Multiresponse optimization in wire electric discharge machining (WEDM) of HCHCr steel by integrating response surface methodology (RSM) with differential evolution (DE)

Gaitonde, V. N.; Manjaiah, M.; Maradi, S.; Karnik, S.R.; Petkar, Praveenkumar M.; Davim, J. Paulo

doi:10.1016/b978-0-85709-481-0.00007-0

Cited by 19 publications

(9 citation statements)

References 22 publications

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“…Although E (SHP Temperature), and K (Induced Draft Fan Power) were accepted with P-Value >0.05, they were accepted to maintain the hierarchical order in the final RSM model. Hierarchical order was prioritized over P-Value in the RSM analysis to obtain a reliable final equation model (Gaitonde et al, 2017;Noordin et al, 2004;Tanaka et al, 2007). The hierarchical order prioritization in this analysis was supported by the high R-square at 87.22% compared to the advised R-square of >75% (Haaland, 1989;Omar et al, 2009).…”

Section: Resultsmentioning

confidence: 94%

Ethylene Yield from a Large Scale Naphtha Pyrolysis Cracking Utilizing Response Surface Methodology

Zakria¹,

Nawawi²,

Rahman³

2021

JST

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Statistical software is a robust application that has proven reliable worldwide. However, it is not normally used in the actual large scale olefin plant as it relies on the simulation software by Olefin Licensor should any issue rises. The study was conducted in a newly commissioned large scale olefin plant to see the impact of various operating variables on the ethylene yield from Short Residence Time (SRT) VII Furnace. The analysis was conducted utilizing statistical analysis, Response Surface Methodology (RSM) in Minitab Software Version 18 to develop a reliable statistical model with a 95% confidence level. The historical data was taken from the Process Information Management System (PIMS) Software, PI Process Book Version 2015, and underwent both residuals and outliers removal prior to RSM analysis. 10 variables were shortlisted from the initial 15 identified variables in the studied SRT VII via Regression analysis due to RSM limitation to conduct the larger analysis in Minitab Software Version 18. The Response Optimizer tool showed that the ethylene yield from naphtha pyrolysis cracking in the studied plant could be maximized at 34.1% with control setting at 600.39 kg/ hr of Integral Burner Flow, 6.81% of Arch O2, 113.42 Barg of Steam Drum Pressure, 496.96°C of Super High Pressure (SHP) Temperature, 109.11 t/hr of SHP Boiler Feed Water (BFW) Flow, 92.78 t/hr of SHP Flow, 63.50 t/hr of Naphtha Feed Flow, and -13.38 mmHg of Draft Pressure.

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Section: Resultsmentioning

confidence: 94%

Ethylene Yield from a Large Scale Naphtha Pyrolysis Cracking Utilizing Response Surface Methodology

Zakria¹,

Nawawi²,

Rahman³

2021

JST

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“…An increase in pulse-on time increases EWR due to higher spark intensity during the EDM operation. Thus, the discharge energy during longer pulse-on time dissipates more heat which would cause the tool to wear out more due to excess heat [37]. Voltage and current also show similar effects on EWR, as both of them are responsible for increased discharge energy, resulting in intense heating of the tool, wearing it out at higher rate.…”

Section: Simultaneous Estimation Of the Discriminant Functionmentioning

confidence: 99%

“…Voltage and current also show similar effects on EWR, as both of them are responsible for increased discharge energy, resulting in intense heating of the tool, wearing it out at higher rate. Increased pulse-off time permits heat dissipation during sparking, while flushing away the debris from the machining zone, thus decreasing the amount of heat generated [37]. Hence, the tool wears out less, when pulse-off time increases.…”

Section: Simultaneous Estimation Of the Discriminant Functionmentioning

confidence: 99%

Discriminant Analysis-based Parametric Study of an Electrical Discharge Machining Process

Sarker

Chakraborty

2021

Scientia Iranica

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This paper presents the application of discriminant analysis in an electrical discharge machining (EDM) process to determine the comparative contribution of each of its input parameters on the measured responses. It also identifies the most significant EDM process parameters influencing those responses. For this process, voltage, current, pulse-on time and pulse-off time are treated as the input parameters, whereas, material removal rate, electrode wear rate and surface roughness are the responses. Based on the past and simulated experimental data, both simultaneous and step-wise estimations are carried out for each of the three responses showing the relationships between the EDM process parameters and the considered responses. It is observed that in both these estimations, pulseoff time, current and pulse-on time respectively evolve out as the most significant parameters for material removal rate, electrode wear rate and surface roughness. Step-wise estimation identifies voltage as the least significant input parameter for all these responses. The developed discriminant functions, which can also help in predicting the responses, are finally cross-validated.

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“…where R is the response parameter of the system, f is the performance function, p i is the independent parameter i. 25 The numerical data are required for this approach. These were obtained by the mentioned methods and were analyzed by Minitab software based on principles of the RSM model.…”

Section: Rsm Modelmentioning

confidence: 99%

Fabrication of fused silica‐based cores by the gel‐casting method: Modeling by response surface methodology

Poorvahab

Kazemi

Haghshenas

2020

Int J Applied Ceramic Tech

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In this study, fused silica‐based cores fabrication via gel‐casting procedure was modeled via the response surface methodology (RSM). Zircon content, alumina content, and sintering time were selected as variables and the influences of these parameters on the characteristic of the prepared samples were investigated. Two series of experiments were organized; (i) evaluation and modeling of the sintered samples and (ii) evaluation and modeling of the samples which passed pouring‐simulation treatment. Mechanical strength, porosity content, and leachability were selected as the response parameters and the obtained numerical data were analyzed using the Minitab software. Based on the RSM principles, the main effects plots and contour plots were recorded and the system was studied by means of a statistical model. However, both series of samples were evaluated and mathematical regression equations were gained from the RSM technique for modeling the response parameters; MOR, porosity, and leachability. Results showed that in the sintered samples, the most effective parameters on the MOR, porosity, and leachability values were Al2O3 content, zircon content & sintering time, and zircon content, respectively. It is interesting that in the pouring‐simulated samples, the most effective variable on the response parameters was Al2O3 content.

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Multiresponse optimization in wire electric discharge machining (WEDM) of HCHCr steel by integrating response surface methodology (RSM) with differential evolution (DE)

Cited by 19 publications

References 22 publications

Ethylene Yield from a Large Scale Naphtha Pyrolysis Cracking Utilizing Response Surface Methodology

Ethylene Yield from a Large Scale Naphtha Pyrolysis Cracking Utilizing Response Surface Methodology

Discriminant Analysis-based Parametric Study of an Electrical Discharge Machining Process

Fabrication of fused silica‐based cores by the gel‐casting method: Modeling by response surface methodology

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