Investigation and optimization of machining parameters influence on surface roughness in turning AISI 4340 steel

Anantha, Lakshmipathi Raman; Senthilkumar, N.; Shivasankar, Jayakumar

doi:10.5937/fme2002383b

Cited by 32 publications

(5 citation statements)

References 31 publications

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“…If there is no combinatory impact, the significance of the first variable is the same regardless of the value of the second factor, and vice versa. The bigger the skew among the components, the greater the interaction among the lines (Deepanraj et al 2020). A interaction implies that the effect of the first variable is dependent on the level of the second factor, and vice versa.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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“…To assess and optimize the welding parameters, the Taguchi methodology, a method for generating process improvements, was employed [25,26]. By choosing the main variables that influence the process [27][28][29] and optimizing the methods to get the best results, these enhancements strive to improve the desirable features and reduce flaws. To examine the variation in the output responses, the input parameters are grouped and clustered in the L9 orthogonal array (OA) order [30,31].…”

Section: Taguchi Methodsmentioning

confidence: 99%

Multi-objective optimization of FSW variables on joint properties of AA5754 aluminum alloy using Taguchi approach and grey relational analysis

sobhy,

Elbadawi,

Zoalfakar

2023

Preprint

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The friction stir welding (FSW) process was applied to butt-weld AA5754 plates, and the joints of each weld case were identified by their tensile strength, hardness, and impact toughness. The combined effects of the process variables rotation speed, welding speed, and tilt angle have been evaluated in the present study using Taguchi's design of experiments approach and grey relational analysis (GRA). A multi-objective optimization approach (Taguchi method and GRA) was used to optimize and rank the process variables. The developed models are evaluated for sufficiency, and the most significant terms are found using the ANOVA approach. The results of the ANOVA showed that the rotational speed has a maximum contribution of 55.24%, 59%, and 46.27% in obtaining the optimal values of tensile strength, hardness, and impact toughness, respectively. Finding an optimal parameter that offers the maximum value of all replies requires the use of the GRA and Taguchi Technique by Similarity to Suitable Solution. The two methods provide the same results, and the optimal conditions are a rotational speed of 1000 rpm, a welding speed of 60 mm/min, and a tilt angle of 2.5 degrees. The optimal values for tensile strength, hardness, and impact toughness, respectively, were found to be 136 MPa, 85.25 HV, and 13 J.

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“…Balakrishnan Deepanraj et al, [18] conducted experiments on selected process parameters like cutting speed, feed rate, depth of cut to get the surface roughness as an output by using Taguchi L9 OA design on AISI 4340 steel as a workpiece. Therefore, it was observed that the most contribution factor to get surface roughness was feed rate 70.50% and followed by depth of cut (18.54%) and cutting speed (9.15%).…”

Section: Introductionmentioning

confidence: 99%

Multi-response optimization of PMEDM on Inconel 718 using hybrid T-GRA, TOPSIS, and ANN model

Sai,

Shiek,

Syed

2023

FME Transactions

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Inconel 718 is one of the Nickel-based superalloys considered one of the most difficult-to-machine materials owing to its property to retain hardness at higher temperatures. This study examined the performance of Edcutor-based PMEDM machining on Inconel 718. Taguchi L9 OA has been used with current, Pulse-OFF time & Pulse-ON time as process parameters with a delivery side pressure of 6 bar for the Alumina mixed dielectric. Material removal rate (MRR), Tool wear rate (TWR), & surface roughness (SR) have been taken as output responses. The results have been investigated by MADM techniques, namely Taguchi-based Grey Relational Analysis (T-GRA) & TOPSIS analysis. Furthermore, the ranks obtained from T-GRA & TOPSIS have been validated by developing a single layered ANN model. Ranks predicted by the ANN model are the same for T-GRA and TOPSIS and the R-values are 0.924 & 0.871, respectively. ANOVA has also been used to analyze parameter effects on output responses.

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Investigation and optimization of machining parameters influence on surface roughness in turning AISI 4340 steel

Cited by 32 publications

References 31 publications

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

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

Multi-objective optimization of FSW variables on joint properties of AA5754 aluminum alloy using Taguchi approach and grey relational analysis

Multi-response optimization of PMEDM on Inconel 718 using hybrid T-GRA, TOPSIS, and ANN model

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