Wire Electrical Discharge Machining of AISI304 and AISI316 Alloys: A Comparative Assessment of Machining Responses, Empirical Modeling and Multi-Objective Optimization

IF 3.3 Q2 ENGINEERING, MANUFACTURING Journal of Manufacturing and Materials Processing Pub Date : 2023-11-03 DOI:10.3390/jmmp7060194
Mona A. Aboueleaz, Noha Naeim, Islam H. Abdelgaliel, Mohamed F. Aly, Ahmed Elkaseer
{"title":"Wire Electrical Discharge Machining of AISI304 and AISI316 Alloys: A Comparative Assessment of Machining Responses, Empirical Modeling and Multi-Objective Optimization","authors":"Mona A. Aboueleaz, Noha Naeim, Islam H. Abdelgaliel, Mohamed F. Aly, Ahmed Elkaseer","doi":"10.3390/jmmp7060194","DOIUrl":null,"url":null,"abstract":"This research investigates the multi-response of both material removal rate (MRR) and surface roughness (Ra) for the wire electrical discharge machining (WEDM) of two stainless steel alloys: AISI 304 and AISI 316. Experimental results are utilized to compare the machining responses obtained for AISI 316 with those obtained for AISI 304, as previously reported in the literature. The experimental work is conducted through a full factorial experimental design of five running parameters with different levels: applied voltage, transverse feed, pulse-on/pulse-off times and current intensity. The machined workpieces are analyzed using an image processing technique in order to evaluate the size of cut slots to allow the calculation of the MRR. Followed by the characterization of the surface roughness along the side walls of the slots. Different mathematical regression techniques were developed to represent the multi-response of both materials using the MATLAB regression toolbox. It was found that WEDM process parameters have a fuzzy influence on the responses of both material models. This allowed for multi-objective optimization of the regression models using four different techniques: multi-objective genetic algorithm (MOGA), multi-objective pareto search algorithm (MOPSA), weighted value grey wolf optimizer (WVGWO) and osprey optimization algorithm (OOA). The optimization results reveal that the optimal WEDM parameters of each response are inconsistent to the others. Hence, the optimal results are considered a compromise between the best results of different responses. Noteworthily, the multi-objective pareto search algorithm outperformed the other candidates. Eventually, the optimal results of both materials share the high voltage, high transverse feed rate and low pulse-off time parameters; however, AISI 304 requires low pulse-on time and current intensity levels while AISI 316 optimal results entail higher pulse-on time and current levels.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":"16 9","pages":"0"},"PeriodicalIF":3.3000,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing and Materials Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/jmmp7060194","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0

Abstract

This research investigates the multi-response of both material removal rate (MRR) and surface roughness (Ra) for the wire electrical discharge machining (WEDM) of two stainless steel alloys: AISI 304 and AISI 316. Experimental results are utilized to compare the machining responses obtained for AISI 316 with those obtained for AISI 304, as previously reported in the literature. The experimental work is conducted through a full factorial experimental design of five running parameters with different levels: applied voltage, transverse feed, pulse-on/pulse-off times and current intensity. The machined workpieces are analyzed using an image processing technique in order to evaluate the size of cut slots to allow the calculation of the MRR. Followed by the characterization of the surface roughness along the side walls of the slots. Different mathematical regression techniques were developed to represent the multi-response of both materials using the MATLAB regression toolbox. It was found that WEDM process parameters have a fuzzy influence on the responses of both material models. This allowed for multi-objective optimization of the regression models using four different techniques: multi-objective genetic algorithm (MOGA), multi-objective pareto search algorithm (MOPSA), weighted value grey wolf optimizer (WVGWO) and osprey optimization algorithm (OOA). The optimization results reveal that the optimal WEDM parameters of each response are inconsistent to the others. Hence, the optimal results are considered a compromise between the best results of different responses. Noteworthily, the multi-objective pareto search algorithm outperformed the other candidates. Eventually, the optimal results of both materials share the high voltage, high transverse feed rate and low pulse-off time parameters; however, AISI 304 requires low pulse-on time and current intensity levels while AISI 316 optimal results entail higher pulse-on time and current levels.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
AISI304和AISI316合金的线切割加工:加工响应的比较评估、经验建模和多目标优化
研究了AISI 304和AISI 316两种不锈钢合金电火花线切割加工(WEDM)时材料去除率(MRR)和表面粗糙度(Ra)的多重响应。实验结果用于比较AISI 316和AISI 304的加工响应,如先前文献报道的那样。实验工作通过施加电压、横向进给、脉冲开/关时间和电流强度等5个不同水平的运行参数进行全析因实验设计。利用图像处理技术对加工后的工件进行分析,以评估切割槽的尺寸,从而计算MRR。其次是沿槽侧壁的表面粗糙度表征。利用MATLAB回归工具箱开发了不同的数学回归技术来表示两种材料的多响应。结果表明,线切割工艺参数对两种材料模型的响应均有模糊影响。这允许使用四种不同的技术对回归模型进行多目标优化:多目标遗传算法(MOGA)、多目标pareto搜索算法(MOPSA)、加权值灰狼优化算法(WVGWO)和鱼鹰优化算法(OOA)。优化结果表明,每种响应的最优线切割参数不一致。因此,最优结果被认为是不同响应的最佳结果之间的折衷。值得注意的是,多目标pareto搜索算法优于其他候选算法。最终,两种材料的最佳结果都具有高电压、高横向进给速率和低脉冲时间参数;然而,AISI 304要求较低的脉冲接通时间和电流强度水平,而AISI 316的最佳结果需要较高的脉冲接通时间和电流水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Manufacturing and Materials Processing
Journal of Manufacturing and Materials Processing Engineering-Industrial and Manufacturing Engineering
CiteScore
5.10
自引率
6.20%
发文量
129
审稿时长
11 weeks
期刊最新文献
Assessing the Feasibility of Fabricating Thermoplastic Laminates from Unidirectional Tapes in Open Mold Environments Vickers Hardness Mechanical Models and Thermoplastic Polymer Injection-Molded Products’ Static Friction Coefficients Phase Composition, Microstructure and Mechanical Properties of Zr57Cu15Ni10Nb5 Alloy Obtained by Selective Laser Melting In-Process Machining Distortion Prediction Method Based on Bulk Residual Stresses Estimation from Reduced Layer Removal A Combined Microscopy Study of the Microstructural Evolution of Ferritic Stainless Steel upon Deep Drawing: The Role of Alloy Composition
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1