A study on machinability evaluation of Al-Gr-B4C MMC using response surface methodology-based desirability analysis and artificial neural network technique
A study on machinability evaluation of Al-Gr-B4C MMC using response surface methodology-based desirability analysis and artificial neural network technique
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基于响应面法的合意性分析和人工神经网络技术评估Al-Gr-B4C MMC的切削加工性研究
DOI：
10.1504/ijrapidm.2019.10017666
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
N. Senthilkumar
中科院分区：
文献类型：
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作者：
S. Ponnuvel;N. Senthilkumar
研究了铝-石墨-碳化硼复合材料在电火花线切割加工过程中的加工性能。试验设计采用响应面法（RSM）的中心组合-面中心设计，并应用期望函数多个质量特性，即，同时优化了切口宽度、表面粗糙度和材料去除率（MRR）。考虑了输入参数间隙电压、脉冲接通时间、脉冲关断时间和碳化硼颗粒在铝基体中的增强%。得到的最佳加工条件为：间隙电压150 V，脉冲ON时间124.56 ms，脉冲OFF时间48.03 ms和2.5%的碳化硼增强。从实验值，可以观察到，更好的输出响应实现与较低的增强的碳化硼。二阶回归模型分别开发的输出响应。建立了一个人工神经网络模型，对输出响应进行了预测，结果表明，人工智能技术可以实现更好的预测。
In this work, machinability behaviour of aluminium-graphite-boron carbide metal matrix composite is performed during wire-cut electrical discharge machining (WEDM) process. Experiments were designed using central composite-face centred design of response surface methodology (RSM) and with the application of desirability function multiple quality characteristics viz., kerf width, surface roughness and material removal rate (MRR) were optimised simultaneously. Input parameters gap voltage, pulse ON-time, pulse OFF-time and % reinforcement of boron carbide particles in the aluminium matrix are considered. The optimised machining condition obtained is a gap voltage of 150 V, pulse ON-time of 124.56 ms, pulse OFF-time of 48.03 ms and 2.5% reinforcement of boron carbide. From the experimental values, it is observed that better output responses are achieved with lower reinforcement of boron carbide. Second order regression models are developed individually for the output responses. An artificial neural network model is developed to predict the output responses, results obtained show that a better prediction can be achieved through artificial intelligent technique.