done by converting the data points to some type of indices for quantification purposes through normalizing procedure [26].…”

Section: International Journal Of Innovative Technology and Exploringmentioning

confidence: 99%

Performance of Zinc Coated Electrode Wire and Parametric Optimization During Profile Cutting of Die-Steel

Saravanan*,

Thiyagarajan,

Somasundaram

2020

IJITEE

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In this work, the effect of brass wire coated with zinc as electrode wire towards maximizing MRR and minimizing SR for the considered input parameters was studied in elaborate during profile cutting of tool steel AISI D3. Experiments were designed based on the L18(21 ,37 ) Taguchi’s orthogonal array considering cutting speed, Ton and Toff, wire tension, servo feed rate and voltage, current and electrode feed rate. The outputs MRR and SR were analyzed and optimized using Grey Relational Analysis (GRA) simultaneously. For both SR and MRR, the drawn chart shows that, the raw data’s lies within upper and lower control limits. The optimum input conditions obtained for higher MRR and lower SR were: 75% of cutting speed, 128 µsec of Ton, 54µsec of Toff, 190A of input current, electrode feed of 3 mm/min with 7 gm of electrode tension, 10V of servo voltage and 2060 mm/min of 10V. Execution of ANOVA on grey grade identifies that, wire tension was the parameter that influences more by 25.89%, followed by Ton and servo voltage by 21.37% and feed rate of wire by 11.89%, with an R2 value of 96.55%. For validating the results, confirmation experiment was also done

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Optimization and Performance Analysis of Uncoated and Coated Carbide Inserts during Hard Turning AISI D2 Steel Using Hybrid GRA-PCA Technique

Cited by 34 publications

References 0 publications

Taguchi’s methodology of optimizing turning parameters over chip thickness ratio in machining P/M AMMC

Taguchi’s methodology of optimizing turning parameters over chip thickness ratio in machining P/M AMMC

Multiobjective optimization of electric discharge machining of an Al–SiCp composite using the Taguchi–PCA method as well as the firefly and cuckoo search algorithms

Performance of Zinc Coated Electrode Wire and Parametric Optimization During Profile Cutting of Die-Steel

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