Application of Response Surface Methodology and Firefly Algorithm for Optimizing Multiple Responses in Turning AISI 1045 Steel

Senthilkumar, N.; Tamizharasan, T.; Gobikannan, S.

doi:10.1007/s13369-014-1320-3

Cited by 51 publications

(13 citation statements)

References 36 publications

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“…The input parameters in the study are speed (V, PM), feed rate (fr, mm/min) and depth of cut (d, mm) and responses fixed are average surface roughness (Ra, µm) and material removal rate (MRR,cm 3 /min). Similar studies using these inputs and responses have been cited in the literature [11,16,20,21]. The objective is to optimize the input parameters to achieve desired response values.…”

Section: Levels Of Process Variablesmentioning

confidence: 93%

“…A decrease in cutting forces and improvement in the surface finish was reported with the rise of cutting speed, whilst both depths of cut and feed had an adverse influence on surface finish. Senthilkumar et al [16] performed optimization of process parameters i.e V, fr and d for Ra, MRR and flank wear during turning of AISI 1045 HSLA steel using CCD in RSM and compared the results with firefly algorithm. It was revealed that the substantial factor for Ra and flank wear is feed rate while cutting speed for MRR.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Seeking Surface Roughness and Material Removal Rate Responses of Hardened AISI 4340 High Strength Low AlloySteel in Dry Sustainable Environment

Khawaja

Jahanzaib

Zaka

2021

Mehran Univ. res. j. eng. technol.

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The aim of this research is to study the machinability aspects of hardened AISI 4340 High Strength Low Alloy (HSLA) steel (50 ± 2 HRC (Hardness Rockwell C)). The experimental investigation using coated carbide inserts is carried out during the dry hard milling process in a sustainable environment. The input parameters in the study are speed, feed rate and depth of cut and the responses are Average surface Roughness (Ra) and Material Removal Rate (MRR) that are selected through screening. Central Composite Design (CCD) in response surface methodology has been utilized as the experimental design technique with twenty experiments. Analysis of variance has been employed to examine the momentous machining parameters and responses. A mathematical model has been developed to optimize the surface roughness and material removal rate. It has been observed that the most significant factor for Ra is feed rate while for MRR depth of cut is the most significant factor. The results show that the minimum value of Ra ~ 0.098 μm is achieved at speed ~ 1000 RPM, feed rate ~ 300 mm/min and depth of cut ~ 0.2 mm while the maximum value of MRR ~ 6.35 cm3/min is attained at feed rate ~ 500mm/min and depth of cut ~ 0.4 mm regarding less or no effect of speed ~ 500-1000 RPM. The average forecast error for the validation information has been observed to be 3.35%. for Ra and 3.2% for MRR. Further, it is investigated that good surface finish like grinding and dimensional accuracy can be achieved with coated carbide tools.

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Section: Levels Of Process Variablesmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Optimal Seeking Surface Roughness and Material Removal Rate Responses of Hardened AISI 4340 High Strength Low AlloySteel in Dry Sustainable Environment

Khawaja

Jahanzaib

Zaka

2021

Mehran Univ. res. j. eng. technol.

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“…DoE is a scientific approach, which is used to perform the analysis and modeling of the impact of process variables on the responses. 19 In such methods, process variables are treated as design factors. 20 In this study, face-centered central composite design is used in RSM.…”

Section: Methodsmentioning

confidence: 99%

Multi-response optimization of dry sliding wear parameters of AA6026 using hybrid gray relational analysis coupled with response surface method

Gajalakshmi¹,

Senthilkumar²,

Prabu

2019

Measurement and Control

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Experimental study on dry sliding wear properties of aluminum alloy 6026 were performed utilizing pin-on-disk wear testing machine, considering the wear parameters like the applied load on the pin and the rotational and track diameter of disk. Wear of the pin, coefficient of friction and frictional force were observed during the test procedure for analysis. The experimental trials were designed by L16 Orthogonal Array based on Taguchi’s design of experiments and a hybrid approach of gray relational analysis combined with response surface methodology was applied for optimizing the output responses. The optimum conditions obtained for lower wear, coefficient of friction and frictional force were 35.21 N load, 376 r/min speed of disk and 111.53 mm disk track diameter, respectively. Scanning electron microscopy image of specimens taken after testing shows that abrasive wear mechanism is the predominant mechanism of wear. Experiment of confirmation with optimum conditions shows that the result was nearer to the predicted results.

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“…Analysis of several responses is carried out in RSM. Simultaneous consideration of multiple responses involves first building an appropriate response surface model for each response and then trying to find a set of operating conditions that in some sense optimises all responses or at least keeps them in desired ranges (Senthilkumar et al, 2014b;Ghodsiyeh et al, 2014). MRR is directly proportional to the amount of energy applied during the ON-time.…”

Section: Response Surface Methodologymentioning

confidence: 99%

A study on machinability evaluation of Al-Gr-B<SUB align="right">4C MMC using response surface methodology-based desirability analysis and artificial neural network technique

Ponnuvel¹,

Senthilkumar²

2019

IJRAPIDM

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In this work, machinability behaviour of aluminium-graphite-boron carbide metal matrix composite is performed during wire-cut electrical discharge machining (WEDM) process. Experiments were designed using central composite-face centred design of response surface methodology (RSM) and with the application of desirability function multiple quality characteristics viz., kerf width, surface roughness and material removal rate (MRR) were optimised simultaneously. Input parameters gap voltage, pulse ON-time, pulse OFF-time and % reinforcement of boron carbide particles in the aluminium matrix are considered. The optimised machining condition obtained is a gap voltage of 150 V, pulse ON-time of 124.56 ms, pulse OFF-time of 48.03 ms and 2.5% reinforcement of boron carbide. From the experimental values, it is observed that better output responses are achieved with lower reinforcement of boron carbide. Second order regression models are developed individually for the output responses. An artificial neural network model is developed to predict the output responses, results obtained show that a better prediction can be achieved through artificial intelligent technique.

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Application of Response Surface Methodology and Firefly Algorithm for Optimizing Multiple Responses in Turning AISI 1045 Steel

Cited by 51 publications

References 36 publications

Optimal Seeking Surface Roughness and Material Removal Rate Responses of Hardened AISI 4340 High Strength Low AlloySteel in Dry Sustainable Environment

Optimal Seeking Surface Roughness and Material Removal Rate Responses of Hardened AISI 4340 High Strength Low AlloySteel in Dry Sustainable Environment

Multi-response optimization of dry sliding wear parameters of AA6026 using hybrid gray relational analysis coupled with response surface method

A study on machinability evaluation of Al-Gr-B<SUB align="right">4C MMC using response surface methodology-based desirability analysis and artificial neural network technique

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