ABSTRACT

Hole making is an important phase in composite machining as structural applications of composites require assemblage. To do so, abrasive waterjet machining (AWM) is recommended by several fabricators and researchers. The quality of the holes produced in composites severely affects the durability of assembled structures. Hence, exploring this aspect is important. In this context, the current study investigates the influence of the AWM variables on GFRP composites. Here, cutting speed (V_c), and abrasive flow rate (Q_ab) are selected as input variables whereas the output attributes are the material removal rate (MRR), surface roughness (R_a), roundness (R_o), and cylindricity (C_y). Initially, mathematical models (objective functions) are derived using statistics of nonlinear regression for correlating the aforementioned variables and output attributes. In the next phase, the study utilizes recently developed Rao algorithms i.e. Rao 1, Rao 2, and Rao 3 to determine the ideal machining condition as V_c = 100 cm/min, and Q_ab = 300 gm/min. The results were also compared with the JAYA and TLBO approaches in order to show the effectiveness of the proposed methodology and it was observed that exploration of Rao 1, Rao 2, and Rao 3 algorithms appears more fruitful in terms of computational time and effort.

摘要

孔加工是复合材料加工中的重要阶段，因为复合材料的结构应用需要组装。为此，一些制造商和研究人员推荐使用研磨水刀加工（AWM）。复合材料中产生的孔的质量严重影响组装结构的耐用性。因此，探索这一方面很重要。在这种情况下，本研究调查了AWM变量对GFRP复合材料的影响。在此，选择切削速度（V _c）和磨料流速（Q _ab）作为输入变量，而输出属性为材料去除率（MRR），表面粗糙度（R _a），圆度（R _o）和圆柱度（C _y）。最初，使用非线性回归的统计数据导出数学模型（目标函数），以将上述变量和输出属性相关联。在下一阶段，该研究利用最近开发的Rao算法，即Rao 1，Rao 2和Rao 3来确定理想的加工条件，即V _c  = 100 cm / min，Q _ab  = 300 gm / min。还将结果与JAYA和TLBO方法进行了比较，以显示所提出方法的有效性，并且观察到，就计算时间和精力而言，对Rao 1，Rao 2和Rao 3算法的探索似乎更加富有成果。