Modelling and multiobjective optimization for productivity improvement in high speed milling of Ti–6Al–4V using RSM and GA

Sahu, Neelesh Kumar; Andhare, Atul B.

doi:10.1007/s40430-017-0804-y

Cited by 38 publications

(19 citation statements)

References 37 publications

(64 reference statements)

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“…From the response surface plots also, it is observed that interaction of cutting speed/feed rate is strongly affecting the surface roughness value whereas interaction of feed/ doc and cutting speed/doc has negligible effect over surface roughness [22,23].…”

Section: Validation Of Developed Model For Surface Roughness In Turnimentioning

confidence: 94%

Design of Experiments Applied to Industrial Process

Sahu¹,

Andhare²

2018

Statistical Approaches With Emphasis on Design of Experiments Applied to Chemical Processes

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Response optimization and exploration are the challenging task in front of experimenter. The cause and effect of input variables on the responses can be found out after doing experiments in proper sequence. Generally relationship between response of interest y and predictor variables x 1 ,x 2 ,x 3 , … x k is formed after carefully designing of experimentation. For examples y might be biodiesel production from crude 'Mahua' and x 1 ,x 2 and x 3 might be reaction temperature, reaction time and the catalyst feed rate in the process. In the present book chapter, design of experiment is discussed based on predictor variables for conducting experiments with the aim of building relationship between response and variables. Subsequently a case study is also discussed for demonstration of design of experiments for predicting surface roughness in the machining of titanium alloys based on response surface methodology.

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Section: Validation Of Developed Model For Surface Roughness In Turnimentioning

confidence: 94%

Design of Experiments Applied to Industrial Process

Sahu¹,

Andhare²

2018

Statistical Approaches With Emphasis on Design of Experiments Applied to Chemical Processes

Self Cite

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“…Fig. 8 clearly shows that the residues close to each other are correlated and therefore not independent [22][23][24]. While determining the optimum bending parameters from the S/N ratio obtained with Taguchi method, the relationship between the bending parameters was determined using the analysis of variance.…”

Section: Interaction Plot For S N Ratiosmentioning

confidence: 99%

Optimization of Spring Back in Air V Bending Processing using Taguchi and RSM Method

Özdemir

2020

mech

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In this study, springback behaviour of Dp600 (Dual Phase Steel) sheet material was investigated using air V bending technique. As bending parameters, four different punch tip radii and four different sheet thicknesses were used. The springback values obtained from the experiments were compared with each other. At the same time, the experimental data were analysed with Response Surface Method (RSM) and Taguchi (L16) orthogonal array. The experimental data and the simulation results were found to be consistent. The signal-to-noise (S/N) ratio is used to determine the most appropriate parameter of minimum springback behaviour. Additionally, it is possible to estimate the optimal combination of the bending parameters. The signal-to-noise (S/N) ratio and the design of experiment (DOE) were utilised for the analysis of the experimental results. It was determined that the effect of these parameters on the air V bending process was based on springback behaviours.

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“…The adequacy of developed models was verified to determine the significant and insignificant parameters considering probability (p value) and coefficient of determination (R 2 ). The model is further improved by eliminating insignificant terms using backward elimination method for obtaining best fit model [16,28]. The stepwise procedure for performing backward elimination is as follows:…”

Section: Analysis Of Vb P and R A For Dry Turningmentioning

confidence: 99%

“…They found that improved surface finish is observed at higher cutting speed and lower feed and depth of cut, providing minimum R a and VB of 0.434 lm and 0.0234 mm, respectively. Sahu and Andhare [16] developed a predictive model on different sustainable measures such as power consumption, material removal rate, surface roughness and tool wear using RSM. The models were validated and improved using ANOVA and stepwise backward elimination method.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Sustainable machining: an experimental investigation and optimization of machining Inconel 825 with dry and MQL approach

Tamang

Chandrasekaran

Sahoo

2018

J Braz. Soc. Mech. Sci. Eng.

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Sustainability is a vital issue for present and future generation, and it aims to obtain overall efficiency in terms of economic, environmental and social aspects. Inconel 825 belongs to the family of nickel-based super alloy and is widely used in the chemical and marine industries. This work attempts to investigate machining performance of Inconel 825 using physical vapor deposition-titanium nitrate inserts, with a focus on sustainable machining. The effect of cutting parameters, viz. cutting speed (v), feed (f) and depth of cut (d) on three aspects of sustainability has been explored in two different machining environments, viz. dry and minimum quantity lubrication (MQL). The experimental results show a significant improvement in MQL machining and tool wear, and cutting power is reduced by 16.57 and 8.47%, respectively, and surface roughness is improved by 10.41%. The interacting effects of parameters on responses are studied using 3-D surface plots; it shows cutting speed and feed are found as dominating parameters on all the three responses. The novelty of this work is to optimize the process for the sustainable production of components by optimizing the process parameters with multiple and conflicting objectives. The sustainable optimization using genetic algorithm provides surface roughness (R a) as 0.49 lm, tool flank wear (VB) as 110.68 lm and cutting power (P) as 5.44 kW with better convergent capability having 4% deviation. For the application of manufacturing industry, an optimization table is generated for selection of optimum process parameters for achieving desired surface roughness with minimum VB or minimum P.

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Modelling and multiobjective optimization for productivity improvement in high speed milling of Ti–6Al–4V using RSM and GA

Cited by 38 publications

References 37 publications

Design of Experiments Applied to Industrial Process

Design of Experiments Applied to Industrial Process

Optimization of Spring Back in Air V Bending Processing using Taguchi and RSM Method

Sustainable machining: an experimental investigation and optimization of machining Inconel 825 with dry and MQL approach

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