Optimization of Machining Parameters for Minimizing Surface Roughness in Turning GFRP Composite Using ANN and PSO Methodology

Md. Shafiul Alam, Ahmed Yusuf, Abir Rahman, Inzamam-ul-haq, Nr Dhar
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Abstract

The influence of surface roughness in determining the quality of finished products in any industrial application has an enormous impact on gaining competitive edge and establishing superiority. Thus, recognizing and understanding the factors influencing the resulted surface roughness are the crucial issues helping to achieve the desired goal in any competitive industrial environment. Fact is that machining process parameters are major factors affecting the outcome. This research is focused on determining the optimum machining parameters (cutting speed, feed rate, depth of cut) which result in minimizing the surface roughness in turning glass fiber reinforced polymer (GFRP) matrix composite using coated carbide insert. To understand the effects of machining parameters on surface roughness and to determine relationship between them; Particle Swarm Optimization (PSO) has been employed. A multiple regression equation is used as objective function to determine the optimum values of inputs (cutting speed, feed, and depth of cut) using PSO formula and it yields an optimum value of surface roughness of 0.6252 µm. Artificial Neural Network (ANN) has also been implemented to predict various level of surface roughness for different machining parameters. To predict the surface roughness (Ra), standard multilayer feed-forward back-propagation hierarchical neural network has been applied and the findings provide an overall value of coefficient of determination of 0.88881. These investigations of turning operation provide optimal process parameters for any desired value of surface roughness which result in gaining a competitive edge over others in any industrial application.
基于神经网络和粒子群算法的玻璃钢复合材料车削加工参数优化
在任何工业应用中,表面粗糙度在决定成品质量方面的影响对获得竞争优势和建立优势有着巨大的影响。因此,认识和理解影响结果表面粗糙度的因素是在任何竞争激烈的工业环境中帮助实现预期目标的关键问题。事实上,加工工艺参数是影响加工结果的主要因素。本研究的重点是确定最佳加工参数(切削速度、进给速度、切削深度),从而使使用涂层硬质合金刀片车削玻璃纤维增强聚合物(GFRP)基复合材料的表面粗糙度最小化。了解加工参数对表面粗糙度的影响,并确定它们之间的关系;采用了粒子群算法(PSO)。以多元回归方程作为目标函数,利用PSO公式确定切削速度、进给量和切削深度等输入参数的最优值,得到表面粗糙度的最优值为0.6252µm。人工神经网络(ANN)也被用于预测不同加工参数下不同水平的表面粗糙度。为了预测表面粗糙度(Ra),采用标准的多层前馈反向传播层次神经网络,结果表明,总体决定系数为0.88881。这些对车削操作的研究为任何期望的表面粗糙度值提供了最佳的工艺参数,从而在任何工业应用中获得竞争优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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