EDM investigation of Al 7075 alloy reinforced with B4C and fly ash nanoparticles and parametric optimization for sustainable production

Sweety Mahanta, M. Chandrasekaran, Sutanu Samanta, R. M. Arunachalam

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Sustainability plays a crucial role in manufacturing industries for the production of components in terms of economic, social and environmental considerations. In the present work, aluminium alloy (Al 7075) based hybrid nanocomposites reinforced with boron carbide (B4C) (1.5 wt%) and fly ash (1.5 wt%) were developed. The machining investigation is carried out using electrical discharge machining (EDM) with a focus on sustainable production of components considering economic, environmental and social aspects. Face-centered central composite design (CCD) was adopted to conduct experiments using four EDM parameters such as voltage, current, pulse-on time and pulse-off time to ascertain the effects of three sustainable measures, viz., material removal rate (as economic aspect), power consumption (as environmental aspect) and surface roughness (as social aspect). Current and pulse-on-time found to be the dominating factors affecting the sustainable measures. The response surface methodology (RSM) models establish input–output relationships and the R-squared value of the developed full quadratic model is found to be more than 97.33% indicating better predictive capability. For the sustainable production of aluminium hybrid nanocomposites (Al-HNC), the process parameters were optimized simultaneously using two approaches. The optimized parameters are validated by confirmation experiments and show an error percentage less than 8.11%. The Pareto optimal fronts obtained from the genetic algorithm (GA) provide a different set of optimum cutting conditions for production of jobs with maximum material removal rate (MRR) or minimum power consumption (PC) fulfilling the desired value of surface roughness (SR). The proposed method is found to be easy to implement and very useful for shop floor applications.

Original languageEnglish
Article number263
JournalJournal of the Brazilian Society of Mechanical Sciences and Engineering
Volume40
Issue number5
DOIs
Publication statusPublished - May 1 2018

Fingerprint

Electric discharge machining
Fly ash
Nanoparticles
Social aspects
Economics
Nanocomposites
Electric power utilization
Surface roughness
Boron carbide
Sustainable development
Aluminum alloys
Machining
Genetic algorithms
Experiments
Aluminum
Composite materials
Electric potential
Industry

Keywords

  • EDM
  • MMC
  • Optimization
  • Pareto optimal fronts
  • Sustainability

ASJC Scopus subject areas

  • Mechanical Engineering

Cite this

@article{6c99d07fbdb74f1fa2b24ea7496d297f,
title = "EDM investigation of Al 7075 alloy reinforced with B4C and fly ash nanoparticles and parametric optimization for sustainable production",
abstract = "Sustainability plays a crucial role in manufacturing industries for the production of components in terms of economic, social and environmental considerations. In the present work, aluminium alloy (Al 7075) based hybrid nanocomposites reinforced with boron carbide (B4C) (1.5 wt{\%}) and fly ash (1.5 wt{\%}) were developed. The machining investigation is carried out using electrical discharge machining (EDM) with a focus on sustainable production of components considering economic, environmental and social aspects. Face-centered central composite design (CCD) was adopted to conduct experiments using four EDM parameters such as voltage, current, pulse-on time and pulse-off time to ascertain the effects of three sustainable measures, viz., material removal rate (as economic aspect), power consumption (as environmental aspect) and surface roughness (as social aspect). Current and pulse-on-time found to be the dominating factors affecting the sustainable measures. The response surface methodology (RSM) models establish input–output relationships and the R-squared value of the developed full quadratic model is found to be more than 97.33{\%} indicating better predictive capability. For the sustainable production of aluminium hybrid nanocomposites (Al-HNC), the process parameters were optimized simultaneously using two approaches. The optimized parameters are validated by confirmation experiments and show an error percentage less than 8.11{\%}. The Pareto optimal fronts obtained from the genetic algorithm (GA) provide a different set of optimum cutting conditions for production of jobs with maximum material removal rate (MRR) or minimum power consumption (PC) fulfilling the desired value of surface roughness (SR). The proposed method is found to be easy to implement and very useful for shop floor applications.",
keywords = "EDM, MMC, Optimization, Pareto optimal fronts, Sustainability",
author = "Sweety Mahanta and M. Chandrasekaran and Sutanu Samanta and Arunachalam, {R. M.}",
year = "2018",
month = "5",
day = "1",
doi = "10.1007/s40430-018-1191-8",
language = "English",
volume = "40",
journal = "Journal of the Brazilian Society of Mechanical Sciences and Engineering",
issn = "1678-5878",
publisher = "Brazilian Society of Mechanical Sciences and Engineering",
number = "5",

}

TY - JOUR

T1 - EDM investigation of Al 7075 alloy reinforced with B4C and fly ash nanoparticles and parametric optimization for sustainable production

AU - Mahanta, Sweety

AU - Chandrasekaran, M.

AU - Samanta, Sutanu

AU - Arunachalam, R. M.

PY - 2018/5/1

Y1 - 2018/5/1

N2 - Sustainability plays a crucial role in manufacturing industries for the production of components in terms of economic, social and environmental considerations. In the present work, aluminium alloy (Al 7075) based hybrid nanocomposites reinforced with boron carbide (B4C) (1.5 wt%) and fly ash (1.5 wt%) were developed. The machining investigation is carried out using electrical discharge machining (EDM) with a focus on sustainable production of components considering economic, environmental and social aspects. Face-centered central composite design (CCD) was adopted to conduct experiments using four EDM parameters such as voltage, current, pulse-on time and pulse-off time to ascertain the effects of three sustainable measures, viz., material removal rate (as economic aspect), power consumption (as environmental aspect) and surface roughness (as social aspect). Current and pulse-on-time found to be the dominating factors affecting the sustainable measures. The response surface methodology (RSM) models establish input–output relationships and the R-squared value of the developed full quadratic model is found to be more than 97.33% indicating better predictive capability. For the sustainable production of aluminium hybrid nanocomposites (Al-HNC), the process parameters were optimized simultaneously using two approaches. The optimized parameters are validated by confirmation experiments and show an error percentage less than 8.11%. The Pareto optimal fronts obtained from the genetic algorithm (GA) provide a different set of optimum cutting conditions for production of jobs with maximum material removal rate (MRR) or minimum power consumption (PC) fulfilling the desired value of surface roughness (SR). The proposed method is found to be easy to implement and very useful for shop floor applications.

AB - Sustainability plays a crucial role in manufacturing industries for the production of components in terms of economic, social and environmental considerations. In the present work, aluminium alloy (Al 7075) based hybrid nanocomposites reinforced with boron carbide (B4C) (1.5 wt%) and fly ash (1.5 wt%) were developed. The machining investigation is carried out using electrical discharge machining (EDM) with a focus on sustainable production of components considering economic, environmental and social aspects. Face-centered central composite design (CCD) was adopted to conduct experiments using four EDM parameters such as voltage, current, pulse-on time and pulse-off time to ascertain the effects of three sustainable measures, viz., material removal rate (as economic aspect), power consumption (as environmental aspect) and surface roughness (as social aspect). Current and pulse-on-time found to be the dominating factors affecting the sustainable measures. The response surface methodology (RSM) models establish input–output relationships and the R-squared value of the developed full quadratic model is found to be more than 97.33% indicating better predictive capability. For the sustainable production of aluminium hybrid nanocomposites (Al-HNC), the process parameters were optimized simultaneously using two approaches. The optimized parameters are validated by confirmation experiments and show an error percentage less than 8.11%. The Pareto optimal fronts obtained from the genetic algorithm (GA) provide a different set of optimum cutting conditions for production of jobs with maximum material removal rate (MRR) or minimum power consumption (PC) fulfilling the desired value of surface roughness (SR). The proposed method is found to be easy to implement and very useful for shop floor applications.

KW - EDM

KW - MMC

KW - Optimization

KW - Pareto optimal fronts

KW - Sustainability

UR - http://www.scopus.com/inward/record.url?scp=85046100217&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85046100217&partnerID=8YFLogxK

U2 - 10.1007/s40430-018-1191-8

DO - 10.1007/s40430-018-1191-8

M3 - Article

VL - 40

JO - Journal of the Brazilian Society of Mechanical Sciences and Engineering

JF - Journal of the Brazilian Society of Mechanical Sciences and Engineering

SN - 1678-5878

IS - 5

M1 - 263

ER -