基于混合神经网络-遗传算法的EDDG过程建模与多目标优化

Q3 Engineering
P. Shrivastava, A. K. Dubey
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引用次数: 8

摘要

在电火花金刚石磨削(EDDG)过程中,为了提高材料去除率,砂轮磨损率(WWR)和表面光洁度会受到不利影响。因此,总是需要同时优化以上三个响应。本文采用基于人工智能的混合ANN-GA方法对EDDG进行建模和多目标优化。同时还考虑了砂轮粒度与峰值电流、脉冲接通时间和脉冲关闭时间等电气参数的影响。找到了不同反应的显著控制参数,并讨论了其变化的影响。对不同的响应所建立的人工神经网络模型已被证明是可靠的,预测误差可以忽略不计。优化结果表明,MRR显著提高97%,而WWR和表面粗糙度略有增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling and multi-objective optimisation of EDDG process using hybrid ANN-GA approach
It has been found that wheel wear rate (WWR) and surface finish is adversely affected in order to improve the material removal rate (MRR) in electrical discharge diamond grinding (EDDG) process. Therefore, simultaneous optimisation of above three responses is always desired. This research paper presents the modelling and multi-objective optimisation of EDDG using AI-based hybrid ANN-GA approach. The effect of wheel grit size has also been considered along with electrical parameters such as peak current, pulse-on time and pulse-off time. The significant control parameters for different responses have been found and effect of their variation has been discussed. The developed ANN models for different responses have been found reliable with negligible prediction errors. The optimisation results show considerable improvement of 97% in MRR with marginal increase in WWR and surface roughness.
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来源期刊
International Journal of Abrasive Technology
International Journal of Abrasive Technology Engineering-Industrial and Manufacturing Engineering
CiteScore
0.90
自引率
0.00%
发文量
13
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